Abstract:
Disclosed is a computer system provided with a plurality of sensors provided in a plurality of devices to observe a predetermined amount, and a server for examining the physical amount transmitted from the sensors, wherein the plurality of devices are classified into a first device group and a plurality of second device groups, a plurality of second examination rules indicating the examination methods of the physical amount are set in the plurality of second device groups, the server calculates the similarity between the first device group and each of the second device groups, and, on the basis of the calculated similarity, a first examination rule to be set in the first device group is extracted from the plurality of second examination rules set in the plurality of second device groups.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority from Japanese patent application JP 2010-113175 filed on May 17, 2010, the content of which is hereby incorporated by reference into this application. 
     TECHNICAL FIELD 
     The present invention relates to a computer system, and relates in particular to a computer system for examining a physical amount measured in a device, etc. 
     BACKGROUND ART 
     Plants such as thermal power plants and nuclear power plants utilize systems for swiftly detecting device abnormalities and maintaining device to ensure the safe operation of device within the plant. In these types of systems, sensors are installed in each device in order to detect indications of abnormal device or indications that might lead to abnormal device, and the systems collect physical amounts (hereafter, “quantities”) measured by the sensors and by diagnose these accumulated physical quantities to diagnose abnormalities in each piece of device. 
     These device abnormalities can be diagnosed by establishing rules in advance that show what calculation formula to utilize for the measured physical quantity. The rules established for this type of diagnosis are sometimes generated by applying calculation formulas utilized in similar device structures in similar systems. 
     Japanese Unexamined Patent Application Publication No. 2007-094538 discloses for instance, an airport light maintenance system that extracts failure histories containing similar peripheral information when a failure has for example been found in the lights within an airport facility; and estimates the cause of the failure by comparing past failure histories with peripheral information on the environment (temperature, rainfall amount) where the failure occurred, the number of replacements of failed lights, the number of sub-stations, power cable total extension lengths between main station and sub-stations, and identical airports and identical type circuits. 
     The International Patent Publication No. WO03/055145 pamphlet discloses technology for estimating the cause of failure when a failure has been found in a communication path where a plurality of relay devices are coupled in numerous stages, by searching the failure histories of communication paths having a connecting structure similar to that (problem) communication path. 
     SUMMARY OF INVENTION 
     Technical Problems 
     Abnormality indicator diagnostic services for diagnosing a device abnormality or an indication leading to a device abnormality require that new rules be written for diagnosing abnormalities when providing diagnostic services to new customers or when the customer&#39;s device has been modified or additions to the device have been made. 
     Abnormality indicator diagnosis of the related art utilizing vector quantize clustering is capable of making a diagnosis by using statistical techniques to analyze plural measured physical quantities, in other words, observation values, and so can diagnose an abnormality without analyzing the cause of the failure as performed in the previously described technology of the related art. 
     However, when making an abnormality indicator diagnosis using VQC to diagnose a large number of unrelated observation values, the problem of so-called, “curse of dimensionality” occurs and the abnormality detection accuracy worsens. In other words, increasing the observation values increases the number of quantitative parameters for indicating the abnormal phenomenon. Increasing the number of parameters serves to exponentially increase the number of abnormal phenomenon patterns so that pinpointing the abnormality becomes impossible. 
     Abnormality indicator diagnosis using VQC therefore required selecting the observation values needed in the diagnosis, but in the related art the administrator or person in charge selected these observation values through trial-and-error or through past experience. Selection of these observation values also requires many man-hours. Rules must be drawn up for example for 20,000 sensors at a thermal power plant and a large number of man-hours are required for setting which observation values to measure. 
     The VQC moreover requires learning data (codebook) for making the diagnosis. In the related art however, the codebook must be newly made for relearning when the device structure is different, causing the problem that utilization is impossible until all the abnormal phenomenon have been accumulated as information. 
     The present invention has the object of providing rules for making a diagnosis in the maintenance system by way of VQC. 
     Solution to Problem 
     A typical aspect of the present invention disclosed in the present application is given as follows. Namely, a computer system including a plurality of sensors installed in plural devices to measure specified physical quantities and a server to diagnose the physical quantities sent from the sensor; and in which, the plural devices are classified into a first device groups and a plurality of second device groups; and the plural second device groups contain a plurality of second examination rules showing diagnosis methods for the physical quantities; and the server calculates the similarity between the first device group and each of the second device groups; and extracts a first examination rule set in the first device group from the plural second examination rules set in the plural second device groups based on the calculated similarity. 
     Advantageous Effects of Invention 
     The typical embodiment of the present invention renders the effect of reducing the man-hours for setting the examination rules in a new device. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram showing the system of the first embodiment of the present invention; 
         FIG. 2  is a block diagram showing the hardware structure of the examination server of the first embodiment of the present invention; 
         FIG. 3  is a block diagram showing the examination server function in the first embodiment of the present invention; 
         FIG. 4  is a block diagram showing the processing by the examination server in the first embodiment of the present invention; 
         FIG. 5  is a flow chart showing the diagnostic processing by the examination executer unit of the first embodiment of the present invention; 
         FIG. 6  is a descriptive drawing showing an example applying a device cluster to new devices in the first embodiment of the present invention; 
         FIG. 7  is a descriptive drawing showing the structure of the device graph in the first embodiment of the present invention; 
         FIG. 8  is a descriptive drawing showing the data structure of device information and sensor information in the first embodiment of the present invention; 
         FIG. 9  is a descriptive drawing showing a specific example of the device information and sensor information of the first embodiment of the present invention; 
         FIG. 10  is a descriptive drawing showing the structure of the sensor selector unit and sensor selection rule in the first embodiment of the present invention; 
         FIG. 11A  is a descriptive drawing showing an observation value processing rule of the first embodiment of the present invention; 
         FIG. 11B  is a descriptive drawing showing an example of processing by the observation value processor unit in the first embodiment of the present invention; 
         FIG. 12  is a flow chart showing frequency analysis by the observation value processor unit in the first embodiment of the present invention; 
         FIG. 13A  is a descriptive drawing showing an example of the event distribution in the first embodiment of the present invention; 
         FIG. 13B  is a descriptive drawing showing the relation between the event cluster and the feature vector in the first embodiment of the present invention; 
         FIG. 13C  is a descriptive drawing showing the (math) function for calculating the reliability in the first embodiment of the present invention; 
         FIG. 14  is descriptive drawing showing the input/output data for VQC by the examination unit of the first embodiment of the present invention; 
         FIG. 15  is a flowchart showing the VQC by the examination unit of the first embodiment of the present invention; 
         FIG. 16  is a flow chart showing an overview of the processing for generating a device cluster in the first embodiment of the present invention; 
         FIG. 17  is a flow chart showing in detail the processing for generating a device cluster in the first embodiment of the present invention; 
         FIG. 18A  is a descriptive drawing showing a specific example for acquiring similar device graphs in the first embodiment of the present invention; 
         FIG. 18B  is a descriptive drawing showing the generation of a sensor selection rule in the first embodiment of the present invention; 
         FIG. 19  is a flow chart showing the procedure for acquiring similar device graphs in the first embodiment of the present invention; 
         FIG. 20  is a flow chart showing the method for calculating the similarity of the device graphs in the first embodiment of the present invention; 
         FIG. 21  is a flow chart showing the procedure for calculating the similarity of the nodes in the first embodiment of the present invention; 
         FIG. 22  is a descriptive drawing showing a specific example of calculating the attribute distance in the first embodiment of the present invention; 
         FIG. 23  is a flow chart showing the procedure for calculating the similarity coefficient in the first embodiment of the present invention; 
         FIG. 24A  is a descriptive drawing showing an example of the device cluster distance in the first embodiment of the present invention; 
         FIG. 24B  is a descriptive drawing showing an example of the event cluster distance in the first embodiment of the present invention; 
         FIG. 25  is a descriptive drawing showing an example of applying a device cluster to a new plant in the second embodiment of the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     First Embodiment 
       FIG. 1  is a block diagram showing the system of the first embodiment of the present invention. 
     The system of the embodiment of the present invention includes the sensors  104 , a collection server  105 , a network  106 , and an examination server  107 . 
     The sensors  104  are respectively mounted in the devices  102  and pipes  103  installed within the plant  101  and measure the observation values such as vibration, heat, or rotation speed generated in the devices  102  and pipes  103 . A plant  101  is an electrical generating plant or factory, etc. The plant  101  contains the devices  102  and the pipes  103 . The devices  102  are a motor or a pump, etc., and the pipes  103  are pipes or cables that connect the devices  102  to each other. The collection server  105  collects observation values measured by the sensors. The collection server  105  sends the measured observation values to the examination server  107  byway of the network  106 . 
     In the present embodiment the plant  101  is an electrical power plant and so on but if a system for measuring the observation values by way of the sensor  104 , and collecting measured observation values, then the system can apply to any plant. 
     The network  106  is a LAN (Local Area Network), WAN (Wide Area Network) or may be any network of the Internet. The examination server  107  analyzes the observation values sent from the collection server  105  and judges whether or not a phenomenon indicating an abnormality has occurred. The examination server  107  sends the examination results to the user terminal  108  by way of the network  106 . The user terminal  108  displays the examination results so that the user can view the examination results. 
       FIG. 2  is a block diagram showing the hardware structure of the examination server  107  of the first embodiment of the present invention. 
     The examination server  107  includes an auxiliary storage device  202 , a CPU 203 , a network adapter  204 , an AC adapter  205 , a display  206 , and a keyboard  207 . When executing the program, the CPU 203  runs the program in a memory  201 , and utilizes the memory  201  as a temporary storage (buffer) area. The auxiliary storage device  202  is a non-volatile storage device (for example a magnetic disk drive) where a program executed by the CPU 203  and data, etc. are stored. The CPU 203  executes the program in order to analyze the observation values measured by the sensors  104  and judges whether or not a phenomenon indicating an abnormality has occurred. 
     The network adapter  204  receives the observation value sent from the collection server  105  byway of the network  106 , and is also a network interface for sending results diagnosed in the CPU 203  to the user terminal  108  by way of the network  106 . The AC adapter  205  is a power supply device for supplying electrical power to the examination server  107 . 
     The display  206  is an output device for displaying the examination results from the examination server  107  to allow viewing by the administrator. The keyboard  207  is an input device for allowing the administrator to input parameters for making a diagnosis. 
       FIG. 3  is a block diagram showing the function of the examination server  107  in the first embodiment of the present invention. 
