Patent Publication Number: US-2023161337-A1

Title: Diagnostic device, server, and diagnostic method

Description:
TECHNICAL FIELD 
     The present invention relates to a diagnostic device, a server, and a diagnostic method. 
     BACKGROUND ART 
     There are diagnostic devices that perform diagnosis (such as prediction and detection of out of order) for industrial devices such as machine tools and robots. In such diagnostic devices, it is necessary for vendors such as manufacturers and distributors of the industrial devices to collect sensor data of measurement values measured by sensors arranged in a user&#39;s industrial device for the purpose of functional improvement such as improvement of diagnosis accuracy. 
     In this regard, there is known a technique for: transmitting context information corresponding to a current operation of a target device and detection information such as sound data detected in the operation to a learning device; acquiring a model corresponding to the transmitted context information, from the learning device that combines models Generated from detection information corresponding to identical or similar context information, respectively; and determining using the detection information detected in the operation and the acquired model whether an operation of the target device is normal. For example, see Patent Document 1. 
     Patent Document 1: Japanese Unexamined Patent Application, Publication No.2018-25945 
     DISCLOSURE OF THE INVENTION 
     Problems to be Solved by the Invention 
     However, for example, it is difficult to constantly upload sensor data of the industrial device to a vender&#39;s server or the like. In other words, the capacity of sensor data of the industrial device is generally large, and uploading all the data will put pressure on a network bandwidth. Further, from the viewpoint of security, the industrial device is often not always connected to an external network. 
     In addition, when labeling (annotation) for sensor data is required, there is a problem that a load on a user increases when the number of target data is large. 
     Therefore, it is desirable to select only data that has a great influence on functional improvement at the vendor, and to upload the selected data to a server. 
     Means for Solving the Problems 
     (1) An aspect of the present disclosure provides a. diagnostic device that is communicatively connected to a server configured to learn an anomaly of an industrial device and to generate a classification learning model for the anomaly, the diagnostic device including: a sensor signal acquisition unit configured to acquire a sensor signal including a measurement value measured by at least one sensor arranged in the industrial device; a device diagnosis unit configured to diagnose based on the acquired sensor signal whether the industrial device is normal or anomalous; a classification unit configured to classify the anomaly of the industrial device based on the sensor signal and the classification learning model when the device diagnosis unit diagnoses that the industrial device is anomalous; and a transmission unit configured to determine, based on at least one of a diagnosis result by the device diagnosis unit and a classification result by the classification unit, whether to transmit the sensor signal to the server, and transmits the sensor signal to the server when it is determined that the sensor signal can be transmitted. 
     (2) An aspect of the present disclosure provides a diagnostic device that is communicatively connected to a server including a classification learning model to which a sensor signal indicating an anomaly acquired by an industrial device is input to classify the anomaly and configured to learn a classification of the sensor signal that is not classifiable and to update the classification learning model, the diagnostic device including: a sensor signal acquisition unit configured to acquire a sensor signal including a measurement value measured by at least one sensor arranged in the industrial device; a device diagnosis unit configured to diagnose based on the acquired sensor signal whether the industrial device is normal or anomalous; a transmission unit configured to transmit the sensor signal to the server when the device diagnosis unit diagnoses that the industrial device is anomalous; and a label information generation unit configured to determine, based on a classification result for the sensor signal acquired from the server, a generation timing of a label indicating a content of the anomaly of the industrial device with respect to the sensor signal and generates the label, in which the transmission unit transmits the label to the server. 
     (3) An aspect of the present disclosure provides a server that is communicatively connected to the diagnostic device according to (1) above, the server including a classification model learning unit configured to learn the anomaly of the industrial device using a sensor signal received from the diagnostic device, generates the classification learning model, and transmits the generated classification learning model to the diagnostic device. 
     (4) An aspect of the present disclosure provides a server that is communicatively connected to the diagnostic device according to (2) above, the server including: a classification model learning unit configured to learn the anomaly of the industrial device using a sensor signal received from the diagnostic device and generates the classification learning model; and a classification unit configured to classify the anomaly of the industrial device based on the sensor signal and the classification learning model. 
