Patent Publication Number: US-2023136749-A1

Title: Monitoring System, Monitoring Apparatus, and Monitoring Method

Description:
TECHNICAL FIELD 
     The present invention relates to a monitoring system, monitoring apparatus, and monitoring method for monitoring an operating status of monitoring object equipment. 
     BACKGROUND ART 
     The way of maintenance of equipment is making the transition from time based maintenance to perform periodic maintenance to condition based maintenance to perform maintenance according to the status of each equipment. It is necessary to always monitor the status in order to carry out the condition based maintenance and a remote monitoring service using IoT cloud has been being widely spread. 
     PTL 1 is known as a technique regarding a monitoring apparatus for monitoring equipment and detecting a predictive failure sign(s). PTL 1 recites that “monitoring data of a monitoring object system during a period in which no anomaly was detected regarding the monitoring object system is classified by days of week, time slots, dates, or the number of weeks and is stored in a storage unit, a permissible range is set based on a distribution by the days of week, time slots, dates, or the number of weeks of the stored monitoring data, and the monitoring data currently acquired from the monitoring object system is compared with the permissible range based on the distribution of the monitoring data of the relevant day of week, time slot, date, or the number of the relevant week to which the current date and time belongs; and if the acquired monitoring data exceeds an upper limit or a lower limit of the permissible range, a computer is caused to execute processing for detecting a predictive failure sign of the monitoring object system.” 
     CITATION LIST 
     Patent Literature 
     
         
         PTL 1: Japanese Patent Application Laid-Open (Kokai) Publication No. 2014-153736 
       
    
     SUMMARY OF THE INVENTION 
     Problems to be Solved by the Invention 
     PTL 1 discloses the technique for implementing the processing for detecting the predictive failure sign by using an appropriate threshold value according to the operating status of a computer system which is a monitoring object. However, the predictive failure sign is judged based on whether the result of monitoring the monitoring object equipment exceeds the threshold value or not; and there is no description about a method for judging the status of the equipment within the permissible range. 
     In order to realize the condition based maintenance of industrial equipment, it is required to judge not only whether a failure has occurred or not, but also judge the status of the equipment. It is an object of the present invention to provide a monitoring apparatus and monitoring system for diagnosing and visualizing the status of the monitoring object equipment for the sake of the condition based maintenance to perform the maintenance in accordance with the status of each equipment. 
     Means to Solve the Problems 
     A preferred example of the present invention is a monitoring apparatus including: an input unit that accepts equipment identifying information for identifying monitoring object equipment and an analysis purpose; and an output unit that, when analysis item data corresponding to the analysis purpose among operation data indicating an operating status of the identified monitoring object equipment is a value within a specified range on the basis of the equipment identifying information and the analysis purpose which have been input, outputs a tendency of the analysis item data compared with predetermined comparison item data. 
     Advantageous Effects of the Invention 
     The status of the equipment can be visualized according to the present invention. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a conceptual diagram illustrating the concept of a monitoring system according to Embodiment 1; 
         FIG.  2    is a configuration diagram illustrating the monitoring system according to Embodiment 1; 
         FIG.  3    is a diagram illustrating the configuration of an analysis server; 
         FIG.  4    is a diagram illustrating functional blocks of the analysis server; 
         FIG.  5    is a diagram illustrating an example of visualization; 
         FIG.  6    is a diagram illustrating a data format of an equipment identifying information DB; 
         FIG.  7    is a diagram illustrating a data format of an analysis item DB; 
         FIG.  8    is a diagram illustrating a data format of a specified range DB; 
         FIG.  9    is a diagram illustrating a data format of a comparison item DB; 
         FIG.  10    is a diagram illustrating a data format of a comparison data DB; 
         FIG.  11    is a diagram illustrating a data format of an analysis data DB; 
         FIG.  12    is a diagram illustrating a data format of an alarm and failure information DB  227 ; 
         FIG.  13    is a diagram illustrating a processing flow for creating a report; 
         FIG.  14    is a diagram illustrating a processing flow for extracting a tendency; and 
         FIG.  15    is a configuration diagram illustrating a monitoring system according to Embodiment 2. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Embodiments will be described below with reference to the drawings. 