     The examination server  107  includes a sensor input unit  401 , a result output unit  402 , an examination rule generator unit  411 , and an RDB 306 . 
     The sensor input unit  401  receives the observation values sent from the collection server  105 , and send the received observation values to the examination executer unit  403 . The result output unit  402  sends the results diagnosed (examined) by the examination executer unit  403  to the user terminal  108 . 
     The examination executer unit  403  includes a sensor selector unit  404 , an observation value processor unit  405 , an examination unit  406 , and a processor function  301 . The examination executer unit  403  judges whether or not a phenomenon indicating an abnormality has occurred based on the observation values that were sent. 
     The sensor selector unit  404  is a program for analyzing the observation values that were sent, and selecting which sensor observation values to utilize for the diagnosis. The observation value processor unit  405  is a program for processing the observation value utilized in functions such as Fourier transforms, and selecting which observation values among the processed observation values to utilize for the diagnosis. The examination unit  406  is a program for diagnosing the observation values by methods such as VQC described later on. The processor function  301  is a function utilized in the observation value processor unit  405 , and is a sub-routine of the observation value processor unit  405 . 
     The examination rule generator unit  411  includes a selection rule maker unit  412 , a processing rule maker unit  413 , an examination rule maker unit  414 , a similar graph search unit  302 , a similar node search unit  303 , a learning unit  304 , and a relearning unit  305 . Each program in the examination executer unit  403  diagnoses (or examines) the observation values by way of the rules made or rewritten by the examination rule generator unit  411 . 
     The selection rule maker unit  412  is a program for making selection rules utilized in the sensor selector unit  404 . The similar graph search unit  302  and the similar node search unit  303  are sub-routines of the selection rule maker unit  412 . The processing rule maker unit  413  is a program for making rules for processing observation values utilized in the observation value processor unit  405 . The examination rule maker unit  414  is a program for making examination rules utilized in the examination unit  406 . The learning unit  304  and the relearning unit  305  are sub-routines of the examination rule maker unit  414 . 
     The RDB 306  stores the device information  702 , the sensor information  703 , the schema information  810 , the time-series data  704 , and the device cluster  407 . The RDB 306  can be a file system or a relational DB (Database) mounted in the memory  201 . 
     The device information  704  is information showing what type of device  102  and pipe  103  are present within the system, and how the device  102  and pipe  103  are connected. The device information  702  is stored beforehand by the administrator and so on in the RDB 306 . 
     More specifically, the device information  702  contains an identifier showing the unique device  102  and pipe  103 , names of the device  102  or pipe  103  (e.g. pump), and the type of operation of the device  102  or pipe  103  (e.g. rotation), the installation date of the device  102  or pipe  103 , and an identifier showing the connected device  102  or pipe  103 , etc. The device information  702  is shown by numbers or character strings. 
     The sensor information  703  is information showing what the specifications are for each sensor inside the system. The administrator or other party stored the sensor information  703  beforehand in the RDB 306 . 
     More specifically, the sensor information  703  stores attribute information showing the identifiers or names of the connected device  102  or pipe  103 , the object for measurement (e.g. rotation speed, temperature, etc.) observation value units (e.g. rpm, ° C., and so on), and measurement period (e.g. minutes and so on). The sensor information  703  is shown by way of numbers or character strings. 
     The schema information  810  is information showing what type of information is stored in the device information  702  and the sensor information  703 . More specifically, the schema information  810  showing the device information  702 , includes an identifier showing the unique device  102  or pipe  103 , a name of the device  102  or pipe  103 , the type of operation of the device  102  or pipe  103 , the installation date of the device  102  or pipe  103 , and an identifier showing the connected device  102  or pipe  103 . 
     The schema information  810  showing the sensor information  703  further contains, identifiers showing the sensor information  703 , identifiers or names of the connected devices  102  or the pipes  103 , object for measurement (e.g. rotation speed, temperature, etc.), observation value units (e.g. rpm, ° C., and so on) and measurement period (e.g. 5 minutes and so on). 
     The time-series data  704  stores the observation values measured by the sensor  104  in their time-series order. More specifically, the measurement time, identifier showing the sensor  104  that made the measurement, and the observation value are stored in the time-series data  704 . 
     The device cluster  407  contains a device graph  502 , a sensor selection rule  408 , an observation value processing rule  409 , a processing parameter  1103 , and a code book  410 . Among the devices  102  and pipes  103 , the device cluster  407  is information on the device  102  and pipe  103  combinations that require diagnosis together. One device cluster may contain at least one device  102  or pipe  103 . 
     The device graph  502  contains identifiers showing each unique device  102  or pipe  103 , and information such as how the device  102  or pipe  103  is installed and at what distances. The device graph  502  for example contains information such as that a pump and pipe are connected, or information that a turbine and pipe are installed 30 centimeters apart. 
     Among the sensors  104  connected to the devices  102  or the pipe  103  shown in the device graph  502 ; the sensor selection rule  408  contains information showing what observation values are utilized by which sensor  104 . 
     The observation value processing rule  409  contains information showing how to process observation values that were measured on the device  102  or the pipe  103  shown in the device graph  502 . The processing parameter  1103  is a parameter utilized in functions in cases where the observation value processing rule  409  is the function. 
     The codebook  410  stores parameters for making a diagnosis. Namely, the codebook  410  stores examination rules for diagnosing, what type of observation value shows what type of event at what numerical value. 
       FIG. 4  is a block diagram showing the processing by the examination server  107  in the first embodiment of the present invention. 
     The sensor selector unit  404  selects which sensor observation value to utilize for the diagnosis when an observation value is received from the collection server  105  by way of the sensor input unit  401 . Moreover in order to select the sensor, the sensor selector unit  404  refers to the sensor selection rule  408  made by the selection rule maker unit  412 . 
     When an observation value for a sensor selected by the sensor selector unit  404  is received, the observation value processor unit  405  processes the observation value that was received, and selects which observation value to utilize for the diagnosis from among the processed observation values. In order to process the observation value, the observation value processor unit  405  also refers to the observation value processing rule  409  made by the processing rule maker unit  413 . 
     When an observation value processed by the observation value processor unit  405  is received, the examination unit  406  proceeds to diagnose that received observation value. Moreover, in order to diagnose the observation value, the examination unit  406  searches the code book  410  made by the examination rule maker unit  414 . 
       FIG. 5  is a flow chart showing the diagnostic processing by the examination executer unit  403  of the first embodiment of the present invention. 
     The device cluster  407  shown in  FIG. 5  includes a device cluster ID 501 , a device graph  502 , a sensor selection rule  408 , an observation value processing rule  409 , and a code book  410 . The device cluster ID 501  is an identifier showing a unique device cluster  407 . 
     The examination executer unit  403  implements the flow chart (processing) shown in  FIG. 5  when the observation value sent from the collection server  105  is a fixed cumulative quantity for each device cluster  407 . In other words, when the observation value sent from the collection server  105  is a fixed cumulative quantity for each device cluster  407 , the examination executer unit  403  selects the device cluster  407  where the observation value has been accumulated (Step  511 ). The examination executer unit  403  then sends the selected observation value for the device cluster  407  to the sensor selector unit  404 . 
     The sensor selector unit  404  selects which sensor observation value to utilize by searching the sensor selection rule  408  contained in the device cluster  407  selected in step  511  (step  512 ). The sensor selector unit  404  then sends the selected sensor observation value to the observation value processor unit  405 . 
     The observation value processor unit  405  processes (step  513 ) the observation value selected in step  512  by searching the observation value processing rule  409  contained in the device cluster  407  selected in step  511  (step  513 ). The observation value processor unit  405  then sends the processed observation value to the examination unit  406 . 
     The examination unit  406  diagnoses the observation values processed in step  513  by searching the code book  410  contained in the device cluster  407  selected in step  511  (step  514 ). There are plural observation values processed by the observation value processor unit  405  and each show a different phenomenon. These plural observation values are therefore shown by a feature vector. The examination unit  406  converts the observation values shown by the feature vector into scalar values in this step  514 . 
     The examination unit  406  compares the observation values converted into scalar values in step  514 , with pre-established threshold values (step  515 ) and if those results are values showing the observation values are abnormal or namely if indications of an abnormality are detected, issues a warning (step  516 ). The examination unit  406  may issue a warning by displaying a warning on a display  206  or may issue a warning by sending a message signifying a warning to the user terminal  108 . Information showing abnormality indications even other than a warning may be retained in the RDB 306 , etc. 
     In step  515 , when there is no observation value indicating an abnormality in step  515 , or after the warning was issued in step  516 , the examination executer unit  403  decides whether or not the diagnosis for all device clusters  407  has ended or not by judging whether the observation values sent from the collection server  105  have accumulated to a specified quantity or not (step  517 ). If the diagnosis has not ended then the examination executer unit  403  returns to step  511 . If the diagnosis has ended then the examination executer unit  403  terminates the processing and waits for observation values to accumulate. 
       FIG. 6  is a descriptive drawing showing an example applying a device cluster  407  to a new device  102  in the first embodiment of the present invention. 
       FIG. 6  shows the connective relation of the device  102  or pipe  103  contained within the one plant  101 . 
     The device  102  and the pipe  103  are shown (m1 through m12) in  FIG. 6 . Each of the devices  102  and pipes  103  are connected by way of a tree structure. The example shown in  FIG. 6 , describes only the device  102  but the connectional relation may also include the pipe  103 . The connectional relation for the device  102  shown in  FIG. 6  is a connectional relation for the device  102  contained the plant  101 , however a connectional relation within a specified department within the plant  101  is also allowed. 
     As already described, the connectional relation of the devices  102  requiring collective diagnosis of the observed observation values is shown by the device graph  502 , and the device clusters  407  are assigned to the device graph  502 . The device cluster  407  shown in  FIG. 6  includes a device cluster  407 A assigned to the devices and pipes m1-m3, the device cluster  407 B assigned to the devices and pipes m4-m6, and the device cluster  407 C assigned to the devices and pipes m7-m9. 
     This drawing shows the case where a facility containing a new device  102  has been newly added to the plant  101 . Among the newly added plural devices  102 , the examination rule generator unit  411  of the present embodiment searches for device graphs  502  resembling the already existing device graph  502 , and generates a device cluster  407  for the newly added facility by applying a new device  102  to the device cluster  407  stored in the device graph  502  that was searched. 
     In the example shown in  FIG. 6 , when the device graph  502  including the devices and pipes m4-m6, is similar to the device graph  502  including the devices and pipes m10-m12, the device cluster  407 D is generated for devices and pipes m10-m12 by applying the device cluster  407 B including the devices and pipes m4-m6 to the devices and pipes m10-m12. 
       FIG. 7  is a descriptive drawing showing the structure of the device graph  502  in the first embodiment of the present invention. 