     (5) An aspect of the present disclosure provides a diagnostic method using a diagnostic device that is communicatively connected to a server configured to learn an anomaly of an industrial device and to generate a classification learning model for the anomaly, the diagnostic method including: a sensor signal acquisition step of acquiring a sensor signal including a measurement value measured by at least one sensor arranged in the industrial device; a device diagnosis step of diagnosing, based on the acquired sensor signal, whether the industrial device is normal or anomalous; a classification step of classifying the anomaly of the industrial device based on the sensor signal and the classification learning model when the industrial device is diagnosed to be anomalous; and a transmission step of determining, based on at least one of a diagnosis result in the device diagnosis step and a classification result in the classification step, whether to transmit the sensor signal to the server, and transmitting the sensor signal to the server when it is determined that the sensor signal can be transmitted, 
     (6) An aspect of the present disclosure provides a diagnostic method using a diagnostic device that is communicatively connected to a server including a classification learning model to which a sensor signal indicating an anomaly acquired by an industrial device is input to classify the anomaly and configured to learn a classification of the sensor signal that is not classifiable and to update the classification learning model, the diagnostic method including: a sensor signal acquisition step of acquiring a sensor signal including a measurement value measured by at least one sensor arranged in the industrial device; a device diagnosis step of diagnosing, based on the acquired sensor signal, whether the industrial device is normal or anomalous; a transmission step of transmitting the sensor signal to the server when the industrial device is diagnosed to be anomalous; and a label information generation step of determining, based on a classification result for the sensor signal acquired from the server, a generation timing of a label indicating a content of the anomaly of the industrial device with respect to the sensor signal and generating the label, in which the transmission step includes transmitting the label to the server. 
     Effects of the Invention 
     According to the aspect, it is possible to select only data that has a great influence on functional improvement at the vendor, and to upload the selected data to a server. 
    
    
     
       BRIEF DESCRIPTION CF THE DRAWINGS 
         FIG.  1    is a functional block diagram showing a functional constitution example of a diagnosis system according to a first embodiment; 
         FIG.  2    is a flowchart illustrating a diagnosis process of a diagnostic device and a collection process of a server; 
         FIG.  3    is a flowchart illustrating an acquisition process of the diagnostic device and a learning process of the server; 
         FIG.  4    is a functional block diagram showing a functional constitution example of a diagnosis system according to a second embodiment; 
         FIG.  5    is a flowchart illustrating a diagnosis process of a diagnostic device and a collection process of a server; 
         FIG.  6    is a flowchart illustrating a learning process of the server; and 
         FIG.  7    is a diagram showing an example of a constitution of a diagnosis system. 
     
    
    
     PREFERRED MODE FOR CARRYING OUT THE INVENTION 
     Hereinafter, a first embodiment of the present disclosure will be described with reference to the drawings. 
     First Embodiment 
       FIG.  1    is a functional block diagram showing a functional constitution example of a diagnosis system according to a first embodiment. As shown in  FIG.  1   , a diagnosis system  1  includes an industrial device  10 , a diagnostic device  20 , and a server  30 . 
     The industrial device  10 , the diagnostic device  20 , and the server  30  may be connected to each other via a network (not shown) such as a LAN (Local Area Network) or the Internet. In this case, the industrial device  10 , the diagnostic device  20 , and the server  30  include a communication unit (not shown) configured to communicate with each other by such a connection. Further, the industrial device  10 , the diagnostic device  20 , and the server  30  may be directly connected to each other via a connection interface (not shown). 
     The industrial device  10  is a machine tool, an industrial robot or the like known to those skilled in the art, and includes a sensor  11 . The industrial device  10  operates based on an operating instruction from a controller (not shown). The controller (not shown) is a numerical controller when the industrial device  10  is a machine tool, and as a robot controller when the industrial device  10  is a robot. Further, the controller (not shown) may be included in the industrial device  10 . 
