Patent Publication Number: US-2021178615-A1

Title: Abnormality diagnosis device and abnormality diagnosis method

Description:
TECHNICAL FIELD 
     The present invention relates to an abnormality diagnosis device and an abnormality diagnosis method of diagnosing an abnormality caused in a movable part such as a speed reducer provided in an apparatus such as a robot. 
     BACKGROUND ART 
     Devices are known that detect an abnormality caused in an apparatus, as disclosed in Patent Document 1, for example. Patent Document 1 discloses a device that records an abnormality caused in an apparatus as a target to be diagnosed while associating the abnormality with a date, sound data, true-or-false results of diagnosis, and records of operations (such as the presence or absence of inspections, the presence or absence of problems, and an executed time and place), and notifies a user of a similar case having been caused before regarding sound produced upon the occurrence of an abnormality in the apparatus. 
     CITATION LIST 
     Patent Literature 
     Patent Document 1: Japanese Unexamined Patent Application Publication No. 2005-339142 
     SUMMARY OF INVENTION 
     Technical Problem 
     Patent Document 1, however, cannot recognize a cause-and-effect relationship between the target apparatus to be diagnosed and other apparatuses when an abnormality is caused in the target apparatus. 
     In view of the foregoing problem, the present invention provides an abnormality diagnosis device and an abnormality diagnosis method capable of, when an abnormality is caused in one movable part, predicting and notifying an abnormality of another associated movable part to an operator. 
     Technical Solution 
     An aspect of the present invention includes a maintenance history storage unit configured to store maintenance data on maintenance made for each movable part, and a control unit configured to diagnose an abnormality in each movable part. The control unit, when detecting an abnormality in one movable part in accordance with movable-part data, predicts an abnormality in another movable part caused in association with the abnormality in the one movable part in accordance with the maintenance data, and outputs information on the abnormality predicted in the other movable part. 
     Advantageous Effects 
     The aspect of the present invention, when an abnormality is caused in one movable part, can predict and notify an abnormality of another associated movable part to an operator. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating a configuration of an abnormality diagnosis device according to an embodiment of the present invention, and peripheral apparatuses. 
         FIG. 2  is an explanatory diagram showing an example in which the abnormality diagnosis device shown in  FIG. 1  is implemented by an integrated computer. 
         FIG. 3  is a flowchart showing a process of correlation analysis processing executed by the abnormality diagnosis device according to the present embodiment. 
         FIG. 4  is a flowchart showing a processing process executed by the abnormality diagnosis device according to the present embodiment. 
         FIG. 5  is a graph showing a change in disturbance torque and abnormality level. 
         FIG. 6  is an explanatory diagram showing abnormality detected in each speed reducer and execution of maintenance. 
         FIG. 7A  is an explanatory diagram showing a maintenance history A. 
         FIG. 7B  is an explanatory diagram showing a maintenance history B. 
         FIG. 8  is an explanatory diagram showing a tree image indicating details of abnormality diagnosis. 
         FIG. 9  is an explanatory diagram showing an abnormality diagnosis image displayed on a display. 
         FIG. 10  is an explanatory diagram showing the abnormality diagnosis image displayed on the display, illustrating the maintenance history A with a larger size than the maintenance history B. 
         FIG. 11  is a timing chart showing maintenance (H 1  to Hn) executed in the respective speed reducers and an abnormality detected in the respective speed reducers. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Embodiments of the present invention will be described below with reference to the drawings. 
     Explanations of First Embodiment 
       FIG. 1  is a block diagram illustrating a configuration of an abnormality diagnosis device according to one embodiment of the present invention, and peripheral apparatuses. As illustrated in  FIG. 1 , the abnormality diagnosis device  102  according to the present embodiment is connected to a robot  101  (an apparatus) and a user interface  103  (indicated by reference sign “UI” in  FIG. 1 ). The abnormality diagnosis device  102  diagnoses an abnormality of the robot  101 , and outputs data regarding the diagnosis results to a display  62  (a display unit) provided in the user interface  103  so as to display the diagnosis results on the display  62 . As used herein, the phrase “diagnosing an abnormality” encompasses a concept of not only determining an abnormality currently caused but also predicting an abnormality which can be caused in the future. 
     The robot  101  is a teaching-playback multi-axis robot, for example. The term “teaching-playback” is indicative of a function of actually operating a robot to make action by an operator using a teaching pendant belonging to the robot, and storing and reproducing the action so as to cause the robot to perform the action. While the present embodiment is illustrated with the teaching-playback robot, the present invention is not limited to this case. 
     As illustrated in  FIG. 1 , the robot  101  includes a speed reducer  14  (a movable part), an action control unit  15 , a sensor  13 , a disturbance torque calculation unit  12 , and a communication unit  11 . While the robot  101  includes a plurality of speed reducers  14 ,  FIG. 1  illustrates only one speed reducer  14 . 
     Each speed reducer  14  includes a servo motor (simply referred to below as a “motor”) for operating each joint shaft of a robot arm, and is operated in accordance with the control by the action control unit  15 . The operation of the respective speed reducers  14  causes a welding electrode (a welding part) mounted at the tip of the robot arm, for example, to come into contact with a necessary part of a target object to be processed (for example, a metallic blank material) so as to execute the welding operation. The robot  101  can further execute various kinds of operations such as pressing, coating, resin molding, and assembling of a target object, in addition to the welding operation. 