     The device graph  502  includes 0 or one or more device information  702 , and these device information  702  are concentrated in the device graph  502 . In some cases the device information  702 , 0 or one or more device information  702  are contained in another device information  702 . If a pipe (pipe  103 ) is for example connected to a pump (device  102 ), then the device information  702  for the pipe is contained in the device information  702  for the pump. 
     If the number of device information  702  is 0, then there are no devices  102  contained in the device graph  502  so there is also no device cluster  407 . 
     The device information  702  includes one or more sensor information  703 , and these sensor information  703  are concentrated in the device information  702 . The sensor information  703  shows the sensor  104  connected to the device information  702 . The sensor information  703  also includes 0 or one or more time-series data  704 , and this time-series data  704  is concentrated in the sensor information  703 . The time-series data  704  includes observation values that were measured by the sensor  104 . 
       FIG. 8  is a descriptive drawing showing the data structure of the device information  702  and the sensor information  703  in the first embodiment of the present invention. 
     The device information  702  and the sensor information  703  both include data structures shown in the instance information  801 . The instance information  801  contains an ID 802 , a schema ID 803 , an attribute  804 , and a relation  807 . The ID 802  is an identifier showing a unique device  102  or the pipe  103 , or the sensor  104 . In the following description, the ID 802  corresponding to the device  102  or pipe  103  is displayed as device ID 802 , the ID 802  corresponding to the sensor  104  is displayed as the sensor ID 802 . The schema ID 803  is an identifier showing the unique applicable schema information  810 . 
     The attribute  804  includes the attribute name  805  and the attribute value  806 . The attribute name  805  indicates the attribute of the device information  702  or the sensor information  703  and includes for example the name of the device  102  or the pipe  103 , the type of operation, and the installation date, etc. The attribute value  806  shows the corresponding value for the attribute name  805 , and includes for example, pump, rotation, Jan. 1, 2010, etc. 
     The relation  807  includes the relation name  808  and the relation ID 809 . The relation name  808  shows the relation between the device  102  or the pipe  103  or the sensor  104  indicated by the relation ID 809  and the device  102  or the pipe  103  or the sensor  104  shown by the ID 802 ; and for example indicates a “connection” or “inclusion” etc. The relation ID indicates an identifier for another related device  102  or pipe  103 , or indicates an identifier for a connected sensor  104 . 
     The instance information  801  contains 0 or one or more attributes  804 . The instance information  801  also contains 0 or one or more relations  807 . The relation  807  further contains 0 or one or more relations ID 809 . 
     The time-series data  704  includes the ID 802 , the time  819 , and the observation value  820 . There are 0 or one or more time-series data  704  present. The ID 802  are concentrated in the ID 802  included in the instance information  801 . The time  819  shows the time that the observation value  820  was measured. The observation value  820  shows the observation value that was measured in the device  102  or the pipe  103 . The time-series data  704  is related to the sensor information  703 . 
     The schema information  810  includes a schema ID 803 , 0 or one or more attribute schema  812 , and 0 or one or more relation schema  816 . The schema ID 803  is concentrated in the ID 802  contained in the instance information  801 . 
     The attribute schema  812  includes the attribute name  813 , the data type  814 , and the similarity coefficient  815 . The attribute name  813  is the same as the attribute name  805 , and includes the name of the device  102  or the pipe  103 , the type of operation, and the installation date, etc. The data type  814  shows the data type of the attribute shown in the attribute name  813 . If the attribute name  813  for example is a name of a device  102  or pipe  103 , then the data type  814  shows a character string, and if the attribute name  813  is an installation date then the data type  814  shows a date type. 
     Beside the above described examples, the attribute name  813  may also include attributes such as the installation position, the production manufacturer&#39;s name, the average performance, the need (or not) for calibration (namely corrections), or the calibration period in the case that calibration is required. 
     The similarity coefficient  815  is a coefficient for evaluating the similarity of each device  102 , each pipe  103 , or each sensor  104 . The similarity coefficient  815  is described in detail later on. 
     The relation schema  816  contains a relation name  817  and 0 or one or more schema ID 818 . The relation name  817  is the same as the relation name  808  and indicates the type of relation with other devices  102 , pipe  103  or sensors  104 . The schema ID 818  indicates an identifier for the schema information  810  of a device  102 , pipe  103 , or sensor  104  connected to a device  102 , pipe  103 , or sensor  104  corresponding to the schema ID 803 . 
     If the schema ID 803  for example indicates an identifier for the schema information  810  for the motor, then the schema ID 818  shows an identifier for the schema information  810  for the cable, and the attribute name  817  contains a character string for “connection” showing a connected (state). Also if the schema ID 803  shows for example an identifier for the schema information  810  for the turbine, then the schema ID 818  shows an identifier for the schema information  810  for the pipe, and the attribute name  817  contains a character string for “position separated by 10 centimeters” showing positioning separated by a fixed distance. 
     The relation schema  816  contains a schema ID 818  showing another schema information  810 , and in the aforementioned example includes the schema ID 818  for a specified pipe. 
     The device information  702  and the sensor information  703  are stored beforehand by the administrator in an examination server  107 . 
       FIG. 9  is a descriptive drawing showing a specific example of the device information  702  and sensor information  703  of the first embodiment of the present invention. 
     The device  102  shown in  FIG. 9  is a pump #1 ( 102 - 1 ), a motor #2 ( 102 - 2 ), and a pump #4 ( 102 - 3 ). The pipe  103  is a pipe #3 ( 103 - 1 ). The sensor  104  is a sensor #5 ( 104 - 1 ), a sensor #6 ( 104 - 2 ), a sensor #7 ( 104 - 3 ), and a sensor #8 ( 104 - 4 ). 
     The pump #1 ( 102 - 1 ) and the pipe #3 ( 103 - 1 ) are connected, and the pump #1 ( 102 - 1 ) and the motor #2 ( 102 - 2 ) are connected. Also, the pipe #3 ( 103 - 1 ) and the pump #4 ( 102 - 3 ) are connected. 
     The sensor #6 ( 104 - 2 ) is connected to the pump #1 ( 102 - 1 ), and the sensor #5 ( 104 - 1 ) is connected to the motor #2 ( 102 - 2 ). Also, the sensor #7 ( 104 - 3 ) is connected to the pipe #3 ( 103 - 1 ), and the sensor #8 ( 104 - 4 ) is connected to the pump #4 ( 102 - 3 ). 
     The device information  702  corresponding to the pump #1 ( 102 - 1 ) is the device information  702 - 1 . The devices  102  shown in  FIG. 9  correspond to each of the device information  702 . 
     The sensor information  703  corresponding to the sensor #8 ( 104 - 4 ) is the sensor information  703 - 4 . The pipe  103  shown in  FIG. 9  corresponds to each of the sensor information  703 . Moreover, the sensor #8 ( 104 - 4 ) corresponds to the time-series data  704 - 4 . The ID 802  for the time-series data  704 - 4  is the same as the ID 802  shown in the sensor information  703 - 4 . The sensor information  703  and the time-series data  704  for the pipe  103  correspond to the pipe  103  as described above. 
     The relation  807  is not shown for the device information  702 - 1  and the sensor information  703 - 4  shown in  FIG. 9 . 
     The pump #1 ( 102 - 1 ) and pump #4 ( 102 - 3 ) correspond to the schema information  810 - 1  showing the schema information  810  for the same A type pump. In other words, the schema ID 803  for the device information  702 - 1  of pump #1 ( 102 - 1 ), and the schema ID 803  for the device information  702  of pump #4 ( 102 - 3 ) both show the schema information  810 - 1 . 
     The sensor #5 ( 104 - 1 ) and sensor #7 ( 104 - 3 ) are both a sensor  104  for measuring the vibration in the motor and the pipe; and correspond to the schema information  810 - 2  as the schema information  810  for the vibration sensor. Further, the sensor #6 ( 104 - 2 ) and the sensor #8 ( 104 - 4 ) are both a sensor  104  for measuring the pressure in the pump; and correspond to the schema information  810 - 3  as the schema information  810  for the pressure sensor. 
       FIG. 10  is a descriptive drawing showing the structure of the sensor selector unit  404  and the sensor selection rule  408  in the first embodiment of the present invention. The structure shown in  FIG. 10  corresponds to step  511  and step  512  shown in  FIG. 5 . 
     The observation values measured by the plural sensors  104  are sent to the sensor selector unit  404  by way of the sensor input unit  401 . The sensor  104  sends the observation values by sending the observation event  1001  containing the observation values for sending, to the sensor selector unit  404 . The sensor ID 802  for each of the sensors s1-S6 are here assigned in advance to each sensor  104 . 
     The observation event  1001  includes the time  819  when the observation values measured, the sensor ID 802 , and the observation value  820 . The observation event  1001  corresponds to the time-series data  704 . The device cluster  407  includes the sensor selection rule  408  just as already described. The observation event  1001  holds the same data as the time-series data  704 . The examination server  107  stores the observation event  1001  as the time-series data  704  during storing of the observation event  1001  in the RDB 306 . 
     The sensor selector unit  404  retains an assignment map  1005  for assigning the observation events  101  sent from the sensor  104  according to the device cluster  407 . The assignment map  1005  is a map for linking the sensor ID 802  and the device cluster  501 . The assignment map  1005  may for example be a database that holds a key value structure for utilizing the sensor ID 802  as a key for retaining the plural device cluster ID 501  as values, and moreover contains a hash map structure. 
     The sensor selection rule  408  includes the input ID 1006  and sensor ID 802 , and assigns an input ID 1006  to the sensor ID 802 . The input ID 1006  is an identifier assigned to the sensor ID 802  for selecting the observation value  820  to input to the function required for the processing, during the processing of the observation value  820  in the observation value processor unit  405 . The sensor selector unit  404  is capable of selecting which observation value  820  for the sensor  104  to set as which input values for the function. 
     The observation event  1001  sent from the sensor  104  is assigned by way of the sensor selector unit  404  to the device cluster  407  for each sensor ID 802 . The sensor selector unit  404  assigns the input ID 1006  to the observation events  1001  sent by way of the sensor  104  shown by the sensor ID 802  according to the sensor selection rule  408  contained in the device cluster  407 . 
     Here there are cases when one sensor ID 802  is assigned to plural device clusters ID 501 . In the assignment map  1005 , for example, the observation event  1001  whose sensor ID  802  are “s1”, “s2”, and “s3” are assigned to the device cluster  407  whose device cluster ID 501  are “r1”. 
     The observation event  1001  whose sensor ID 802  are “s3”, “s4”, “s5” are assigned to the device cluster  407  whose device cluster ID 501  is “r2”. The observation event  1001  whose sensor ID 802  is s3 are therefore assigned to the device cluster  407  whose device cluster ID 501  are r1 and r2. In this case, the sensor selector unit  404  copies the observation event  1001  into two items, and assigns the input ID 1006  to each of the copied observation events  1001 . 
       FIG. 11A  is a descriptive drawing showing an observation value processing rule  409  of the first embodiment of the present invention. 
     The observation value processing rule  409  includes an element ID 1102 , an input ID 1006 , a processing function  301  and a processing parameter  1103 . The observation value processing rule  409  is present in each device cluster  407  as already described. 
     The element ID 1102  is an identifier for identifying the elements contained in the feature vector  1101 . The input ID 1006  is an identifier assigned by the sensor selector unit  404  to the observation event  1001 . The elements contained in the element ID 1102  are hereafter given as the element v1, element v2, . . . , and the element Vn. 
     The processing function  301  is a function for processing the observation value  820  among the observation events  101 . The processing function  301  shown in  FIG. 11A  is shown by way of a character string but the processing function  301  may include formulas, and also include parameters for the readout by the formula. The processing parameter  1103  is a parameter for inputting the function shown by way of the processing function  301 . 
     Plural inputs ID 1006  are sometimes assigned to the element ID 1102 . This arrangement allows inputting observation values  820  that were measured by plural sensors  104  to the function shown in the processing function  301 . 
       FIG. 11B  is a descriptive drawing showing an example of processing by the observation value processor unit  405  in the first embodiment of the present invention. The processing shown in  FIG. 11B  corresponds to step  513  in  FIG. 5 . 