     The sensor  11  measures a state related to moving of a motor included in the industrial device  10  and movable parts (not shown) such as a spindle and an arm attached to the motor. The sensor  11  outputs a sensor signal including sensor data, which is a measurement value measured by the sensor  11 , to the diagnostic device  20  so as to use the sensor data as data for diagnosis. The sensor  11  can be realized by any sensor, but can be realized by, for example, a sensor such as an acceleration sensor, an AE (Acoustic Emission) sensor, a temperature sensor, an ammeter, or a voltmeter. 
     Further, the sensor data measured by the sensor  11  may include feedback data for servo control (speed feedback and torque command calculated from the speed feedback). 
     In  FIG.  1   , the number of sensors  11  is one, but is not limited thereto. For example, the industrial device  10  may be arranged with a plurality of sensors  11  configured to measure the same type of sensor data, or may be arranged with a plurality of sensors  11  configured to measure sensor data of different types from each other. 
     &lt;Diagnostic Device  20 &gt; 
     The diagnostic device  20  includes a control unit  21  and a display unit  22 . Further, the control unit  21  includes a sensor signal acquisition unit  210 , a device diagnosis unit  212 , a classification unit  213 , a transmission unit  214 , a display control unit  215 , and a label information generation unit  216 . 
     The display unit  22  is a display device such as an LCD (Liquid Crystal. Display). The display unit  22  displays, based. on a control instruction of the display control unit  215  to be described below, a diagnosis result of the industrial device  10  diagnosed by the device diagnosis unit  212  to be described below and a classification result of anomaly of the industrial device  10  classified by the classification unit  213  to be described below. Further, the display unit  22  may display, based on the control instruction of the display control unit  215 , a user interface that receives an instruction to transmit the sensor data using the transmission unit  214  to be described below, the instruction being input via an input device (not shown) such as a keyboard or a touch panel included in the diagnostic device  20  from the user. 
     &lt;Control Unit  21 &gt; 
     The control unit  21  includes a CPU (Central Processing Unit) , a ROM (Read Only Memory) , a RAM, a CMOS (Complementary Metal-Oxide-Semiconductor) memory and the like, and these components are configured to be able to communicate with each. other via a bus, as is known to those skilled in the art. 
     The CPU is a processor that controls the diagnostic device  20  as a whole. The CPU reads out a system program and an application program stored in the ROM via the bus, and controls the entire diagnostic device  20  according to the system program and the application program. Thus, as shown in  FIG.  1   , the control unit  21  is configured to realize functions of the sensor signal acquisition unit  210 , the device diagnosis unit  212 , the classification unit  213 , the transmission unit  214 , the display control unit  215 , and the label information generation unit  216 . The RAM stores various data, for example, temporary calculation data and display data. Further, the CMOS memory is backed up by a battery (not shown) , and is configured as a non-volatile memory in which a storage state is retained even when the diagnostic device  20  is powered off. 
     The sensor signal acquisition unit  210  acquires a sensor signal including a measurement value (sensor data) measured by at least one sensor  11  arranged in the industrial device  10 . The sensor signal acquisition unit  210  outputs the acquired sensor signal to the device diagnosis unit  212 , the classification unit  213 , and the transmission unit  214 . 
     The device diagnosis unit  212  diagnoses based on the acquired sensor signal whether the industrial device  10  is normal or anomalous. 
     The device diagnosis unit  212  is, for example, a one-class classifier such as One-class SVM (Support Vector Machine) (hereinafter, also referred to as “one-class SVM”) or Gaussian mixture model. The device diagnosis unit  212  learns distribution of the sensor data in a normal state of the industrial device  10  in the same or similar operational condition of the industrial device  10 , and determines to be anomalous with a deviation from the distribution of the sensor data in the normal state (that is, the degree of divergence), for example. 
     In the following description, the operational condition of the industrial device  10  will not be described, but in reality, a one-class classifier is generated for each operational condition. (context information) of the industrial device  10 , and the diagnosis may be made on the industrial device  10  for each operational condition (context information) of the industrial device  10 . 