     Embodiment 1 
     Firstly, the concept of a monitoring system according this embodiment will be explained.  FIG.  1    is a conceptual diagram illustrating the concept of a monitoring system according to Embodiment 1. An analysis server  20  illustrated in  FIG.  1    has a plurality of databases regarding the status of monitoring object equipment. 
     The monitoring object equipment is, for example, industrial equipment like an air compressor. The monitoring object equipment has a communication function and transmits data regarding the status to the analysis server  20  as appropriate. The analysis server  20  registers and manages alarms, failures, etc., which have occurred at the monitoring object equipment, in an alarm and failure information DB (DataBase)  227 . Furthermore, the analysis server  20  registers and manages data regarding the operation of the monitoring object equipment, as operation data, in an operation data information DB  224 . 
     The operation data information DB  224  includes a comparison data DB  225  and an analysis data DB  226 . Operation data of a specified period which is an analysis object is registered as analysis data in the analysis data DB  226 . Data to be used for a comparison with the analysis data is registered in the comparison data DB  226 . For example, if the specified period is the last three months, the operation data for the past three months may be registered in the analysis data DB  226  and the operation data before the last three months may be accumulated in the comparison data DB  225 . 
     The analysis server  20  can utilize these databases and automatically generate a report regarding the operating status of the monitoring object equipment. The generated report is visualized via display output or print output. For example, a support staff member in charge of the maintenance of the monitoring object equipment can print the report automatically generated by the analysis server  20  and provide it to a user of the monitoring object equipment. 
     Referring to  FIG.  1   , the report is provided with fields of a name, a graph of the operation data, an alarm and failure history, a maintenance history, and a comment. The name is provided to identify the object equipment and is “Equipment  1 ” in  FIG.  1   . The alarm and failure history is extracted from the alarm and failure information DB  227 . The maintenance history is extracted from, for example, a database for managing a maintenance history (which is not illustrated in the drawing). The graph of the operation data is extracted from the analysis data DB  226  and is displayed as a graph. 
     Comment data which reports the operating status of the monitoring object equipment is inserted into the comment field. The comment data functions as a general comment in the report and is not suited for uniform generation. For example, when generating the comment data, it is necessary to keep an amount of the comment within a proper range. This is because the size of the comment field for the display output or the print output is limited and also there occurs a risk of skipping without reading the comment if a large amount of information is listed. Therefore, if there is/are a report item(s) with high importance, it is desired that any item(s) with low importance should be omitted. On the other hand, if there is no report item with high importance, even the item(s) with low importance should also be reported. Particularly, from the viewpoint of the condition based maintenance, we can say that the challenge would be how early a report can be made to state that the operation data does not exceed a warning threshold value, but is in a state requiring caution (while the importance is low), rather than a report to state that the operation data exceeded the warning threshold value and the warning was made. 
     If the threshold value were simply lowered under this circumstance in order to judge that the operation data is in the state requiring caution, the warning could be made early, but the precision of the warning would degrade and the reliability would be impaired. So, the monitoring system according to this embodiment is designed so that if the analysis data which does not satisfy the warning threshold value, but is within a specified range close to the warning threshold value exists, a tendency of the analysis data as compared with comparison data is output as the comment. 
     Specifically, if an alarm or failure is registered in the alarm and failure information DB  227  as illustrated in  FIG.  1   , the analysis server  20  sets an actual result of the alarm or failure as the comment data. Then, if the alarm or failure is not registered, a tendency of the analysis data DB  226  as compared with the comparison data of the comparison data DB  225  is set as the comment data. 
     Next, the configuration of the monitoring system according to Embodiment 1 will be explained.  FIG.  2    is a configuration diagram illustrating the monitoring system according to Embodiment 1. The monitoring system according to Embodiment 1 is configured by connecting communication, for example, via wired or wireless communication between the analysis server  20  and a plurality of pieces of equipment  10  in a service base  100  which is a place to provide services.  FIG.  2    shows examples of equipment  10 - 1  to equipment  10 -N as the plurality of pieces of equipment  10 . 