     In the example shown in  FIG. 11B , the circles along the time axis show the observation events  1001 , and show the observation event  1001  accumulated along the time axis. In the time axis shown in  FIG. 11B , the time is newer towards the right side of the axis. The observation events  1001  accumulated according to the time series are given the collective name of input data  1104 . 
     The element v1 among the feature vectors  1101 , indicates an input ID 1006  that is i1 for the observation value processing rule  409 , and the processing function  301  shows a “none”. The observation value processor unit  405  therefore stores the observation value  820  in the element v1 unchanged, and without processing the observation value  820  in the observation event  1001  where input ID 1006  is i1. 
     The element v2 among the feature vectors  1101 , indicates an input ID 1006  that is i2 for the observation value processing rule  409 , the processing function  301  shows a “movement average”, and the processing parameter  1103  shows “5 seconds”. The observation value processor unit  405  therefore calculates an average of observation values  820  over 5 seconds, or in other words, calculates the movement average based on the observation value  820  measured over a five second period by the sensor  104  corresponding to i2. The observation value processor unit  405  then stores the calculated movement average in the element v2. 
     When the processing parameter  1103  shows “5 seconds”, then the observation value  820  utilized for the calculation may also be an observation value  820  for the observation event  1001  received over a five second period by the observation value processor unit  405 . Therefore, even if the processing parameter  1103  shows “5 seconds” this display does not signify that there are five observation events  1001 . 
     The element v3 among the feature vectors  1101 , indicates an input ID 1006  that is i3 and i4 for the observation value processing rule  409 , and the processing function  301  shows “average”. The observation value processor unit  405  therefore calculates the average value for the observation value  820  measured by the sensor  104  corresponding to i3 and the sensor  104  corresponding to i4; or in other words, calculates an average for the observation value  820  per the sensor  104  at that same time. The observation value processor unit  405  then stores the calculated average in the element v3. 
     The element v4 among the feature vectors  1101 , indicates an input ID 1006  that is i5 for the observation value processing rule  409 , and the processing function  301  shows “frequency analysis”, and the processing parameter  1103  shows “5 second FFT, A point”. 
     The observation value processor unit  405  therefore makes an FFT (Fast Fourier Transform) or in other words performs a frequency analysis based on the observation value  820  measured over a five second period by the sensor  104  corresponding to i5. Moreover, the “A point” indicates an optional frequency, and among the results  1105  obtained by frequency analysis, the amplitude  1106  for the A point is stored in the element v4. 
     The processing function  301  for the element v5 among the feature vector  1101  and element v6 among the feature vector  1101  also indicates “frequency analysis” for the observation value processing rule  409 , and the processing parameter  1103  shows “5 second FFT” the same as the feature vector  1101  showing “v4”. However, a “B point” is also included in the processing parameter  1103  in the element v5 per the observation value processing rule  409 ; and “C point” is included in the processing parameter  1103  in the element v6 per the observation value processing rule  409 . 
     The observation value processor unit  405  therefore stores the amplitude  1107  at the B point in element V5 and stores the amplitude  1108  for the C point in the element v6 from among the frequency analysis results  1105  implemented based on the observation value  820  measured over a five second period by the sensor  104  corresponding to i5, and the input data  1104  for i5. 
       FIG. 12  is a flow chart showing frequency analysis by the observation value processor unit  405  of the first embodiment of the present invention. Detailed information on the frequency analysis in  FIG. 11  is described below. 
     First of all, the observation value processor unit  405  acquires the input data  1104  (step  1211 ). The input data  1104  is a plurality of observation events  1001  (time-series data  704 ) accumulated on a time-series base in the examination server  107 . The observation value processor unit  405  sub-divides the acquired input data  1104  into a frame  1201  at time intervals (5 seconds, in  FIG. 11B ) specified by the processing parameter  1103  (step  1212 ), and converts each frame  1201  into frequency components by FFT and so on (step  1213 ). 
     The observation value processor unit  405  moreover extracts each frequency amplitude specified by the processing parameter  1103  from among the results  1105  obtained by FFT and so on, and outputs the extracted frequency amplitude as the feature vector  1101 . 
     The event  1305  of the present embodiment is described next. 
       FIG. 13A  is a descriptive drawing showing an example of distribution of the event  1305  in the first embodiment of the present invention. 
     The event  1305  here indicates damage likely to occur in the device  102  or the pipe  103 ; or that the device  102  or the pipe  103  is in a normal state. Specifically, the event  1305  shows events such as, “fracture has occurred” or “pressure is rising, creating explosion hazard”, or “safe condition”, etc. 
     The example in  FIG. 13A  shows how the events  1305  are distributed when there are two elements for the feature vector  1101 . The two elements are shown by the element V1 and the element V2. In the drawing shown in  FIG. 13A , the horizontal axis is the value  1501  for element V1 and the vertical axis is the value  1502  for element V2. 
     The horizontal axis and vertical axis shown in  FIG. 13A  are each feature spaces or in other words are dimensions. The feature spaces in the present embodiment are the same in number as the number of feature vector  1101  elements. 
     The event  1305  is shown in  FIG. 13A  by the event A (event  1305 - 1 ), the event B ( 1305 - 2 ) and the event C (event  1305 - 3 ). 
     The event cluster  1308  in  FIG. 13A  is the range of values of the feature vector  1101  that the event  1305  occurred in the past and indicates the range of the occurred element V1 value  1501  and element V2 value  1502  judged as extremely likely that the event  1305  occurs. 
     The event A (event  1305 - 1 ) includes an event cluster  1308  shown by the range c1-c7. The event B (event  1305 - 2 ) includes an event cluster  1308  shown by the range of c8 and c9. The event C (event  1305 - 3 ) includes the event cluster  1308  shown by the range of c10. 
     The feature vector  1101  value is shown by p1-p3 in  FIG. 13A . If the feature vector  1101  is the p1 contained in the event cluster c2, then in  FIG. 13A  there is an extremely high probability that the event A (event  1305 - 1 ) will occur in the device  102  or pipe  103  connected to the sensor  104  corresponding to the feature vector  1101 . If the feature vector  1101  is p2, then in  FIG. 13A  there is an extremely high probability that the event B (event  1305 - 2 ) will occur in the device  102  or pipe  103  connected to the sensor  104  corresponding to the feature vector  1101 . 
     If the feature vector  1101  is p3, then in  FIG. 13A  there is a high probability that no event  1305  has occurred in the device  102  or pipe  103  connected to the sensor  104  corresponding to the feature vector  1101 . 
     The event distance  1503  is the distance between the feature vector  1101  and the center-of-gravity of each event cluster  1308 . 
       FIG. 13B  is a descriptive drawing showing the relation between the event cluster  1308  and the feature vector  1101  in the first embodiment of the present invention. 
     The event cluster center-of-gravity  1309  is the center-of-gravity of the event cluster  1308 . The event cluster  1308  is a hyper sphere, or namely is sphere in a multidimensional feature space. The event cluster radius  1310  is the radius of the event cluster  1308  whose center is the event cluster center-of-gravity  1309 . 
     The distance between the event cluster center-of-gravity  1309  and the feature vector  1101  is the same as the event distance  1503  that was already described. The closer that the feature vector  1101  approached the center-of-gravity of the event cluster  1308 , the higher the probability that the event  1305  corresponding to the event cluster  1308  will occur in the device  102  or the pipe  103  connected to the sensor  104  corresponding to the feature vector  1101 . 
       FIG. 13C  is a descriptive drawing showing the (math) function for calculating the reliability  1302  in the first embodiment of the present invention. 
     The reliability  1302  shows the possibility that no event  1305  will occur. The reliability  1302  is calculated by dividing the event distance  1503  by the event cluster radius  1310 . The larger the value of the event distance  1503  and the smaller the value of the event cluster radius  1310 , the higher the reliability  1302  value becomes, and the higher the possibility that no event  1305  will occur. 
     Also, the lower the value of the event distance  1503  and the higher the value of the event cluster radius  1310 , the smaller the reliability  1302  value becomes, and the lower the possibility that the event cluster  1308  will occur. In other words, the event distance  1503  becomes smaller than the event cluster radius  1310 , and if the reliability  1302  becomes a value lower than 1, then there is a high possibility that the event  1305  will occur. 
     Conversely, the higher the event cluster radius  1310 , and the lower the event distance  1503 , the lower the reliability  1302  value becomes. The smaller the reliability  1302  value the higher the possibility that the event cluster  1308  will occur. 
       FIG. 14  is descriptive drawing showing the input/output data for VQC by the examination unit  406  of the first embodiment of the present invention. 
     The feature vector  1101  generated by the observation value processor unit  405  is sent to the examination unit  406 . The examination unit  406  generates an examination results  1300  by utilizing the codebook  410  contained in the feature vector  1101 , and the device cluster  407 . 
     The examination results  1300  include the event ID 1301 , the reliability  1302 , and the contribution rate  1303 . The event ID 1301  is an identifier showing the unique event  1305 . The reliability  1302  is the possibility calculated by using the formula shown in  FIG. 13C , and the event  1305  shown by the event ID 1301  might not occur in the device  102  or the pipe  103  connected to the sensor  104  corresponding to the feature vector  1101 . 
     The contribution rate  1303  contained (contribution rate  1303 - 1  through contribution rate  1303 - n ) in each element of the feature vector  1101  is a numerical value showing the extent of the contribution the elements apply to the examination results  1300 . 
     The contribution rate  1303  is found by calculating each of the distance between the each element contained in the feature vector  1101 , and the center-of-gravity of the event  1305 ; and then calculating the percent that the calculated distances between the center-of-gravity of event  1305  and each element occupies in the total sum of the distance between the event  1305  center-of-gravity and all elements. The contribution rate  1303  may also be standardized so that summing all of the contribution rates  1303  attains a 1. 
     The feature vector  1101  for p3 shown in  FIG. 13A  is contained in the event cluster  1308  in the value for element V1 but is not contained in the event cluster  1308  in the value for element V2. Therefore when calculating the examination results  1300 , the element V2 contributes more to the examination results  1300  so that the contribution rate  1303  is high. 
     The code book  410  includes the event set  1304 , the event  1305 , the event cluster  1308  and the event cluster center-of-gravity  1309 . The event set  1304  includes 0 or one or more events  1305 . The code book  410  contains information placed in advance relating to the event  1305 . 
     The event  1305  includes the event  1131301  and the event cluster set  1307 . The event  1305  includes 0 or one or more event clusters  1308 . 
     The event cluster  1308  includes the event cluster center-of-gravity  1309  and the event cluster radius  1310 . The event cluster  1308  includes 0 or one or more event cluster center-of-gravity  1309 . 
     The event cluster center-of-gravity  1309  includes the center-of-gravity (position)  1311 . The event cluster center-of-gravity  1309  includes a number (quantity) of elements for the feature vector  1101 , or namely a number (quantity) of center-of-gravity positions  1311 - 1  through  1311 - n  for the feature space (dimension). 
       FIG. 15  is a flow chart showing the VQC by the examination unit  406  of the first embodiment of the present invention. 
     When the feature vector  1101  is sent from the observation value processor unit  405 , the examination unit  406  stores a numerical value infinity for the nearest distance to initialize (step  1401 ). This nearest distance is a parameter. The examination unit  406  then selects one event  1305  from the event  1305  contained in the code book  410  (step  1402 ), and selects an event cluster  1308  contained in the event  1305  selected in step  1402  (step  1403 ). 
     The examination unit  406  compares the event cluster center-of-gravity  1309  for the event cluster  1308  selected in step  1403 , with the feature vector  1101  sent from the observation value processor unit  405 , and calculates the distance (step  1404 ). The function utilized when calculating the distance is shown below 
     