     Specifically, the one-class classifier by the one-class SVM method is a method in which an SVM as a classification learning model for classifying sensor data into two classes (groups) is applied. The SVM obtains a hyperplane that classifies learning data whose classes are defined such that a distance (margin) between data of two classes is maximized, and uses the hyperplane to classify the sensor data to be determined as any of the classes. Then, the one-class classifier uses only one class of normal data as learning data to obtain a hyperplane that classifies the class of the learning data and the others, and classifies the sensor data using the obtained hyperplane. As a result, the one-class classifier creates a discrimination boundary that can surround most of the learning data through some of the learning data in a space of the sensor data, and classifies the sensor data to be determined as either normal or anomalous according to the discrimination boundary. 
     In other words, the device diagnosis unit  212 , which is a one-class classifier based on the learning data in the normal state, can diagnose whether the sensor data is classified into a normal class of the learning data, that is, whether the industrial device  10  to be diagnosed is normal or anomalous. The device diagnosis unit  212  outputs the diagnosis result to the classification unit  213 , the transmission unit  214 , and the display control unit  215  which will be described below. 
     When the device diagnosis unit  212  diagnoses that the industrial device  10  is anomalous, the classification unit  213  classifies the anomaly of the industrial device  10  based on the sensor signal and the classification learning model co be described below. 
     Specifically, as will be described below, the classification unit  213  acquires (downloads) a one-class classifier as a classification learning model based on learning data of anomaly A i  from the server  30 , and adds the acquired one-class classifier, for example. The classification unit  213  determines using the added one-class classifier and the sensor data whether the sensor data is classified into a class of the learning data of the anomaly A i , that is, whether the anomaly of the industrial device  10  is the anomaly A i . The subscript i is an integer from 1 to n, and n is an integer of 1 or more. 
     In other words, the classification unit  213  classifies the anomaly of the industrial device  10  to be diagnosed, based on the sensor data of the industrial device  10  that is diagnosed to be anomalous by the device diagnosis unit  212 . For example, the classification unit  213  uses the one-class classifier based on the learning data of each known anomaly A generated by the server  30  to be described below to determine whether the sensor data conforms to the learning data of the anomaly A i , whereby determining whether the anomaly of the industrial device  10  is the anomaly A i . On The other hand, when it is determined not to be any one from anomaly A l  to anomaly A n , the classification unit  213  may determine that the sensor data is unknown data. 
     Then, the classification unit  213  outputs the classification result to the transmission unit  214 . The display control unit  215 , and the label information generation. unit  216 . 
     Even when it is determined to be anomaly A i , if the number of sensor data corresponding to the determined anomaly A i  is smaller than the preset number of samples, the classification unit  213  may determine that the sensor data is unknown data. 
     Further, when the classification learning model is a learned model of a neural network, the classification unit  213  may determine to be unknown data when a value of an output layer (softmax function) of the neural network is equal to or less than a predetermined value preset for all classes. 
     Further, when the classification learning model is the one-class classifier that is learned by input of data of all classes from the known anomaly A l  to the anomaly A n , the classification unit  213  may determine to be unknown data when. the output of the one-class classifier is larger or smaller than a preset threshold value. 
     Further, the classification unit  213  acquires the one-class classifier based on the learning data of each known anomaly A i  from the server  30 , and classifies the anomaly of the industrial device  10  using the acquired one-class classifier and the sensor data, but is not limited thereto. For example, the classification unit  213  may acquire a classifier such as an SVM or a decision tree generated by machine learning in the server  30  to be described below from the server  30 , and may classify the anomaly of the industrial device  10  using the acquired classifier such as the SVM or the decision tree and the sensor data. 
     Further, the classification unit  213  may be a one-class classifier that classifies as data from the anomaly A l  to the anomaly A n  known to the server  30  to be described below or unknown data. 