       FIG.  3    is a diagram illustrating an example of the configuration of the analysis server  20 . The processing content of the analysis server  20  is stored (or recorded) in a form of a program(s) (software) in an auxiliary storage apparatus  204 , which is a general computer, and a CPU (Central Processing Unit)  202  decompresses the program(s), which has been read from the auxiliary storage apparatus  204 , in a memory  201  and executes it. The analysis server  20  communicates with other servers and equipment via a network I/F  205 . 
     An I/O (input/output interface)  203  is a user interface for a user to input instructions to the analysis server  20  and to present, for example, the execution results of the program to the user. Input/output devices (such as a keyboard, a mouse, a touch panel, a display, and a printer) are connected to the I/O  203 . A user interface which is provided by an administrative terminal connected via the network may be connected to the I/O  203 . 
     The CPU  202  is a processor for executing the program(s) stored in the memory  201 . The memory  201 : is configured from, for example, a RAM (Random Access Memory) which is a volatile storage element; and functions as a main storage apparatus for temporarily decompressing data related to the execution of the program(s). 
     Specifically, the memory  201  stores programs which operate as functional units such as a data input unit  211 , a data decision unit  212 , a data comparison unit  213 , a data output unit  214 , and a data visualization unit  215 . 
     Furthermore, the memory  201  temporarily stores data such as an analysis item DB  221 , a specified range DB  222 , a comparison item DB  223 , an operation data information DB  224 , an alarm and failure information DB  227 , an equipment identifying information DB  228  as necessary. 
     The auxiliary storage apparatus  204  is, for example, a large-capacity and nonvolatile storage apparatus such as a magnetic storage apparatus (HDD: Hard Disk Drive) and a flash memory (SSD: Solid State Drive). Furthermore, the auxiliary storage apparatus  204  stores the program(s) executed by the CPU  202  and data to be used when executing the program(s). Specifically speaking, the program(s) is read from the auxiliary storage apparatus  204 , is loaded to the memory  201 , and is executed by the CPU  202 . 
     The analysis server  20  is a computer system configured in a physically one computer or in a logically or physically plurality of computers; and the program(s) stored in the memory  201  may operate in separate threads in the same computer or may operate in a virtual computer which is constructed in a plurality of physical computer resources. Furthermore, the analysis server  20  and other devices may be accommodated in one physical or logical computer. Incidentally, the entire or part of processing implemented by executing the program(s) may be implemented by hardware (for example, Field-Programmable Gate Array). 
       FIG.  4    is a diagram illustrating functional blocks of the analysis server  20 . The analysis server  20  includes, as illustrated in  FIG.  4   , the data input unit  211 , the data decision unit  212 , the data comparison unit  213 , the data output unit  214 , and the data visualization unit  215 . 
     The data input unit  211  includes a monitoring object equipment identifying information input unit  231  and an analysis purpose input unit  232 . The monitoring object equipment identifying information input unit  231  accepts input of equipment identifying information for uniquely identifying the equipment  10 . The equipment identifying information is, for example, an equipment ID. The analysis purpose input unit  232  accepts input of an analysis purpose. For example, parameters indicating the status of the equipment such as a temperature, a pressure, and an electric current can be designated as the analysis purpose. 
     The data decision unit  212  includes an analysis item decision unit  241 , a specified range decision unit  242 , a comparison item decision unit  243 , and a comparison data decision unit  244 . 
     The analysis item decision unit  241  extracts an analysis item from the analysis item DB  221  in accordance with the monitoring object equipment identifying information and the analysis purpose, which are accepted by the data input unit  211 , and decides the analysis item. This analysis item is data as an analysis object corresponding to the analysis purpose among the operation data indicating the operating status of the equipment. For example, if the temperature is designated as the analysis purpose, data of the temperature such as an internal equipment temperature among the operation data of the equipment identified by the monitoring object equipment identifying information becomes the analysis item. Incidentally, if a plurality of analysis items corresponding to the purpose exist as in a case where internal equipment temperatures are acquired at a plurality of positions in the equipment as the operation data, one or a plurality of analysis items among the above-described analysis items can be an candidate or candidates for the analysis item. 
     The specified range decision unit  242  extracts a specified range from the specified range DB  222  in accordance with the analysis item decided by the analysis item decision unit  241  and decides the specified range. This specified range is a range which does not satisfy a warning threshold value, but is close to the warning threshold value and which should be a comment object. 