     The sensor  13 , which is mounted in the robot  101 , includes a pulse generator and an encoder, for example, and detects various kinds of physical amounts such as a position and an angle of the robot arm operated by the respective speed reducers  14 , a rotation angle, a rotation speed, power consumption, and a current of the motor provided at respective speed reducers  14 , and a rotation angle of respective speed reducers  14 . The sensor  13  also detects a value of torque caused in the motor of the respective speed reducers  14 . The sensor data detected by the sensor  13  is sent to the abnormality diagnosis device  102  through the communication unit  11 . 
     The action control unit  15  causes the respective speed reducers  14  to operate according to an action program set by the teaching described above, and controls the respective robot arms and the joint shafts mounted on the robot  101  to perform necessary actions. The action control unit  15  outputs work data acquired when the robot  101  is operated to the communication unit  11 . The work data includes various kinds of information regarding the work of the robot  101 . The specific explanations are made below. 
     The disturbance torque calculation unit  12  calculates disturbance torque caused in the motor of the respective speed reducers  14 . The term “disturbance torque” refers to a difference between a torque command value when controlling each motor and a torque detection value detected by the sensor  13 . The difference between the torque command value and the torque detection value is substantially constant when the motor is in a normal state and the speed reducer  14  operates stably, and the disturbance torque thus shows a stable numerical value. When an abnormality is caused in the speed reducer  14 , the operation of the corresponding speed reducer  14  is not stable, and a great change occurs in the disturbance torque. The disturbance torque is an example of movable-part data regarding the state of the movable part (the speed reducer  14 ). 
     The communication unit  11  sends the work data of the robot  101 , the disturbance torque calculated by the disturbance torque calculation unit  12 , and various kinds of sensor data detected by the sensor  13  to the abnormality diagnosis device  102 . 
     The respective functions that the robot  101  has can be implemented in single or plural processing circuits. The respective processing circuits include a programmed processing device, such as a processing device including an electric circuit. The processing device includes an application-specific integrated circuit (ASIC) configured to execute the functions that the robot  101  has, and conventional circuit components. 
     The user interface  103  includes the display  62  for displaying the various kinds of information, and a display control unit  61  for controlling a displaying state of the various kinds of information sent from the abnormality diagnosis device  102 . The user interface  103  further includes an input unit  63  on which the operator executes various kinds of operations. When the operator inputs maintenance data indicating that maintenance has been made for the robot  101  via the input unit  63 , the user interface  103  writes the input maintenance data to a maintenance history DB  32  (a maintenance history storage unit) described below. The user interface  103  may be a tablet terminal, for example. 
     Next, the configuration of the abnormality diagnosis device  102  is described below. The abnormality diagnosis device  102  includes a control unit  51  and various kinds of databases (DBs). The control unit  51  includes a communication unit  21 , an abnormality level determination unit  22 , an abnormality prediction unit  23 , a notification content setting unit  24 , and a correlation analysis unit  25 . The databases include a sensor DB  31 , the maintenance history DB  32  (the maintenance history storage unit), an abnormality prediction DB  33 , a correlation storage DB  34  (a correlation storage unit), and a work history DB  35 . 
     The sensor DB  31  stores the sensor data such as the position and the angle of the respective robot arms and the rotation angle and the rotation speed of the respective motors detected by the sensor  13 . The sensor DB  31  also stores the disturbance torque calculated by the disturbance torque calculation unit  12  (the data regarding the state of the apparatus). 
     The work history DB  35  stores the work data of the robot  101 . The work data includes various kinds of data regarding the work of the robot  101 , such as a work date, a work-started time, a work-stopped time, a continuous work time, and a continuous suspension time. The work data also includes a drive mode of the speed reducers  14 . The drive mode includes a regular drive mode, a maintenance mode, and a stop mode. 
     The maintenance history DB  32  stores the maintenance data on maintenance made for the robot  101  acquired when an abnormality is caused or the occurrence of an abnormality is predicted in the respective speed reducers  14 . The maintenance data can be input by the operator through the input unit  63  of the user interface  103 . Alternatively, the maintenance may be determined to be made when the robot  101  is operated in the maintenance mode described above, so as to automatically create and store the maintenance data. The maintenance data includes an ID number of the speed reducer  14  having been maintained, an ID number of the motor mounted on the corresponding speed reducer  14 , the date and time of the maintenance executed, and the contents of the maintenance (such as a replacement, a repair, and a change of grease). 
     The correlation storage DB  34  stores a correlation drawn by the correlation analysis unit  25  (described in detail below). The correlation storage DB  34  also stores a correspondence relation between one speed reducer and other speed reducers operated in association with the one speed reducer provided in the robot  101 . For example, in a case in which, when the robot  101  includes the six speed reducers  14  (herein illustrated with the speed reducers  14   a ,  14   b ,  14   c ,  14   d ,  14   e , and  14   f ), the speed reducers  14   a  and  14   b  are operated in association with each other, and the speed reducers  14   d ,  14   e , and  14   f  are operated in association with each other, the correlation storage DB  34  stores the corresponding relation between the respective speed reducers  14  in each set. The phrase “the speed reducers  14   a  and  14   b  are operated in association with each other” encompasses a concept in which the operation of the speed reducer  14   a  has some influence on the operation of the speed reducer  14   b . Hereinafter, the speed reducers  14 , when specified independently, are indicated with a suffix, such as the “speed reducer  14   a ” and the “speed reducer  14   b ”, and are each referred to as the “speed reducer  14 ” when not particularly specified. 