       
         
           
             
               
                 
                   
                     
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     Here, Vt is a value for the element of the feature vector  1101 . Also, Ct is a value of the center-of-gravity (position)  1311  for the event cluster  1308 . The t in the formula indicates one element among all of the feature spaces (dimensions) and is a number from 0 to the feature space. 
     The examination unit  406  then stores the event cluster  1308  with the shortest distance among the calculated distance and the distances calculated up to then into the nearest cluster (step  1405 ). The nearest cluster is a parameter. The shortest distance among the distances calculated up to then and step  1404  is stored in the shortest distance (step  1406 ). 
     The examination unit  406  then decides whether or not the distance with all the event clusters  1308  was calculated (step  1407 ), and returns to step  1403  if decided the distance with all the event clusters  1308  was not calculated. If the distance for all the event clusters  1308  was calculated then the examination unit  406  decides whether or not the distance for the event clusters  1380  included in all the event  1305  was calculated or not (step  1408 ), and returns to step  1402  if decided that the distances for the event clusters  1308  in all of the events  1305  were not calculated. 
     If decided that the distance for the event clusters  1308  contained in all the event cluster  1305  was calculated, then the examination unit  406  acquires the event ID  1301  contained in the event clusters  1308  shown by the nearest cluster, and stores the acquired event ID  1301  in the event ID  1301  of the examination results  1300  (step  1409 ). Then the reliability  1302  is calculated by subtracting the distance calculated in step  1404  from the event cluster  1308  shown by the nearest cluster (step  1410 ). 
     The examination unit  406  further calculates the contribution rate  1303  (step  1411 ) based on the feature vector  1101 , the event cluster radius  1310 , and the center-of-gravity position  1311 , and outputs the examination results  1300 . 
     The above information described the processing up through step  514  in  FIG. 5 . 
       FIG. 16  is a flow chart showing an overview of the processing for generating the device cluster  407  in the first embodiment of the present invention. 
     The selection rule maker unit  412  generates a new device graph  502  when a new device  102  or pipe  103  has been added to the plant  101  (step  1601 ), and generates a sensor selection rule  408  (step  1602 ). The processing rule maker unit  413  then generates an observation value processing rule  409  (step  1603 ). That examination rule maker unit  414  then generates a code book  410  (step  1604 ). 
       FIG. 17  is a flow chart showing in detail the processing for generating the device cluster  1407  in the first embodiment of the present invention. 
     If a new device  102  or pipe  103  was added to the plant  101 , then the examination server  107  searches device clusters  407  having a device graph  502  similar to the structure of the new device  102  or pipe  103  (step  1701 ). The procedure for searching for a device cluster  407  containing device graph  502  is described later on. The examination server  107  then decides whether or not a similar device cluster  407  was found (step  1702 ). If the examination server  107  does not find a similar device cluster  407 , then the administrator inputs a device cluster  407  via the examination server  107  by way of the steps from  1703  onwards. 
     The administrator makes a device graph  502  based on the design documents (step  1703 ), makes a sensor selection rule  408  (step  1705 ), makes an observation value processing rule  409  (step  1705 ), and inputs each of these rules to the examination server  107 . 
     The administrator then has the learning unit  304  of examination server  107  learn (step  1706 ) the actual events that occurred in specified periods such as a half-year or one year through the usual tasks performed by the new device  102  or pipe  103 . Namely in step  1706 , the learning unit  304  acquires the observation values occurring at specified intervals and learns the contents of the event corresponding to the acquired observation values through entries made by the administrator. 
     The examination server  107  makes the code book  410  by the learning unit  304  shown in  FIG. 3  based on the observation values acquired in step  1706 , and the contents of the events input by the administrator. The examination server  107  then itself inputs the code book  410  that was made in step  1706  (step  1707 ). 
     The examination server  107  then registers (step  1708 ) the device cluster  407  by assigning the device clusters ID 501  to each of the information made in the steps  1703 - 1707 . 
     The examination server  107  then judges whether or not a device cluster  407  was made for all the new devices  102  or pipes  103  (step  1709 ). If there are devices  102  or pipes  103  for which a device cluster  407  was not made, then the processing returns to step  1701 . If a device cluster  407  was made for all the new devices  102  or pipes  103  then the processing ends. 
     The processing from step  1709  onwards may be executed in parallel with the processing from step  1706  since a specified time actually elapses in step  1706 . 
     When decided in step  1702  that a similar device cluster  407  was found by the examination server  107 , the selection rule maker unit  412  stores the similar device graph  502  for the device cluster  407  found in step  1701 , into the device graph  502  for the device cluster  407  of the new device  102  or the pipe  103  (step  1710 ). 
     The selection rule maker unit  412  further stores the sensor selection rule  408  for the similar device cluster  407  into the sensor selection rule  408  for the device cluster  407  of the new device  102  or pipe  103  (step  1711 ). A decision is also made on whether or not all the sensor selection rules  408  corresponding to the device  102  or pipe  103  shown in the device graph  502  were stored (step  1712 ) and if there are device graphs  502  in which the sensor selection rules  408  were not stored then the processing returns to step  1711 . 
     If all of the sensor selection rules  408  corresponding to device  102  or pipe  103  shown in the device graph  502  were stored, then the processing rule maker unit  413  stores the observation value processing rule  409  of the similar device cluster  407  into the observation value processing rule  409  for the device cluster  407  of the new device  102  or pipe  103  (step  1713 ). Also a code book  410  for the similar device cluster  407  is stored in the code book  410  of the device cluster  407  of the new device  102  or pipe  103  (step  1714 ). 
     The examination server  107  next decides whether or not correction of the code book  410  stored in step  1714  is required (step  1715 ). The examination server  107  decides whether or not correction of the code book  410  is required by way of instructions entered by the administrator. The administrator judges whether or not there are records in the code book  410 , and enters the decision results in the examination server  107 . 
     When decided in step  1715  that no correction of the code book  410  is needed, the examination server  107  shifts to step  1708 , and registers the device cluster  407 . 
     If decided in step  1715  that correction of the code book  410  is needed, then the examination server  107  diagnoses the time-series data  704  in cases that occurred in the past by way of the newly generated device cluster  407 . The examination server  107  then outputs the obtained examination results to an output device to allow confirmation by the administrator (step  1716 ). 
     The administrator or other party checks the examination results and decides if relearning is required or not, and inputs information showing whether relearning is necessary or not to the examination server  107 . 
     The examination server  107  acquires the information input by the administrator showing whether relearning is necessary or not and decides whether or not changing the device cluster  407  by relearning is needed (step  1717 ). If decided in step  1717  that relearning is not necessary then the examination server  107  shifts to step  1708  and registers the device cluster  407 . 
     If decided in step  1717  that relearning is necessary, then the examination server  107  makes the relearning unit  305  perform the relearning at specified periods such as a half-year or one year (step  1718 ). The relearning unit  305  performs the relearning by refreshing the event cluster center-of-gravity  1309  and event cluster radius  1310  based on the event cluster  1305  that occurred in a specified period. 
     The examination server  107  corrects the code book  410  of the new device cluster  407  based on the results from step  1718  (step  1719 ), and shift to step  1708 . 
     The examination server of the present invention is capable of applying the examination rule for a similar device cluster  407  to the new device cluster  407  by performing step  1710  through step  1719 . Moreover, even more flexible examination rules can be applied to the new device cluster  407  by relearning, etc. 
     The examination server  107  of the present embodiment searches similar device clusters  407  by way of similar device graphs  502  or in other words, searches the connection relations of similar devices  102  and so on from among existing devices  102 , etc. 
     In the event of new construction at the plant  101 , or addition of the device  102  or the pipe  103 , then the examination server  107  acquires the device cluster  407  corresponding to devices  102  or pipes  103  having a connection relation similar to the new device  102  or the pipe  103  connection relation, among device clusters  407  corresponding to existing devices  102  or pipes  103 ; and generates a device cluster  407  corresponding to the new device  102  or pipe  103  based on the acquired device cluster  407 . 
     In the following description, the device  102  or pipe  103  are given the collective name of node. 
       FIG. 18A  is a descriptive drawing showing a specific example for acquiring similar device graph  502  in the first embodiment of the present invention. 
       FIG. 18A  shows the case where adding a new node A (nodes A1-A6) to a plant  101  containing the existing node B (nodes B1-B4). When the device graph  502  of the nodes A1-A4 among the added nodes A1-A6 are similar to the device graph  502  of the existing (nodes) B1-B4, a device cluster  407 A corresponding to the nodes A1-A6 is generated based on the device cluster  407 B containing the device graph  502  of the nodes B1-B4. 
       FIG. 18B  is a descriptive drawing showing the generation of a sensor selection rule  408  in the first embodiment of the present invention. 
     In step  1701  of  FIG. 7 , when the device cluster  407  corresponding to the nodes A1-A4 shown in  FIG. 18A  is searched for a device cluster  407  similar to the device cluster  407  corresponding to existing nodes B1-B4, the selection rule maker unit  412  in step  1711  of  FIG. 17 , generates a new sensor selection rule  408 A based on the sensor selection rule  408 B contained in the device cluster  407  searched in the step  1701 . 
     The selection rule maker unit  412  first of all makes a combination of nodes A1-A4, and nodes B1-B4 resembling each of the nodes A1-A4, from the results searched in step  1701 . The selection rule maker unit  412  then generates a sensor map  2004  by substituting each node the ID 802  of the sensors  104  connected to each node. 
     The selection rule maker unit  412  further generates a sensor selection rule  408 A for the new nodes A1-A4, based on the sensor map  2004  and the sensor selection rule  408 B corresponding to the existing nodes B1-B4. 