     The comparison item decision unit  243  extracts a comparison item from the comparison item DB  223  in accordance with the specified range decided by the specified range decision unit  242  and decides the comparison item. The comparison item decided here is data of a different type from that of the analysis item data and is data having a correlation with the analysis item data. For example, if the analysis item is an internal equipment temperature, an ambient temperature (a surrounding air temperature) can be used as the comparison item. Incidentally, for example, if internal equipment temperatures are acquired at a plurality of positions in the equipment, the internal equipment temperatures at different positions will be treated as data of different types. Furthermore, even if the data are acquired at the same position in the same equipment, the data with different conditions will be treated as data of different types. For example, if an internal equipment pressure at a specified position is designated as the analysis item, an internal equipment pressure at a different position in the same equipment may be used as the comparison item. Similarly, if a motor current is designated as the analysis item, a motor current upon maximum load can be used as the comparison item. 
     The comparison data decision unit  244  extracts comparison data from the comparison data DB  225 , which is stored in the operation data information DB  224 , and decides the comparison data. The comparison data decided here is data of the same type as that of the analysis item data and is data acquired under a different environment from that of the analysis item data. For example, the operation data acquired in the past regarding the object equipment can be used as the comparison data. As one example, if internal equipment temperatures in the past three months are used as the analysis item data, internal equipment temperature before the past three months are used as the comparison data. 
     Incidentally, if there is a comparison item decided by the comparison item decision unit  243 , data of the same type as that of the comparison item decision unit  243  regarding the comparison data can be further added to the comparison item. For example, if the analysis item is an internal equipment temperature and the comparison item is an ambient temperature, a combination of the internal equipment temperature and the ambient temperature of three or more months ago is used. 
     The data comparison unit  213  extracts the analysis data from the analysis data  226 , which is stored in the operation data information DB  224 , and compares the analysis data with the comparison item data and the comparison data. This comparison can find a tendency of the analysis data. 
     The data output unit  214  outputs the result of the comparison by the data comparison unit  213  in a form of, for example, comment data to the data visualization unit  215 . Incidentally, if an alarm or failure is registered in the alarm and failure information DB  227 , the data output unit  214  outputs an actual result of the alarm or failure as the comment data to the data visualization unit  215 . Specifically speaking, the data output unit  214  can decide to adopt or reject what to be output as the comment data. 
     The data visualization unit  215  automatically creates a report by processing various kinds of information, including the comment data which is input from the data output unit  214 , into a format as a report and visualizes the created report. This visualization can be implemented by display output or print output. 
       FIG.  5    is a diagram illustrating an example of the visualization by the data visualization unit  215 . The visualized display screen example 300 is configured by including, as illustrated in  FIG.  5   , display areas which respectively display a monitoring object equipment name  301 , an alarm and failure history  302 , a maintenance history  303 , a comment  304 , and an operation data graph  305  of the monitoring object equipment. Incidentally, the configuration of the visualization illustrated in  FIG.  5    is one example and the visualization may be implemented in various configurations. Furthermore, regarding visualization media, if the visualization is performed on a web, the visualization may be implemented in a paper medium or in a file format. Now, an explanation will be provided below about a case of the visualization as the report. 
       FIG.  6    is a diagram illustrating a data format of the equipment identifying information  228 . The equipment identifying information  228  is, as illustrated in  FIG.  6   , a database for recording equipment  30 , a production number  31 , a model  32 , an installation site address  33 , an installation date  34 , and so on. The equipment  30  is identification information for a user of the analysis server  20  to uniquely identify the equipment. The production number  31  is identification information of the equipment which is attached by a manufacturer of the equipment. If the analysis server  20  and all pieces of equipment are located in the same service base, the management of addresses by the installation site address  33  is unnecessary. However, by assuming a case where pieces of equipment are distributed and located at a plurality of addresses, or a case where the analysis server  20  is installed at a different location from that of equipment, the equipment identifying information  228  is provided with an item for the installation site address  33 . 