     The transmission unit  214  determines, based on the diagnosis result of the device diagnosis unit  212 , the classification result of the classification unit  213 , or both the results, whether to transmit the sensor signal to the server  30 , and transmits the sensor signal to the server  30  when determining that the sensor signal is determined to be transmitted. 
     Specifically, when the device diagnosis unit  212  diagnoses that the industrial device  10  is anomalous and the classification unit  213  classifies the sensor data of the sensor signal as unknown data, the transmission unit.  214  determines that the sensor signal including the sensor data is transmitted to the server  30 . Then, the transmission unit  214  transmits the sensor signal to the server  30 . 
     In other words, the transmission unit  214  transmits only the sensor signal of the sensor data, which is determined to be unknown data by the classification unit  213  in the server  30 , to the server  30 , and thus a load on the network and a load on the user can be reduced. 
     When the transmission unit  214  transmits the sensor signal, the display control unit  215  displays, on the display unit  22 , a user interface that urges the user to transmit the sensor signal at a timing when data transmission is necessary. 
     Specifically, the display control unit  215  may display, on the display unit  22 , a user interface including a message such as “Please transmit data to the server  30 ” and a “transmit” button, at a timing when the transmission unit  214  transmits the sensor signal, for example. 
     Further, the display control unit.  215  may display, on the display unit  22 , the diagnosis result of the device diagnosis unit  212 , the classification result of the classification unit  213 , or both the results. 
     The label information generation unit  216  determines, based on the classification result of the classification unit  213 , a generation timing of a label indicating contents of the anomaly of the industrial device  10  with respect to the sensor signal, and generates a label for the sensor signal to be transmitted. 
     Specifically, the label information generation unit  216  Generates a label indicating contents of the anomaly of the industrial device  10  in a format in which an anomaly part and an anomaly phenomenon are combined, that is, in a format of “damage of spindle bearing”, “deterioration of guide sliding surface”, “damage of tool” or the like, at a timing when the transmission unit  214  transmits the sensor data determined to be unknown data by the classification unit  213 , for example. 
     In addition, the label information generation unit  216  may generate a label based on the user&#39;s input such as anomalous noise and vibration generated at a time when the industrial device  10  becomes anomalous through an input device (not shown) of the diagnostic device  20 , for example. 
     Further, the label information generation unit  216  may input the label by displaying a screen to urge the user to input the label on the display unit  22 . 
     Further, the label information generation unit  216  may generate a label based on (another) sensor signal, a device operating situation, an environment situation and the like, at the acquisition time of the data to be labeled. 
     The label format is not limited to the format in which the anomaly part and the anomaly phenomenon are combined, and may be another format. 
     &lt;Server  30 &gt; 
     The server  30  is, for example, a computer device, and communicates with the diagnostic device  20  via a network (not shown). As shown in.  FIG.  1   , the server  30  includes a classification model learning unit  31 . 
     The server  30  includes an arithmetic operation processing unit such as a CPU in order to realize a functional block of the classification model learning unit  31 . Further, the server  30  includes not only an auxiliary storage device such as an HDD that stores various control programs including application software and an OS (Operating System), but also a main storage device such as a RAM that stores data temporarily required for The arithmetic operation processing unit to execute the programs. 
     Then, in the server  30 , the arithmetic operation processing unit reads out the application software and the OS from the auxiliary storage device, deploys the read application software and OS, to the main storage device, and performs arithmetic operation processing based on such application software and OS. Further, based on the result of arithmetic operation, various hardware in the server  30  are controlled. Thus, the functional block of the present embodiment is realized. In other words, the present embodiment can be realized by cooperation of hardware and software. 
     In addition, each of the functions of the server  30  may be realized using a virtual server function or the like on a cloud. 
     The classification model learning unit  31  receives, for example, the sensor data determined to be unknown data by the diagnostic device  20  and the label from the diagnostic device  20 . The classification model learning unit  31  stores the received sensor data and label in a storage zone corresponding to the contents of the label in a storage zone of a storage unit (not shown) such as an HDD included in the server  30 . 