     The abnormality prediction DB  33  stores abnormality prediction data predicted by the abnormality prediction unit  23  (described in detail below). For example, when detecting an abnormality of the disturbance torque in one speed reducer  14   a  and thus predicting that the speed reducer  14   b  operated in association with the speed reducer  14   a  has an abnormality, the abnormality prediction DB  33  stores the predicted data as the abnormality prediction data. 
     The communication unit  21  communicates with the communication unit  11  included in the robot  101 . The communication unit  21  receives the work data of the robot  101  sent from the robot  101 , and outputs the work data to the work history DB  35 . The communication unit  21  also receives the disturbance torque and the sensor data sent from the robot  101 , and outputs the received data to the sensor DB  31 . 
     The correlation analysis unit  25  executes the correlation analysis of the abnormality caused in the respective speed reducers  14  according to the maintenance data of the respective speed reducers  14  stored in the maintenance history DB  32 .  FIG. 6  illustrates an example of the maintenance history of the respective speed reducers  14 , in which the axis of abscissas indicates the time with a span from one to several weeks. The sign “circle” in  FIG. 6  indicates an abnormality caused in the respective speed reducers  14 , the sign “triangle” indicates the execution of maintenance of changing grease, and the sign “square” indicates the execution of maintenance of replacing the speed reducers  14 . As illustrated in  FIG. 6 , the replacement of the speed reducers  14  is executed with high frequency within several days from the point when an abnormality is caused and the grease is subsequently changed in the respective speed reducers  14   a ,  14   b , and  14   c . The change of grease and the subsequent replacement of the speed reducers  14  are thus determined to have a correlation. The correlation obtained by the correlation analysis is stored in the correlation storage DB  34 . 
     The abnormality level determination unit  22  acquires the past disturbance torque applied to the motor mounted on the respective speed reducers  14  from the sensor DB  31 , and calculates an abnormality level indicating a degree of abnormality of the disturbance torque acquired. A method of calculating the abnormality level is described below. The abnormality level a(x′), where x′ is the disturbance torque, is given by the following formula (1): 
         a ( x ′)={( x′−m ) 2 }/2· s   2   (1)
 
     where m is a sample average of the disturbance torque, and s is a standard deviation of the disturbance torque. 
     The disturbance torque is determined to be abnormal when the abnormality level a(x′) exceeds a predetermined reference value.  FIG. 5( a )  illustrates a waveform indicating the disturbance torque applied to the speed reducer  14   a . The abnormality level is calculated according to the above formula (1) on the basis of the disturbance torque shown in  FIG. 5( a ) . The calculation leads to the abnormality level as shown in  FIG. 5( b ) . For example, the disturbance torque is determined to be abnormal since the abnormality level exceeds the reference value (1.0) at a point indicated by reference sign p 1  in  FIG. 5( b ) . 
     Instead of the above method, the abnormality level may be calculated by use of kernel density estimation or density ratio estimation. Still another method of determining the abnormality level is to calculate a difference between the disturbance torque and a predetermined value, and further calculate a rate of change in the difference with the passage of time. The corresponding disturbance torque can be determined to be abnormal when the rate of change calculated exceeds a predetermined threshold. The predetermined value may be an average of the disturbance torque acquired in the same month one year ago. 
     The abnormality prediction unit  23  determines or predicts an abnormality caused in the respective speed reducers  14  in accordance with the abnormality level calculated for each speed reducer  14 . When the abnormality level of one speed reducer  14   a  is determined to be high according to the abnormality level calculated by the abnormality level determination unit  22  described above, the speed reducer  14   a  is determined to have an abnormality. The abnormality prediction unit  23  also acquires a list of the speed reducers  14  operated in association with the speed reducer  14   a  while referring to the correlation storage DB  34 . In the present embodiment, the speed reducer  14   b  is presumed to be indicated in the list acquired. The plural speed reducers  14  may be operated in association with the speed reducer  14   a , but the present embodiment is illustrated with the case of only the speed reducer  14   b  for illustration purposes. 
     The abnormality prediction unit  23  detects an abnormality caused in each of the speed reducers  14   a  and  14   b  according to the maintenance data (such as the maintenance time and the contents of the maintenance) of the speed reducers  14   a  and  14   b  stored in the maintenance history DB  32  and the correlation stored in the correlation storage DB  34 . 
     For example, as shown in  FIG. 6 , when the grease is changed after an abnormality is detected in one speed reducer  14 , the maintenance of replacing the speed reducers  14  is subsequently made with high frequency. The change of the grease and the subsequent replacement of the speed reducers  14  are thus determined to have a correlation. When the point of detection of the abnormality in the speed reducer  14   a  is after the execution of the change of the grease in the speed reducer  14   b , the probability is determined to be high that the abnormality is caused in the speed reducer  14   b  that needs to be replaced to another speed reducer  14 , so as to predict that the speed reducer  14   b  has an abnormality. Namely, the abnormality prediction unit  23  refers to the correlation stored in the correlation storage DB  34  when the abnormality is detected in the speed reducer  14   a  (one movable part), and predicts the abnormality in the speed reducer  14   b  (the other movable part) having the correlation with the abnormality detected in the speed reducer  14   a.    