       FIG.  7    is a diagram illustrating a data format of the analysis item DB  221 . The analysis item DB  221  is a database which records a model  40 , a temperature  41 , a pressure  42 , and an electric current  43 . If the model  40  is “Model A,” one item can be selected as an analysis item for each item of the temperature, the pressure, and the electric current. On the other hand, if the model  40  is “Model B,” there are two items which can be selected as analysis items for each item of the temperature and the pressure and there is one item for the electric current. Accordingly, the internal equipment temperature(s) and the internal equipment pressure(s) are sometimes acquired at a plurality of positions depending on the machine type identified by the model  40  and the analysis items can be selected from the plurality of positions. 
       FIG.  8    is a diagram illustrating a data format of the specified range DB  222 . The specified range DB  222  is a database which records a model  50 , an analysis item  51 , and a specified range  52 . The specified range is a range which does not satisfy a warning threshold value, but is close to the warning threshold value. In other words, the specified range is a range to be included within a normal operation range which does not satisfy a condition for anomaly detection, but for which a comment to call for attention should be reported. If the relevant machine type has the analysis items which can be selected from a plurality of positions as illustrated in  FIG.  8   , the specified range is set individually to each of them. 
       FIG.  9    is a diagram illustrating a data format of the comparison item DB  223 . The comparison item DB  223  is a database which records an analysis item  60  and a comparison item  61 . In  FIG.  9   , if the analysis item is an internal equipment temperature, an ambient temperature is associated with it as a comparison item. Furthermore, if the analysis item is an internal equipment pressure, an internal equipment pressure at another position in the same equipment is associated with it as a comparison item. Under this circumstance, the internal equipment pressure which is associated as the comparison item may be the one at the position which is not selected as the analysis item. Incidentally, if the analysis item is a motor current, a motor current upon maximum load is used as the analysis item as one example. 
       FIG.  10    is a diagram illustrating a data format of the comparison data DB  225  which is stored in the operation data information DB  224 . The comparison data  225  is a database which records an acquisition date and time  70 , a first comparison item  71 , a numerical value  72  of the first comparison item, a second comparison item  73 , and a numerical value  74  of the second comparison item. The first comparison item  71  is data of the same type as that of the analysis item  60  in the comparison item DB  223 . The second comparison item  73  is data of the same type as that of the comparison item  61  in the comparison item DB  223 . 
       FIG.  11    is a diagram illustrating a data format of the analysis data DB  226  which is stored in the operation data information DB  224 . The analysis data DB  226  is a database which records an acquisition date and time  80 , an analysis item  81 , a numerical value  82  of the analysis item, a comparison item  83 , and a numerical value  84  of the comparison item. 
       FIG.  12    is a diagram illustrating a data format of the alarm and failure information DB  227 . The alarm and failure information DB  227  is a database which records an alarm or failure occurrence date and time  90 , a production number  91 , a model  92 , and alarm or failure content  93 . 
     Incidentally, this embodiment 1 is explained about the configuration for managing various kinds of information by using the databases; however, these pieces of information may not necessarily have to be expressed in the data structure of databases, but may be expressed in data structures of lists, tables, etc. or in any other structures. Therefore, the “table(s),” “list(s),” “DB,” etc. may sometimes be simply called “information.” 
       FIG.  13    is a diagram illustrating a processing flow of creating a report. Firstly, the monitoring object equipment identifying information input unit  231  accepts input of monitoring object equipment identifying information (for example, the production number XXX1234) via an input/output device or the like (S 101 ). Equipment  1  as the name of the equipment with the production number linked to XXX1234, “Model A” as the name of the model, “XX State, OO City” as the installation site address, and “Aug. 15, 2015” as the installation date are extracted from the input result of S 101  and the equipment identifying information  228  in  FIG.  6   . 
     Subsequently, the analysis purpose input unit  232  accepts input of the analysis purpose (for example, to examine the tendency regarding the temperature) via the input/output device or the like (S 102 ). For example, “the model is Model A and the temperature is an internal equipment temperature” is extracted from the input results in S 101  and S 102  and the analysis item DB  221  in  FIG.  7   . 
     The monitoring object equipment and the analysis item for creating a report has been decided by the processing described above. 