     The procedure for acquiring a similar device graph  502  in order to search for a similar device cluster  407  in step  1701  of  FIG. 17  is shown next. 
       FIG. 19  is a flow chart showing the procedure for acquiring a similar device graph  502  in the first embodiment of the present invention. 
     The processing shown in  FIG. 19  is equivalent to step  1701  in  FIG. 17 . The following description is for an example in the case where the nodes A1-A4 were added as shown in  FIG. 18A . 
     The similar graph search unit  302  contained in the examination server  107  selects one among the new nodes A1-A4 (step  1801 ) when the new nodes A1-A4 have been added to the plant  101 . The node selected in step  1801  is described as node A. 
     One (node) among the existing nodes B1-B4 is selected an object for evaluation (step  1802 ). The node selected in step  1802  is described as node B. 
     After step  1802 , the similar node search unit  303  calculates the (degree of) similarity of the node B selected in step  1802  and the node A selected in step  1801  (step  1803 ). The (degree of) similarity is defined by finding values more than zero and setting the smaller one as similar. The method for calculating the similarity is described later on. 
     The similar graph search unit  302  then decides (step  1804 ) whether the (degree of) similarity calculated in step  1803  is at or below a pre-established threshold value. If the calculated similarity is below the threshold value, then the node A and the node B are similar so that the device graph  502  contained in node B is added to the similar device graph (step  1805 ). 
     The similar graph search unit  302  stores the similarity calculated in step  1803  in the graph similarity of RDB 306  (step  1806 ). The similar device graph and the graph similarity are a storage region stored in the RDB 306 . 
     If the similarity calculated in step  1804  is higher than the threshold value, or after step  1806 , then the similar graph search unit  302  decides whether the existing nodes B1-B4 were all selected or not (step  1807 ). If decided that not all of the existing nodes B1-B4 were selected then the similar graph search unit  302  returns to step  1802 . 
     The similar graph search unit  302  selects node B as a reference for comparing with Node A per the processing through step  1807 . 
     If decided that all of the existing nodes (B1-B4) were selected in step  1807 , then the similar graph search unit  302  selects one existing device graph  502  as an object for evaluation among similar device graphs added in step  1805  (step  1808 ). The similar graph search unit  302  then calculates the similarity of the device graph  502  by utilizing the subroutine X (step  1809 ). 
     The similar graph search unit  302  decides whether or not the similarity of the device graph  502  calculated in step  1809  is above a threshold value (step  1810 ). If the similarity of the device graph  502  calculated in step  1809  is higher than the threshold value then the similar graph search unit  302  deletes the device graph  502  selected in step  1808  from the similar device graphs (step  1811 ) since the device graph  502  selected in step  1808  has no similarity. 
     If the similarity of the device graph  502  calculated in step  1810  is lower than the threshold value, or after step  1811 , then the similar graph search unit  302  outputs the device graph  502  candidate stored in the similar device graph (step  1813 ). If plural device graphs  502  were stored in the similar device graph, then all of the device graphs  502  are output and the administrator may also select a device graph  502 . 
     The similar graph search unit  302  then outputs the device cluster  407  contained in the device graph  502  having the smallest (degree of) similarity as a similar device cluster for processing in step  1701  of  FIG. 17 . When a similarity is 0, or namely when the comparison source and the comparison target device clusters are a complete match, a code book correction is not required so that the steps  1715 - 1719  in  FIG. 17  are omitted and the branch can be set for shifting from step  1714  to step  1708 . However when the similarity is greater than 0, or namely when the comparison source and the comparison target device clusters do not match, the device clusters are judged similar so that the threshold value is set and the applicable threshold value can be set beforehand by the user in the previously described similar graph search unit  302 . However, when the output results in the device cluster processed by the similar graph search unit  302  were judged as unnecessary in step  1717  from results in an abnormal decision check from a past case previously described in step  1716  of  FIG. 17 , then the similarity of the device cluster when this (relearning) was judged unnecessary is substituted with a threshold value set beforehand by the user so that the similarity threshold value can also be set automatically. This automatic setting allows reducing the number of man-hours for optimizing of the user threshold value settings, and can also enhance an optimized objectivity or reproducibility. Moreover this automatic setting also allows extracting a device cluster not requiring relearning and the similarity is small at the step  1701  stage, so that the processing is speeded up by avoiding the relearning step. 
     A similar device graph  502  is acquired by way of the processing shown in  FIG. 19 , and a device cluster  407  corresponding to the acquired device graph  502  serves as the search results in step  1701 . 
       FIG. 20  is a flow chart showing the method for calculating the similarity of the device graphs  502  in the first embodiment of the present invention. The processing shown in  FIG. 20  is equivalent to step  1809  in  FIG. 19 . 
     The similar graph search unit  302  selects the node A adjacent to the node A selected in step  1801  of  FIG. 19  (step  1901 ). The similar graph search unit  302  decides if there is an adjacent node A in step  1801  or not (step  1902 ), and if there is no adjacent node A, then the similar graph search unit  302  ends the processing of subroutine X. 
     If there is an adjacent node A in step  1801 , then the similar graph search unit  302  selects the node corresponding to the device graph  502  selected in step  1802 , or in other words selects a node adjacent to the node B selected in step  1802  (step  1903 ). 
     For example if the similarity of the node A1 selected in step  1801  in  FIG. 19  and the node B1 selected in step  1802  is lower than the threshold value, and if the device graph  502  contained in node B1 in step  1808  is selected in step  1903 , the similar graph search unit  302  then selects node B2 adjacent to node B1 shown in  FIG. 18A . 
     Next, the similar node search unit  303  calculates the similarity between the node A selected in step  1901  and node B selected in step  1903  (step  1904 ). The method for calculating the node similarity is described later on. 
     If the node A selected in step  1901  is for example node A2 shown in  FIG. 18A , and node B selected in step  1903  is the node B2 shown in  FIG. 18A , then the similar graph search unit  302  calculates the similarity of node A2 and node B2 in step  1904 . 
     After step  1904 , the similar graph search unit  302  then decides whether or not all nodes adjacent to node B selected in step  1802  were selected (step  1905 ). If all the nodes adjacent to node B were not selected then the similar graph search unit  302  returns to step  1903 . If all the nodes adjacent to node B were selected, then the similar graph search unit  302  shifts to step  1906 . 
     When the node B selected in step  1903  for example was the node B2 shown in  FIG. 18A , then there is no other adjacent node to node B1 so that the similar graph search unit  302  decides that all nodes adjacent to node B1 were selected in step  1905 . 
     The similar graph search unit  302  then adds the similarity of all adjacent nodes calculated in step  1904 , and further adds this summed adjacent node similarity to the similarity of device graph  502  to refresh the similarity of the device graph  502  (step  1906 ). 
     When step  1906  is complete, the similar graph search unit  302  recursively calls up the subroutine X (step  1907 ) in order to also perform the processing on the node B selected in step  1903  same as the adjacent nodes. The similar graph search unit  302  stores the node A selected in  1901 , and the node B with the smallest value among the similarities calculated in step  1094 , and executes the recursively called subroutine X based on the stored node information. 
     If for example the node A selected in step  1901  was the node A2 shown in  FIG. 18A ; and the node B with the smallest value of similarity calculated in step  1904  was the node B2; then the similar graph search unit  302  selects the node A3 as the node A adjacent to node A2, in the recursive call subroutine X of step  1901 . The similar graph search unit  302  selects the node B3 connected to node B2 in recursively called subroutine X in step  1903 . 
     The similar graph search unit  302  searches for a node similar to the newly added node from the existing nodes, and acquires the device graph  502  corresponding to the similar node by the procedure shown in  FIG. 19  and  FIG. 20 . 
       FIG. 21  is a flow chart showing the procedure for calculating the similarity of the nodes in the first embodiment of the present invention. The processing in  FIG. 21  is identical to the processing in step  1803  of  FIG. 19  and step  1904  in  FIG. 20 . 
     The similar node search unit  303  refers to the device information  702  for node A and node B when a new node A and existing node B were selected in step  1802  or step  1903 . The similar node search unit  303  then selects an attribute  804  matching each of the attribute names  805  among the device information  702  for node A and node B (step  2101 ). 
     Next, the similar node search unit  303  decides if the data type  814  for the attribute  804  selected in step  2101  is a numerical value or a character string based on the schema information  810  (step  2102 ). If the data type  814  for the selected attribute  804  is a numerical value, and the attribute value  806  for node A and the attribute value  806  for node B can be mutually calculated, then the similar node search unit  303  calculates the attribute distance for node A and node B based on the attribute value  806  of the selected attribute  804  (step  2103 ). 
     Here, the attribute distance is a parameter quantitatively showing the difference between the two nodes. 
     If the data type  814  for the selected attribute  804  is a character string, and calculating among the attributes  806  is impossible, then the similar node search unit  303  decides if the attribute value  806  of node A and attribute value  806  of the selected node B are a match or not (step  2104 ). If the attribute value  806  of the selected node A and the attribute value  806  of node B are a match, then the similar node search unit  303  sets the attribute distance of the node A and node B to 0 (step  2105 ). If the attribute value  806  of the selected node A and the attribute value  806  of node B do not match, then the similar node search unit  303  sets the attribute distance between node A and node B to infinity (step  2106 ). 
     After performing step  2103 , step  2105 , or step  2106 , the similar node search unit  303  multiplies the attribute distance between node A and node B calculated in step  2103 , step  2105 , or step  2106 , by the similarity coefficient  815  decided beforehand according to the attribute name  805  (step  2107 ). 
     The similar node search unit  303  also refreshes the node similarity by using the value calculated in step  2107  (step  2108 ). In other words, the similar node search unit  303  calculates the node similarity by way of the following formula (2) using the attribute distance between the nodes and the similarity coefficient  815 . 
     