     The more specific explanations are made below with reference to  FIG. 7A  and  FIG. 7B .  FIG. 7A  is a diagram illustrating the abnormality having been detected in the respective speed reducers  14   a  and  14   b  and the maintenance having been made before, in which the axis of abscissas indicates the time with a span from one to several weeks. This is referred to herein as a “maintenance history A”.  FIG. 7B  is a diagram illustrating the abnormality having been detected in the respective speed reducers  14   a  and  14   b  and the maintenance having been made before, in which the axis of abscissas indicates the time with a span from one to several years. This is referred to herein as a “maintenance history B”. The sign “star” in  FIG. 7A  indicates an abnormality detected this time, and the sign “double circle” in  FIG. 7B  indicates no detection of abnormality, namely, indicates that the respective speed reducers are in a normal state. In addition, the sign “circle” indicates the occurrence of abnormality, the sign “triangle” indicates the change of grease, and the sign “square” indicates the replacement of the speed reducers  14 . 
     As illustrated in the maintenance history A in  FIG. 7A , the abnormality is caused in the speed reducer  14   b  at the time t 1 , and the change of grease is subsequently executed at the time t 2 . The detection of abnormality this time in the speed reducer  14   a  is made at the time t 3 . 
     As illustrated in the maintenance history B in  FIG. 7B , the replacement of the speed reducer  14   a  to another speed reducer  14  is made at the time t 1   l , and the detection of abnormality this time is subsequently made at the time t 12  (corresponding to the time t 3  in  FIG. 7A ) within one year from the replacement. 
     The abnormality prediction unit  23  determines that the probability of occurrence of the abnormality in the speed reducer  14   a  is low, since the abnormality detected this time in the speed reducer  14   a  (at the time t 3  in  FIG. 7A  and the time t 12  in  FIG. 7B ) is within one year from the previous replacement of the speed reducer  14   a.    
     The change of grease is made in the speed reducer  14   b  at the time t 2  in  FIG. 7A . According to the correlation stored in the correlation storage DB  34 , the probability that the speed reducer  14   b  has an abnormality is high. In particular, with reference to the maintenance history A illustrated in  FIG. 7A , the abnormality has been detected in the speed reducer  14   b  at the time t 1 , and the maintenance of changing grease has been made at the time t 2 , so that the probability is determined to be high that any abnormality requiring the replacement of the speed reducers  14  is caused in the speed reducer  14   b  at the time t 3 . Namely, the probability is determined to be high that the speed reducer  14   a  has an abnormality derived from the occurrence of the abnormality in the speed reducer  14   b , since the speed reducer  14   a  is operated in association with the speed reducer  14   b.    
     The abnormality prediction unit  23  thus determines that the probability is high that the speed reducer  14   b  operated in association with the speed reducer  14   a  has an abnormality, even though the abnormality has been detected in the speed reducer  14   a , and predicts the occurrence of the abnormality in the speed reducer  14   b  in addition to the speed reducer  14   a . The abnormality prediction unit  23 , when detecting the abnormality in the speed reducer  14   a , refers to the maintenance data regarding the abnormality in at least one (the speed reducer  14   b ) of the other speed reducers  14 , so as to determine the information on the abnormality of the speed reducer  14   b.    
     The notification content setting unit  24  sets the contents to be provided to the user through the indication on the display  62  of the user interface  103 . The notification content setting unit  24  generates a tree image  73  indicating, with a tree structure, the abnormality diagnosis results of the speed reducers  14  determined to have an abnormality by the abnormality prediction unit  23 . 
       FIG. 8  is an explanatory view showing an example of the tree image  73 . For example, when an abnormality is detected in the speed reducer  14   a , an image indicating “Abnormality diagnosis tree” is created as shown in the block q 1  in  FIG. 8 . As shown in the block q 2 , an image indicating “Abnormality level 2.1&gt;Reference value 1.0” is created in which the numerical value of the abnormality level “2.1” calculated this time is greater than the reference value “1.0”. In addition, an image of sign K 1  is created for easy recognition of the correspondence with the waveform of the abnormality level, as described below. The term “sign” as used herein encompasses a concept including characters, a predetermined mark, and an icon. 
     As shown in the block q 3 , an image indicating “Reference history B” and “Speed reducer  14   a  replaced within one year” is created. As shown in the block q 4 , an image indicating “Reference history A” and “Speed reducer  14   b : requiring attention” is created. 
     In addition, an image indicating the contents of the maintenance to be executed for this abnormality. The abnormality in this case is presumed to be caused in the speed reducer  14   a  or the speed reducer  14   b , and an image indicating the execution of the maintenance, “Need to measure the density of iron powder in the grease for the speed reducers  14   a  and  14   b ”, is creased as shown in the block q 5 . The tree image  73  includes maintenance commands for the speed reducer  14   a  and for the speed reducer  14   b  predicted to have an abnormality, and is associated with the relation between the occurrence of the abnormality and the maintenance commands. 