     The data decision unit  212  judges whether data linked to the monitoring object equipment decided by the operation data information DB  224  exists or not (S 103 ); and if the data does not exist, the processing proceeds to comment decision processing by the data output unit  214  (S 109 ). If the data exists, the data decision unit  212  judges, from the alarm and failure information DB  227 , whether data linked to the decided monitoring object equipment exists or not (S 105 ). 
     If the data exists, the data decision unit  212  proceeds to the comment creation processing (S 107 ) by the data output unit  214 . If the data does not exist, the data decision unit  212  extracts the analysis data linked to the decided monitoring object equipment from the analysis data information DB  226  and judges whether the analysis data exceeds a preset value or not (S 106 ). 
     If the analysis data linked to the decided monitoring object equipment exceeds the preset value according to the analysis data information DB  226 , the data decision unit  212  determines that it is an anomaly, and then proceeds to the comment creation processing (S 107 ) by the data output unit  214 . If the analysis data does not exceed the preset value, the data decision unit  212  proceeds to tendency extraction processing. 
     In the comment creation processing, the data output unit  214  extracts, and creates a list of, actual results of alarms and failures from the alarm/failure content  93  stored in the alarm and failure information DB  227  and outputs them as candidates for the comment data to the comment decision processing (S 107 ). Furthermore, the data output unit  214 : extracts an actual result of occurrence of the case where the analysis data exceeds the preset value; and outputs it as a candidate for the comment data to the comment decision processing (S 107 ). 
     In the tendency extraction processing, the data decision unit  212  extracts an increasing tendency or a decreasing tendency of the first analysis item  81  stored in the analysis data information DB  226  and outputs the extraction result as a candidate for the comment data to the comment decision processing (S 108 ). The details will be explained by using  FIG.  14   . 
     In the comment decision processing (S 109 ), the data output unit  214  selects the comment data from the candidates for the comment data and outputs it to visualization processing. Specifically, if the actual result of the alarm or failure is included in the candidates for the comment data, the data output unit  214  preferentially selects the actual result of the alarm or failure. Furthermore, if the case where the analysis data exceeds the set value has occurred, the data output unit  214  selects the case preferentially next to the alarm/failure. Then, if these pieces of comment data which should be prioritized do not exist or if the number of the pieces of comment data to be prioritized is small, the data output unit  214  selects the tendency extraction result as the comment data. 
     In the visualization processing (S 110 ), the data visualization unit  215  automatically creates a report by processing the various kinds of data, including the comment data which is input from the data output unit  214 , in a report format, visualizes the created e report, and terminates the processing. This visualization can be performed by display output or print output. 
       FIG.  14    is a diagram illustrating a processing flow of extracting the tendency. Once the tendency extraction processing is started, the analysis item decision unit  241  identifies the monitoring object equipment from the monitoring object equipment identifying information (S 801 ). For example, as illustrated in  FIG.  6   , information such as Equipment  1  as the name of the equipment with the production number linked to XXX1234, “Model A” as the name of the model, “XX State, OO City” as the installation site address, and “Aug. 15, 2015” as the installation date is identified as information of the monitoring object equipment by referring to the equipment identifying information  228  on the basis of the production number XXX1234. 
     The analysis item decision unit  241  decides the analysis item from the input analysis purpose and the identified result of the monitoring object equipment (S 802 ). For example, if the input reciting “to examine the tendency regarding the temperature” is accepted as the analysis item, the analysis item decision unit  241  refers to the analysis item DB  221  and decides “the internal equipment temperature of the model ‘Model A’” as the analysis item as illustrated in  FIG.  7   . 
     The specified range decision unit  242  decides the specified range  52  to be from 90° C. to 100° C. as illustrated in  FIG.  8    from the decided analysis item (the internal equipment temperature of the model “Model A”) and the specified range DB  222  (S 803 ). 
     The specified range decision unit  242  judges whether data with the numerical value  82  of the analysis item  81  which falls within this range exists or not, by referring to  FIG.  11    on the basis of the range from 90° C. to 100° C. which is the specified range  52  in  FIG.  8    (S 804 ); and if the data does not exist, the specified range decision unit  242  outputs no tendency as the tendency extraction processing result (S 808 ). 