       
         
           
             
               
                 
                   
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     The k in formula 2 denotes the similarity coefficient  815 , which is established beforehand for each attribute. The a i  denotes a new node attribute value, and b i  denotes an existing node attribute value. The node similarity is calculated by finding the sum of multipliers obtained from the square of the difference between a i  and b i  times the similarity coefficient  815  for all attributes. The similarity coefficient  815  is stored in the schema information  810 . 
     The similar node search unit  303  decides if the attribute distance and the node similarity were calculated for all attributes or not, and if all attributes were not calculated, returns to step  2101 . If all attributes were calculated, then the similar node search unit  303  outputs the sum of the node similarities (step  2110 ), and the similarity calculation procedure then ends. 
     The processing shown in  FIG. 21  calculated the node similarity however the sum of the node similarity is the similarity of the device graph  502 . Moreover, the similarity of the device graph  502  is the similarity among the device clusters  407  or namely is the distance of the device clusters  407 . The sum of the node similarity hereafter indicates the same meaning as the distance of the device clusters  407 . 
       FIG. 22  is a descriptive drawing showing a specific example of calculating the attribute distance in the first embodiment of the present invention. 
     The attribute  804  of node A selected in step  1801  of  FIG. 19  or step  1901  of  FIG. 20  is shown as attribute  804 A- 1 . Also, the attribute  804  of each node B selected from the existing node B in step  1802  or step  1903  is shown as the attributes  804 B- 1  through attribute  804 B- 4 . 
     The attribute  804 A- 1  includes the attribute  804 A- 1 - 1 , attribute  804 A- 1 - 2 , and the attribute  804 A- 1 - 3 . The attribute  804 A- 1 - 1  has “device type” as the attribute name  805 ; and “pump” as the attribute value  806 . The attribute  804 A- 1 - 2  has “sensor type” as the attribute name  805 ; and “vibration” as the attribute value  806 . The attribute  804 A- 1 - 3  has “years used” as the attribute name  805 ; and “2 years” as the attribute value  806 . 
     The attribute  804 B- 1  includes the attribute  804 B- 1 - 1  and the attribute  804 B- 1 - 2 . The attribute  804 B- 1 - 1  has “device type” as the attribute name  805 ; and “motor” as the attribute value  806 . The attribute  804 B- 1 - 2  has “sensor type” as the attribute name  805 ; and “vibration” as the attribute value  806 . 
     The attribute  804 B- 2  includes the attribute  804 B- 2 - 1  and the attribute  804 B- 2 - 2 . The attribute  804 B- 2 - 1  has “device type” as the attribute name  805 ; and “pump” as the attribute value  806 . The attribute  804 B- 2 - 2  has “sensor type” as the attribute name  805 ; and “pressure” as the attribute value  806 . 
     The attribute  804 B- 3  includes the attribute  804 B- 3 - 1 , the attribute  804 B- 3 - 2 , and the attribute  804 B- 3 - 3 . The attribute  804 B- 3 - 1  has “device type” as the attribute name  805 ; and “pump” as the attribute value  806 . The attribute  804 B- 3 - 2  has “sensor type” as the attribute name  805 ; and “vibration” as the attribute value  806 . The attribute  804 B- 3 - 3  has “years used” as the attribute name  805 ; and “10 years” as the attribute value  806 . 
     The attribute  804 B- 4  includes the attribute  804 B- 4 - 1 , the attribute  804 B- 4 - 2 , and the attribute  804 B- 4 - 3 . The attribute  804 B- 4 - 1  has “device type” as the attribute name  805 ; and “pump” as the attribute value  806 . The attribute  804 B- 4 - 2  has “sensor type” as the attribute name  805 ; and “vibration” as the attribute value  806 . The attribute  804 B- 4 - 3  has “years used” as the attribute name  805 ; and “1 year” as the attribute value  806 . 
     When the attribute  804 A- 1 - 1  and the attribute  804 B- 1 - 1  were selected in step  2101  of  FIG. 21 , the attribute  804 A- 1 - 1  and the attribute  804 B- 1 - 1  both have character strings as the data type  814  so that a decision is made on whether the attribute values  806  are a match or not. These attribute values  806  are respectively a “pump” and “motor” and so are decided as a mismatch, and infinity is stored in the attribute distance. 
     Here, when the similarity coefficient  815  whose attribute  804  is “device type” and is not 0, the similarity of the device cluster  407  calculated in step  2110  becomes infinity. 
     When the attribute  804 A- 1 - 1  and the attribute  804 B- 2 - 1  were selected in step  2101  of  FIG. 21 , the attribute values  806  of the attribute  804 A- 1 - 1  and the attribute  804 B- 2 - 1  are both the same so that a “0” is stored in the attribute distance. Further, when the attribute  804 A- 1 - 2  and the attribute  804 B- 2 - 2  were selected, the attribute value  806  of the attribute  804 A- 1 - 2  is “vibration”, and the attribute value  806  of the attribute  804 B- 2 - 2  is “pressure” so that the attribute are decided as a mismatch in step  2104 , and infinity is stored in the attribute distance. 
     When the attribute  804 A- 1 - 1  and the attribute  804 B- 3 - 1  were selected in step  2101  of  FIG. 21 , the attribute value  806  of the attribute  804 A- 1 - 1  and the attribute  804 B- 3 - 1  are the same so that a “0” is stored in the attribute distance. The attributes  804 A- 1 - 2  and the attribute  804 B- 3 - 2  likewise, both have the same attribute values  806  for the attribute  804 A- 1 - 2  and the attribute  804 B- 3 - 2  are the same so that a “0” is stored in the attribute distance. 
     When the attribute  804 A- 1 - 3  and the attribute  804 B- 3 - 3  were selected in step  2101  of  FIG. 21 , the attribute  804 A- 1 - 3  and the attribute  804 B- 3 - 3  both have numerical value as the data type  814  so that the attribute distance is calculated. The attribute value  806  of the attribute  804 A- 1 - 3  is “2 years”, and the attribute value  806  of the attribute  804 B- 3 - 3  is “10 years” so that the attribute distance is calculated as |2−10|=8. 
     When the attribute  804 A- 1 - 1  and the attribute  804 B- 4 - 1  were selected in step  2101  of  FIG. 21 , the attribute values  806  of the attribute  804 A- 1 - 1  and the attribute  804 B- 4 - 1  are both the same so that a “0” is stored in the attribute distance. The attribute  804 A- 1 - 2  and the attribute  804 B- 4 - 2  likewise, both have the same attribute value  806  for the attribute  804 A- 1 - 2  and the attribute  804 B- 4 - 2  so that a “0” is stored in the attribute distance. 
     When the attribute  804 A- 1 - 3  and the attribute  804 B- 4 - 3  were selected in step  2101  of  FIG. 21 , the attribute  804 A- 1 - 3  and the attribute  804 B- 4 - 3  both have numerical value as the data type  814  so that the attribute distance is calculated. The attribute value  806  of the attribute  804 A- 1 - 3  is “2 years”, and the attribute value  806  of the attribute  804 B- 4 - 3  is “1 year” so that the attribute distance is calculated as |2−1|=1. 
     The attribute distance in the present embodiment is calculated as previously described. The calculated attribute distance is multiple by the similarity coefficient  815 , and added to the similarity of the device cluster  407 . If the attribute values  806  are a match, then a “0” was stored in the attribute distance, however any value that is as small as possible but is not “0” may be stored. The administrator can in this way calculated the node similarity so that a more important attribute  804  can be selected. 
     The procedure for calculating the similarity coefficient  815  is shown next. 
       FIG. 23  is a flow chart showing the procedure for calculating the similarity coefficient  815  in the first embodiment of the present invention. 
     First of all, the similar node search unit  303  calculates the distance of the event cluster  1308  corresponding to each device cluster  407  (step  2301 ). Here, when there are N number of similarity coefficients  815  to calculate, the sum total of volumes in the device cluster  407  contained in the attribute  804  corresponding to similarity coefficient  815 ; and also in the dimension of the corresponding event cluster  1308  is shown by Si (i=0 through N). 
     Device clusters  407  equal in number to the attributes  804  corresponding to the similarity coefficient  815 , and one device cluster  407  serving as the reference for calculating the similarity coefficient  815  are selected beforehand for the device cluster  407  utilized for calculating the similarity coefficient  815 . 
     The “dimension” for the total sum of volume Si in the dimension of event cluster  1308  is the number of elements V shown in the  FIG. 13A  corresponding to the event cluster  1308 . The “dimensional volume” shows the volumetric range to distribute the event cluster  1308  in each dimension. The “sum total” is the value from summing all the volumes of each event cluster  1308  because there are plural event clusters  1308  for each event  1305 . 
     Here, S0 is the sum total of the volume in the dimension of the event cluster  1308  corresponding to the device cluster  407  serving as a reference for calculating the similarity coefficient  815 . The event cluster  1308  distance is defined by the formula 3 shown below. 
     