     The blocks q 1  to a 5  showing the process from the occurrence of the abnormality to the execution of the maintenance are indicated with the display frames surrounded by the thick lines, while the other blocks q 6 , q 7 , and q 8  are indicated with the display frames surrounded by the thin lines. These indications can allow the operator to systematically recognize the details until the prediction of the abnormality in the speed reducer  14   b  made by the abnormality prediction unit  23  upon the detection of the abnormality in the speed reducer  14   a . The set of the blocks q 1  to q 5  and the set of the blocks q 6  to q 8  may be indicated in different colors or by shading so as to provide the emphasized indications. 
     The notification content setting unit  24  thus creates the tree image  73  (refer to  FIG. 9 ) with the tree structure indicating the information on the speed reducer  14   a  and the information on the speed reducer  14   b  (refer to the blocks q 4 , q 6  in  FIG. 8  and  FIG. 9 ) when the abnormality is detected in the speed reducer  14   a . The notification content setting unit  24  creates the tree image  73  indicating the information on the speed reducer  14   b  while varying the displaying pattern (such as the line thickness) depending on the contents of prediction of an abnormality in the speed reducer  14   b.    
     The notification content setting unit  24  creates, in addition to the tree image  73 , an abnormality level display image  71  showing the waveform of the disturbance torque and the waveform of the abnormality level as illustrated in  FIG. 5 , and a maintenance history image  72  as shown in  FIG. 7A  and  FIG. 7B . The maintenance history image  72  includes an image indicating the maintenance history A (a first maintenance history image) and an image indicating the maintenance history B (a second maintenance history image) having a different scale in the time axis from the maintenance history A. The notification content setting unit  24  creates an abnormality diagnosis image  70  as shown in  FIG. 9  by combining the respective images  71 ,  72 , and  73  created. The blocks q 5 , q 7 , and q 8  shown in  FIG. 9  indicate the same operation commands as the blocks q 5 , q 7 , and q 8  shown in  FIG. 8 . 
     The abnormality diagnosis device  102  may be implemented by a computer including a central processing unit (CPU)  41 , a memory  42 , and the various databases (the sensor DB  31 , the maintenance history DB  32 , the abnormality prediction DB  33 , the correlation storage DB  34 , and the work history DB  35 ), as illustrated in  FIG. 2 . A computer program (an abnormality diagnosis program) is installed on the computer and executed so as to function as the abnormality diagnosis device  102 . The CPU  41  thus functions as a plurality of information processing circuits included in the abnormality diagnosis device  102 , namely, functions as the communication unit  21 , the abnormality determination unit  22 , the abnormality prediction unit  23 , the notification content setting unit  24 , and the correlation analysis unit  25 . 
     The respective functions included in the abnormality diagnosis device  102  described above can be implemented in single or plural processing circuits. The respective processing circuits include a programmed processing device, such as a processing device including an electric circuit. The processing device includes an application-specific integrated circuit (ASIC) configured to execute the functions included in the abnormality diagnosis device  102 , and conventional circuit components. 
     Explanations of Operation of First Embodiment 
     Next, the functions of the abnormality diagnosis device  102  according to the first embodiment are described below with reference to the flowcharts shown in  FIG. 3  and  FIG. 4 . The flowchart shown in  FIG. 3  illustrates a process of the correlation analysis processing executed by the correlation analysis unit  25 . 
     First, in step S 11 , the correlation analysis unit  25  acquires the maintenance data on the maintenance executed for the respective speed reducers  14  from the maintenance history DB  32 . 
     In step S 12 , the correlation analysis unit  25  executes the correlation analysis in accordance with the maintenance data on the maintenance executed for the respective speed reducers  14 . For example, as shown in  FIG. 6 , when an abnormality is detected in one speed reducer  14  (indicated by the sign “circle” in  FIG. 6 ), the maintenance operations are executed with high frequency such that the grease is changed (indicated by the sign “triangle” in  FIG. 6 ), and the replacement of the speed reducers is subsequently made (indicated by the sign “square” in  FIG. 6 ). The correlation analysis unit  25  thus determines that the change of grease and the subsequent replacement of the speed reducers have a correlation. 
     In step S 13 , the correlation analysis unit  25  stores the correlation obtained by the correlation analysis in the correlation storage DB  34 , and ends the process. 
       FIG. 4  is a flowchart showing a process of the abnormality diagnosis processing. First, in step S 31 , the abnormality determination unit  22  acquires the disturbance torque of one speed reducer (herein illustrated with the speed reducer  14   a ) from the sensor DB  31 . The time-series data of the disturbance torque is thus obtained as illustrated in  FIG. 5( a ) , for example. 
     In step S 32 , the abnormality determination unit  22  calculates the abnormality level indicating a degree of abnormality of the speed reducer  14   a  according to the formula (1) described above. The time-series data of the abnormality level is thus obtained as illustrated in  FIG. 5( b ) , for example. 
     In step S 33 , the abnormality determination unit  22  determines whether an abnormality is caused in the speed reducer  14   a  in accordance with the abnormality level calculated by the processing in step S 32 . For example, when the abnormality level calculated exceeds the reference value of the abnormality level set to 1.0, the abnormality determination unit  22  determines that the speed reducer  14   a  has an abnormality. In the example shown in  FIG. 5( b ) , the abnormality is determined to be caused since the abnormality level exceeds the reference value at the point indicated by the reference sign p 1 . 