     On the other hand, if the data exists within the specified range, the comparison item decision unit  243  decides the comparison item  61  from the comparison item data DB  223  illustrated in  FIG.  9    and the analysis item  81  in the analysis data DB  226  illustrated in  FIG.  11    (S 805 ). Under this circumstance, the acquisition date and time  80  is decided as 13:00 on May 13, 2019, the analysis item  81  is decided as the internal equipment temperature, the numerical value  82  is decided as 93° C., and the comparison item  61  is decided as the ambient temperature. 
     The comparison data decision unit  244  decides the comparison data by referring to the comparison data DB  225  on the basis of the value of the analysis item (S 806 ). One example of a method for deciding the comparison data will be explained. Of the data stored in the comparison data DB  226 , the numerical value of the analysis item which is the internal equipment temperature, that is, data of 93° C. in this case is decided as the comparison data (S 806 ). In this case, Aug. 15, 2015 as the acquisition date and time  70 , the internal equipment temperature as the comparison item  71 , 93° C. as the numerical value  72 , the ambient temperature as the comparison item  73 , and 33 degrees as the numerical value  74  are decided as the comparison data. 
     The data comparison unit  213  compares the analysis data decided in S 805  with the comparison data decided in S 805  and the comparison item decided in S 806  (S 807 ) and outputs the comparison result as a tendency extraction processing result (S 808 ). 
     In this case, the acquisition date and time  80  is 13:00 on May 13, 2019, the analysis item  81  is the internal equipment temperature, the numerical value  82  is 93° C., the comparison item  83  is the ambient temperature, and the numerical value  84  is 21 degrees as the analysis data, while the acquisition date and time  70  is Aug. 15, 2015, the comparison item  71  is the internal equipment temperature, the numerical value  72  is 93° C., the comparison item  73  is the ambient temperature, and the numerical value  74  is 33 degrees as the comparison data; and accordingly, when comparing the differences between the internal equipment temperatures and the ambient temperatures, the difference in the analysis data is 72° C. and the difference in the comparison data is 60° C., so that an increasing tendency is output as the result. 
     According to Embodiment 1, it is possible to provide the monitoring apparatus and monitoring system for diagnosing and visualizing the status of the monitoring object equipment for the sake of the condition based maintenance to perform the maintenance in accordance with the status of each equipment. 
     Embodiment 2 
       FIG.  15    is a configuration diagram illustrating a monitoring system according to Embodiment 2. The monitoring system according to Embodiment 2 is configured so that a plurality of service bases  100 - 1  to  100 -M and a monitoring center  110  for monitoring the plurality of service bases are connected via a network  120 . Furthermore, each of the service bases  100 - 1  to  100 -M has a plurality of pieces of equipment such as equipment  10 - 1  to  10 -N. 
     The monitoring center  110  has an analysis server  20  for managing information of the operating status and information of alarms and failures which are acquired from the equipment. An explanation is omitted about the same content as that of Embodiment 1. The difference form Embodiment 1 is that each service base such as the service base  100 - 1  and the analysis server  20  are connected via the network  120 . Consequently, the analysis server  20  can use a plurality of pieces of equipment in different areas for comparison with analysis object equipment. 
     For example, the analysis server  20  according to Embodiment 2 can use data of the same type as that of the analysis item data acquired regarding the equipment of the same machine type as that of the monitoring object equipment. Specifically speaking, if the “internal equipment temperature of the model ‘Model A’” is the analysis item data, an “internal equipment temperature” of equipment which is the “model ‘Model A’” installed at another service base or the like is used as the comparison item data. 
     Furthermore, the analysis server  20  according to Embodiment 2 can also use data of the same type as that of the analysis item data acquired regarding the equipment of the same area as that of the monitoring object equipment. Specifically speaking, if the “internal equipment temperature of the model ‘Model A’” is the analysis item data, an “internal equipment temperature” of equipment installed in the vicinity (for example, in the same district) is used as the comparison item data. 