       
         
           
             
               
                 
                   
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     Here, |Si−S0| is the difference between the sum total of the volumes of the event cluster  1308 . Formula 3 is used to calculate the percentage that the difference between the sum total of the volumes of the event cluster  1308  as an object for comparison and the sum total of the volumes of the event cluster  1308  serving as a reference, occupies in the sum total of the volumes of the event cluster  1308  serving as the reference, and those calculated results then define the distance of the event cluster  1308 . 
     The similar node search unit  303  calculates the distance of the event cluster  1308  by way of the formula 3. 
     Here, when the distance of the event cluster  1308  and the distance of the device cluster  407  are equal, then the following calculation can be made using formula 1 and formula 3. 
     
       
         
           
             
               
                 
                   
                     
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     Here, aji denotes the value for attribute  804  contained in the device cluster  407  serving as the reference, and bji denotes the attribute  804  contained in the device cluster  407  serving as the object for comparison. The i denotes the number of similarity coefficients  815  for calculation as previously described or in other words is the number of attributes  804 . The j is the number of device clusters  407  serving as the object for comparison or in other words is the number of similarity coefficients  815  for calculation. 
     The first term on the left side is an expression describing the distance of the device cluster  407  by way of a matrix. The right side is an expression showing the distance of the event cluster  1308  calculated in step  2301  by way of a matrix. 
     The similar node search unit  303  calculates the matrix showing the distance of the device cluster  407  (step  2302 ). The similar node search unit  303  further calculates the value for ki serving as the similarity coefficient  815  by calculating the inverse matrix on the left side of formula 4 (step  2303 ). The similar node search unit  303  then outputs the calculated value for the similarity coefficient  815  (step  2304 ). 
     The similarity coefficient  815  is therefore calculated as described above. 
       FIG. 24A  is a descriptive drawing showing an example of the distance of the device cluster  407  in the first embodiment of the present invention. 
     The Table  2041  is a table showing the distance of the device clusters C0-C3 ( 407 ) utilizing the attribute  804  of the nodes contained in each of the device cluster C0 ( 407 ), device cluster C1 ( 407 ), and the device cluster C2 ( 407 ). The horizontal axis in Table  2401  is the frequency of usage, and the vertical axis is the years used. The vertical axis and the horizontal axis in Table  2401  correspond to the attribute name  805 . 
     The attribute  804 - 00  for the node contained in the device cluster C0 ( 407 ) has 2 years as the used years, and 10 times per year as the frequency of usage. The attribute  804 - 01  for the node contained in device cluster C1 ( 407 ) has 10 years as the used years, and 20 times per year as the frequency of usage. The attribute  804 - 02  for the node contained the device cluster C2 ( 407 ) has 1 year as the used years, and 80 times per year as the usage frequency. 
     In the similarity coefficient  815  for the years used are shown by k1, and the frequency of usage for the similarity coefficient  815  is shown by k2. 
     When using the device cluster C0 ( 407 ) as a reference, the distance for device cluster C1 ( 407 ) is k1 (2−10) 2+k2 (10−20)2; and the distance for the device cluster C2 ( 407 ) is k1 (2−1)2+k2 (10−80)2. 
       FIG. 24B  is a descriptive drawing showing an example of the distance of the event cluster  1308  in the first embodiment of the present invention. 
     The event cluster  1308  of device cluster C0 ( 407 ) is plural true spheres shown by the event clusters  1308 - 00  in  FIG. 24B . The event cluster  1308  of device cluster C1 ( 407 ) is plural true spheres shown by the event clusters  1308 - 01 . The event cluster  1308  of device cluster C2 ( 407 ) is plural true spheres shown by the event clusters  1308 - 02  in  FIG. 24B . 
     The sum total of the volume of the true spheres of the event cluster  1308 - 00  is shown by S0, the sum total of the volume of the true spheres of the event cluster  1308 - 01  is shown by S1, and the sum total of the volume of the true spheres of the event cluster  1308 - 02  is shown by S2. 
     The distance of the event cluster  1308  between the device cluster C0 ( 407 ) and the device cluster C1 ( 407 ) by way of the above described formula 3 is shown by |S1−S0|/|S0|. The event clusters  1308 - 00  and the event clusters  1308 - 1  shown in  FIG. 24B  mostly overlap so that the value of |S1−S0| is smaller than |S0|. The |S1−S0|/|S0| is therefore a value smaller than 1. 
     The distance of the event cluster  1308  between the device cluster C0 ( 407 ) and device cluster C2 ( 407 ) on the other hand is shown by |S2−S0|/|S0|. The event cluster  1308 - 00  and the event cluster  1380 - 02  shown in  FIG. 24B  do not overlap so that the |S2−S0|/|S0| is a value larger than 1. 
     Utilizing the distance for the event cluster  1308 , and the device cluster  407  calculated as described above allows establishing the simultaneous equation as shown below.
 
 k 1(2−10) 2   +k 2(10−20) 2   =|S 1 −S 0 |/|S 0|
 
 k 1(2−1) 2   +k 2(10−80) 2   =|S 2 −S 0 |/|S 0|  [Formula 5]
 
     If the value for |S1−S0|/|S0| is set as 0.1, and the value for |S2−S0|/|S0| is set as 2, then the value for k1 is calculated as 9.3e-4 (logarithmic notation), the value for k2 as 4.1e-4. 
     The first embodiment renders the effect that the man-hours required for setting examination rules can be reduced, and that examination rules for the new device  102  or pipe  103  can be set at an early stage, even if a new device  102  or pipe  103  was added, by applying an existing device cluster  407  to the new device  102  or pipe  103  having a structure similar to an existing device  102  or pipe  103 . 
     Second Embodiment 
       FIG. 25  is a descriptive drawing showing an example of applying a device cluster  407  to a new plant  101  in the second embodiment of the present invention. 
     As can be seen in  FIG. 25 , the existing plant  101 A contains the device or pipes m21-m26; and the new plant  101 B contains the device or pipes m27-m35. 
     In the present invention, when a plant  101 B was added to an existing plant  101 A, and a device cluster  407 E and device cluster  407 F were set in the existing plant  101 A, the device cluster  407 E and device cluster  407 F are applied to the device  102  or pipe  103  contained in the newly added plant  101 B. 
     A device combination having a device graph  502  similar to the device graph  502  for m21-m23 is extracted from the new plant  101 B, and applies the device cluster  407 E for the devices or pipes m21-m23 to this extracted device combination. The device cluster  407 G and device cluster  407 H are in this way generated in the device or pipes m27-m29 and m30-m32 as shown in the example in  FIG. 25 . 
     A device combination having a device graph  502  similar to the device graph  502  of the devices or pipes m24-m26 is also extracted from the new plant  101 B, and applies the device cluster  407 I for the device or pipe m24-m26 to this extracted device combination. The device cluster  407 I are in this way generated in the device or pipes m33-35 as shown in the example in  FIG. 25 . 
     Therefore, even if a new plant  101 B was added, the second embodiment is capable or reducing the number of required user man-hours and setting the examination rules at an early stage in the plant  101 B by applying the device cluster  407  contained in the existing plant  101 A. 
     The present embodiment was described above in detail while referring to the accompanying drawings however the present invention is not limited to these types of specific structures and may also include all manner of changes and equivalent structures that fall within the range of the appended claims.