     When the speed reducer  14   a  does not have any abnormality (NO in step S 33 ), the process ends. The notification content setting unit  24  does not cause the information regarding the abnormality to be displayed when no speed reducers  14  have abnormality. The notification content setting unit  24  displays the information regarding the abnormality on the display  62  only when at least one of the speed reducers  14  is diagnosed to have an abnormality. 
     In step S 34 , the notification content setting unit  24  generates the display data on the disturbance torque and the abnormality level to be displayed on the display  62  of the user interface  103 . The abnormality level display image  71  as the display data is thus generated as illustrated in  FIG. 9 . 
     In step S 35 , the abnormality prediction unit  23  obtains the list of the speed reducers  14  operated in association with the speed reducer  14   a  in accordance with the information stored in the correlation storage DB  24 . The speed reducer  14   b , for example, is then specified as the speed reducer  14  operated in association with the speed reducer  14   a.    
     In step S 36 , the abnormality prediction unit  23  acquires the maintenance data on the speed reducers  14   a  and  14   b  from the maintenance history DB  32 . 
     In step S 37 , the notification content setting unit  24  generates the display data on the maintenance history A (refer to  FIG. 7A ) and the maintenance history B (refer to  FIG. 7B ) to be displayed on the display  62 . The maintenance history image  72  as the display data is thus generated as illustrated in  FIG. 9 . 
     In step S 38 , the abnormality prediction unit  23  refers to the maintenance history B. In step S 39 , the abnormality prediction unit  23  determines whether the abnormality detected in the speed reducer  14   a  is contradictory to the maintenance data in the maintenance history B. The process proceeds to step S 40  when the abnormality is determined to be contradictory to the maintenance data (YES in step S 39 ). The process proceeds to step S 43  when abnormality is determined not to be contradictory to the maintenance data (NO in step S 39 ). 
     According to the maintenance history B illustrated in  FIG. 7B , the probability of occurrence of the abnormality in the speed reducer  14   a  this time is low, since the replacement of the speed reducer  14   a  has been made at the time t 11 , and the abnormality detected this time at the time t 12  is within one year from the time t 11 . The abnormality detected in the speed reducer  14   a  is thus determined to be contradictory to the maintenance data in the maintenance history B (YES in step S 39 ). 
     In step S 40 , the abnormality prediction unit  23  refers to the maintenance history A, and determines whether the maintenance data indicating the occurrence of abnormality in the speed reducer  14   b  is present in step S 41 . The process proceeds to step S 42  when the maintenance data indicating the occurrence of the abnormality is present (YES in step S 41 ), or the process proceeds to step S 42  when no data is present (NO in step S 41 ). 
       FIG. 7A  illustrates the case in which the abnormality is detected in the speed reducer  14   b  at the time t 1 , and the change of grease is made at the time t 2 . The probability is thus high that any abnormality requiring the replacement to another speed reducer  14  is caused in the speed reducer  14   b  subsequently at the time t 3 . The maintenance data indicating the occurrence of the abnormality is then determined to be present (YES in step S 41 ). 
     In step S 42 , the abnormality prediction unit  23  determines to instruct the measurement of the density of iron powder in the speed reducers  14   a  and  14   b.    
     In step S 43 , the abnormality prediction unit  23  determines to instruct the measurement of the density of iron powder in the speed reducer  14   a.    
     In step S 44 , the notification content setting unit  24  generates the display data of the tree image  73  as illustrated in  FIG. 8 . The tree image  73  is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer  14   a . The notification content setting unit  24  further generates the display data of the abnormality level display image  71  indicating the disturbance torque and the abnormality level as shown in  FIG. 5  and the display data of the maintenance history image  72  as shown in  FIG. 7A  and  FIG. 7B  so as to generate the display data of the abnormality diagnosis image  70  as shown in  FIG. 9 . 
     As shown in  FIG. 9 , the abnormality level display image  71  displayed includes the image of sign K 1  (an image of a clip) and the image indicating “Abnormality level 2.1&gt;Reference value 1.0”. The same images as the sign K 1  and the letters are indicated in the block q 2  of the tree image  73 . The same signs as those indicated in the abnormality level display image  71  are attached to the corresponding information included in the tree image  73  so as to generate the abnormality diagnosis image  70  including the tree image  73  and the abnormality level display image  71 . The abnormality diagnosis image  70  is thus displayed with the configuration easy to recognize the correspondence relation between the tree image  73  and the abnormality level display image  71 . 
     The maintenance history image  72  displayed includes the image of sign K 2  and the image indicating “Speed reducer  14   b : requiring attention”. The same images as the sign K 2  and the letters are indicated in the block q 4  of the tree image  73 . The same signs as those indicated in the maintenance history image  72  are attached to the corresponding information included in the tree image  73  so as to generate the abnormality diagnosis image  70  including the tree image  73  and the maintenance history image  72 . The abnormality diagnosis image  70  generated is thus displayed with the configuration easy to recognize the correspondence relation between the tree image  73  and the maintenance history image  72 . 
     In step S 45 , the notification content setting unit  24  outputs the display data of the abnormality diagnosis image  70  illustrated in  FIG. 9  to the user interface  103 . The process thus ends. 