     According to the aforementioned embodiments of the present invention, the analysis server  20  is configured to include: the data input unit  211  which accepts the equipment identifying information for identifying the monitoring object equipment and the analysis purpose; and the data output unit  214  which outputs the tendency of the analysis item data compared with predetermined comparison item data when the analysis item data corresponding to the analysis purpose among the operation data indicating the operating status of the identified monitoring object equipment is a value within the specified range on the basis of the equipment identifying information and the analysis purpose which have been input. The above-described configuration makes it possible to diagnose and visualize the status of the monitoring object equipment for the sake of the condition based maintenance to perform the maintenance in accordance with the status of each equipment. 
     The specified range herein used is included in the range which does not satisfy a condition for anomaly detection regarding the relevant item data. Specifically speaking, it is possible to diagnose and output what kind of tendency the status of the equipment has, regarding the monitoring object equipment which does not satisfy the condition for the anomaly detection. Therefore, it is possible to report the status of the equipment at an early stage. 
     Furthermore, the analysis server  20  has the data visualization unit  215  which processes the output from the output unit, creates the comment data for reporting the operating status of the monitoring object equipment, and visualizes a report including the comment data. If there is an actual result which satisfied the condition for the anomaly detection during a target period of the report, this data visualization unit  215  creates the comment data to report the actual result of the anomaly detection; and if there is no actual result which satisfied the condition for the anomaly detection during the target period of the report, the data visualization unit  215  creates the comment data to report the tendency of the analysis item data. Therefore, it is possible to automatically create the report by deciding to adopt or reject information in descending order of importance so that the comment will be made in a proper volume. 
     Furthermore, the analysis server  20  can use data which is data of a different type from that of the analysis item data and has a correlation with the analysis item data, as the comparison item data. For example, if the analysis item data is an internal equipment temperature of the monitoring object equipment, a surrounding air temperature is used as the comparison item data. 
     Furthermore, the analysis server  20  can also use data which is data of the same type as that of the analysis item data and is acquired under a different environment from that of the analysis item data, as the comparison item data. For example, data of the same type as that of the analysis item data, which was acquired in the past regarding the monitoring object equipment, can be used as the comparison item data. Furthermore, data of the same type as that of the analysis item data acquired regarding equipment in the same area as that of the monitoring object equipment can be used as the comparison item data. 
     Furthermore, the analysis server  20  can combine different pieces of comparison item data together and compare them with the analysis item data. Therefore, for example, it is possible to automatically create high-level comments as mentioned below: 
     “the difference between the internal equipment temperature of the object equipment and the ambient temperature tends to be larger than that between the internal equipment temperature and the ambient temperature at the time of its installment”; “the internal equipment pressure of the object equipment tends to be higher than an average internal equipment pressure of equipment of the same machine type introduced around the same time”; and
 
“the motor current of the object equipment seems to have a lowering tendency, while other equipment of the same area also shows a similar tendency.”
 
     Incidentally, in the above-described examples, an explanation has been provided by taking an air compressor as an example of the equipment; however, the equipment may be other industrial equipment such as a transformer. 
     Furthermore, the present invention is not limited to the embodiments explained above, and includes various variations and equivalent configurations. For example, the aforementioned embodiments have been described in detail in order to explain the invention in an easily comprehensible manner and are not necessarily limited to those having all the configurations explained above. Moreover, part of the configuration of a certain embodiment may be replaced with the configuration of another embodiment. Furthermore, part of the configuration of another embodiment may be added to the configuration of a certain embodiment. 
     Furthermore, the aforementioned processing by programs may be implemented by hardware by, for example, designing part or whole of such processing by using integrated circuits or the like, or the processing by the hardware and the processing by the programs may be combined together. Information such as programs and tables may be stored in memories, storage devices such as HDDs (Hard Disk Drives) and SSDs (Solid State Drives), or storage media such as IC cards, SD cards, and DVDs. Furthermore, control lines and information lines which are considered to be necessary for the explanation are indicated; however, there may be control lines or information lines other than those indicated. 
     REFERENCE SIGNS LIST 
     
         
           20 : analysis server 
           110 : monitoring center 
           211 : data input unit 
           212 : data decision unit 
           213 : data comparison unit 
           214 : data output unit 
           215 : data visualization unit 
           221 : analysis item DB 
           222 : specified range DB 
           223 : comparison item DB 
           224 : operation data information DB 
           227 : alarm and failure information DB 
           228 : equipment identifying information DB