     The display control unit  61  of the user interface  103  receives the display data of the abnormality diagnosis image  70 , and displays the abnormality diagnosis image  70  as illustrated in  FIG. 9  on the display  62 . The operator, when seeing the image displayed on the display  62 , can recognize that the abnormality has been detected in the speed reducer  14   a  in which the disturbance torque greatly fluctuates, and the probability is thus high that the speed reducer  14   b  operated in association with the speed reducer  14   a  has an abnormality. 
     As described above, the abnormality diagnosis device  102  according to the first embodiment can achieve the following effects: 
     (1) 
     When an abnormality is detected in the speed reducer  14   a  (one movable part) according to the disturbance torque of the speed reducer  14   a  (the data regarding the state of the one movable part), the abnormality diagnosis device  102  predicts an abnormality of the speed reducer  14   b  (another movable part) operated in association with the speed reducer  14   a , and displays the information regarding the abnormality on the display  62 . The abnormality diagnosis device  102  can notify the operator of not only the abnormality detected in the speed reducer  14   a  but also the abnormality in the speed reducer  14   b  determined to have a probability of occurrence in association with the abnormality detected in the speed reducer  14   a , so as to enable the wide abnormality analysis for the speed reducers  14 . 
     (2) 
     The abnormality prediction unit  23  determines the information regarding the abnormality in the speed reducers  14  in accordance with the time of the maintenance having been made for the respective speed reducers  14  and the contents of the maintenance such as the change of grease or the replacement of the speed reducers  14  obtained from the maintenance data stored in the maintenance history DB  32 . This enables the abnormality diagnosis of the speed reducers  14  with a higher accuracy. 
     (3) 
     When the abnormality is detected in the speed reducer  14   a , the abnormality prediction unit  23  refers to the maintenance data on the speed reducer  14   b  operated in association with the speed reducer  14   a , and determines the information regarding the abnormality of the speed reducer  14   b . This enables the abnormality diagnosis of the speed reducers  14  with a high accuracy. 
     (4) 
     The correlation analysis unit  25  executes the correlation analysis in accordance with the abnormality caused in the respective speed reducers  14  and the contents of the maintenance to be made for the respective speed reducers  14 , and stores the correlation between the respective speed reducers  14  in the correlation storage DB  34 . When the abnormality is detected in the speed reducer  14   a , the correlation analysis unit  25  predicts the occurrence of the abnormality in the speed reducer  14   b  having a correlation with the abnormality caused in the speed reducer  14  in accordance with the correlation stored in the correlation storage DB  34 . This enables the abnormality diagnosis with a high accuracy according to the correlation between the abnormality caused in the respective speed reducers  14  and the maintenance made for the respective speed reducers  14 . 
     (5) 
     When the abnormality is detected in the speed reducer  14   a , the display data of the tree image  73  as illustrated in  FIG. 9  is generated. The tree image  73  emphasizes the indication of the process from the occurrence of the abnormality to the execution of the maintenance. In particular, the frames of the blocks q 1  to q 5  illustrated in  FIG. 9  are surrounded by the thick lines. This indication can allow the operator, when seeing the tree image  73  of the abnormality diagnosis, to systematically recognize the details from the occurrence of the abnormality to the execution of the maintenance. This enables the display of the abnormality diagnosis image  70  with the configuration allowing the operator to easily recognize who does not have technical knowledge about the robot  101 . The frames may be indicated by shading or in display colors for emphasis. 
     (6) 
     As illustrated in  FIG. 9 , the sign K 2  shown in the maintenance history image  72  is the same as shown in the corresponding part (the block q 4 ) in the abnormality diagnosis tree. The operator thus can recognize the tree image  73  in the state of being associated with the contents which are the basis thereof with the same sign. This can provide the indication to the operator with the configuration easy to understand. 
     (7) 
     As illustrated in  FIG. 9 , the sign K 1  shown in the abnormality level display image  71  indicating the disturbance torque and the abnormality level is the same sign as shown in the corresponding part (the block q 2 ) in the abnormality diagnosis tree. The operator thus can recognize the tree image  73  in the state of being associated with the contents which are the basis thereof with the same sign. This can provide the indication to the operator with the configuration easier to understand. 
     (8) 
     As illustrated in  FIG. 9 , the tree image  73  indicates the abnormality caused in the speed reducer  14   a  in the state of being associated with the contents of the maintenance to be made for the speed reducer  14   b  predicted to have an abnormality. This can notify the operator of the contents of the maintenance (the block q 5 , q 7 , or q 8 ) to be executed for the abnormality caused in the speed reducer  14   a  with the configuration easy to recognize. 
     (9) 
     The notification content setting unit  24  outputs the display command for displaying the diagnosis image as illustrated in  FIG. 9  only when an abnormality is detected in at least one speed reducer  14 . This can eliminate unnecessary indication. In addition, the abnormality diagnosis image  70  is not displayed when no abnormality is caused, so as to avoid a problem of making an error of recognition of whether an abnormality is caused. 
     Explanations of Modified Example of First Embodiment 
     A modified example of the above first embodiment is described below. In the modified example, either the maintenance history A or the maintenance history B, which is the basis for predicting the abnormality in the speed reducer  14   b , is enlarged or emphasized to be displayed as the maintenance history image  72 . While the two maintenance histories, the maintenance history A and the maintenance history B, having different scales in the time axis are displayed, either one of the maintenance histories which is the basis for predicting the occurrence of the abnormality is enlarged or emphasized. For example, as illustrated in  FIG. 10 , the maintenance history A is displayed with a larger size than the maintenance history B. This can notify the operator of the basis resulting in the determination that the speed reducer  14   b  has an abnormality with the configuration easier to recognize. 
     Explanations of Second Embodiment 
     A second embodiment of the present invention is described below. A device configuration of an abnormality diagnosis device according to the second embodiment is the same as that described above with reference to  FIG. 1 . The second embodiment differs from the first embodiment described above in causing the correlation analysis unit  25  shown in  FIG. 1  to execute machine learning to create a leaning model according to the data on the abnormality having been detected in the respective speed reducers  14  and the history of the maintenance having been made. In particular, the correlation analysis unit  25  executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers  14  at least within a part of periods stored in the maintenance history DB  32 . The correlation analysis unit  25  also detects an abnormality of the respective speed reducers  14  in accordance with the results of the machine learning. 
     The machine learning extracts the rules from the past abnormality data and maintenance data included in the maintenance history so as to create the learning model. A method of the machine learning may be known “supervised learning”, for example. 
     The “supervised learning” acquires a large amount of the data on the abnormality having been detected and the data on the maintenance having been made so as to create the learning model according to the combination of these data, the order of occurrence, and intervals of occurrence.  FIG. 11  is a timing chart showing the maintenance (H 1  to Hn) executed for the respective speed reducers  14  and the abnormality detected in the respective speed reducers  14 . As illustrated in  FIG. 11 , the supervised learning creates the learning model by the machine learning in accordance with the relationship between the contents (refer to reference numeral  201 ) of each type of maintenance (H 1  to Hn) having been made for the respective speed reducers  14  at the past point before the time t 21  (for example, one month before) and the data on the abnormality caused in the respective speed reducers  14 . The leaning model created is stored in the correlation storage DB  34 . 
     Another method, “unsupervised learning”, other than the supervised learning may be used to create the learning model. Another method, “deep learning”, other than the machine learning may be used to create the learning model. 
     The abnormality prediction unit  23  refers to the learning model stored in the correlation storage DB 34  when an abnormality is detected in one speed reducer  14 , and extracts other speed reducers  14  having a high probability of occurrence of an abnormality. 
     The processing process in the abnormality diagnosis device according to the second embodiment is the same as that in the first embodiment excluding the use of the learning model, and overlapping explanations are not repeated below. 
     As described above, the abnormality diagnosis device according to the second embodiment creates the learning model by use of the machine learning according to the data on the maintenance having been executed before for the respective speed reducers  14 . When an abnormality is caused in the speed reducer  14   a , the abnormality diagnosis device refers to the learning model described above so as to predict an abnormality caused in the speed reducer  14   b . The abnormality diagnosis device thus can execute the abnormality diagnosis of the speed reducers  14  with a higher accuracy. 
     The apparatus as a target for the abnormality diagnosis is not limited to the robot  101 . For example, an engine of a vehicle instead of the motor or a transmission instead of the speed reducer  14  may be applicable as a target. Any apparatus including a rotating mechanism and a transmitting mechanism thereof can be a target for the abnormality diagnosis, such as a rotating mechanism of a moving object, a moving object such as playground equipment in an amusement park, and a work machine such as a three-dimensional printer. Any other types of apparatus may also be a target for the abnormality diagnosis. 
     The abnormality diagnosis device may be installed in a remote place to send/receive necessary signals or data via a communication line so as to determine an abnormality of the apparatus. The abnormality diagnosis may be executed for a plurality of apparatuses by a single abnormality diagnosis device. The plural apparatuses may be installed at different locations. 
     While the present invention has been described above by reference to the embodiments, it should be understood that the present invention is not intended to be limited to the descriptions and the drawings composing part of this disclosure. Various alternative embodiments, examples, and technical applications will be apparent to those skilled in the art according to this disclosure. 
     REFERENCE SIGNS LIST 
     
         
         
           
               11  COMMUNICATION UNIT 
               12  DISTURBANCE TORQUE CALCULATION UNIT 
               13  SENSOR 
               14  SPEED REDUCER 
               15  ACTION CONTROL UNIT 
               21  COMMUNICATION UNIT (CONTROL UNIT) 
               22  ABNORMALITY LEVEL DETERMINATION UNIT (CONTROL UNIT) 
               23  ABNORMALITY PREDICTION UNIT (CONTROL UNIT) 
               24  NOTIFICATION CONTENT SETTING UNIT (CONTROL UNIT) 
               25  CORRELATION ANALYSIS UNIT (CONTROL UNIT) 
               31  SENSOR DB 
               32  MAINTENANCE HISTORY DB (MAINTENANCE HISTORY STORAGE UNIT) 
               33  ABNORMALITY PREDICTION DB 
               34  CORRELATION STORAGE DB (CORRELATION STORAGE UNIT) 
               61  DISPLAY CONTROL UNIT 
               62  DISPLAY (DISPLAY UNIT) 
               101  ROBOT 
               102  ABNORMALITY DIAGNOSIS DEVICE 
               103  USER INTERFACE (UI)