     Then, for example, when the number of sensor data in the storage zone for each label is equal to or more than the predetermined number of preset data, the classification model learning unit  31  obtains a hyperplane, which classifies a class as a new anomaly A n+1  and the others, using only a one-class of sensor data as learning data in the storage zone, and newly generates a classification learning model of a one-class classifier that classifies sensor data using the obtained hyperplane. Then, the classification model learning unit  31  transmits the classification learning model, which classifies the newly generated anomaly A n+1 , to the diagnostic device  20 . 
     The classification model learning unit  31  may construct a learned model of a neural network that predicts a probability (softmax function) of the anomaly A n+1  in the output layer with respect to the input of the sensor data in the input layer by accepting a set of the sensor data of the anomaly A n+1  and the label as training data and performing supervised learning using the accepted training data. 
     Further, the classification model learning unit  31  may Generate, for example, a classifier such as an SVM or a decision tree, or may generate a one-class classifier that classifies as data from the anomaly A i  to the anomaly A n  known. to the server  30  or unknown data. 
     &lt;Diagnosis Process of Diagnostic device  20  and Collection Process of Server  30 &gt; 
     Next, operations related to a diagnosis process of the diagnostic device  20  and a collection process of the server  30  will be described. 
       FIG.  2    is a flowchart illustrating a diagnosis process of the diagnostic device  20  and a collection process of the server  30 . 
     In Step  311 , the sensor signal acquisition unit  210  acquires sensor signal including sensor data measured by the sensor  11  of the industrial device  10 . 
     In. Step S 12 , the device diagnosis unit  212  diagnoses, based on the sensor data of the sensor signal acquired in Step S 11 , whether the industrial device  10  is normal or anomalous. 
     In Step S 13 , when it is diagnosed in Step S 12  that the industrial device  10  is anomalous, the classification unit  213  classifies the anomaly of the industrial device  10  based on the sensor data. 
     In Step S 14 , the display control unit  215  displays the diagnosis result and the classification result on the display unit  22 . 
     In Step S 15 , the transmission unit  214  determines, based on the diagnosis result in Step S 12 , the classification result in Step S 13 , or both the results, whether to transmit the sensor signal. When the sensor signal is determined to be transmitted, the process proceeds to Step S 16 . On the other hand, when it is determined that the sensor signal cannot be transmitted, the process returns to Step S 11 . 
     In Step S 16 , the label information generation unit  216  determines a generation timing of a label for the sensor data determined to be unknown data in Step S 13 , and generates a label for the sensor data. 
     In Step S 17 , when the user presses the “transmit” button of the user interface displayed on the display unit.  22 , the transmission unit  214  transmits the sensor signal of the sensor data of the unknown data with the label attached to the server  30 . Then, the process returns to Step S 11 . 
     In Step S 31 , the classification model learning unit  31  of the server  30  receives the sensor signal of the sensor data of the unknown data with the label attached transmitted in Step S 17 , from the diagnostic device  20 , and stores the received. sensor data and label in the storage zone corresponding to the contents of the label in the storage zone of the storage unit (not shown) of the server  30 . 
     The diagnostic device  20  performs the process related to the acquisition of the sensor signal and the process related to the label generation and transmission of the unknown data in a time-series manner, but may execute the above processes in parallel or individually. 
     &lt;Acquisition Process of Diagnostic Device  20  and Learning Process of Server  30 &gt; 
     Next, operations related to an acquisition process of the diagnostic device  20  and a learning process of the server  30  will be described. 
       FIG.  3    is a flowchart illustrating an acquisition process of the diagnostic device  20  and a learning process of the server  30 . 
     In Step S 51 , the classification model learning unit  31  determines whether the sensor data collected by the collection process in  FIG.  2    is equal to or more than the predetermined number of preset data. When the collected sensor data is equal to or more than the predetermined number of data, the process proceeds to Step S 52 . On the other hand, when the collected sensor data is less than the predetermined number of data, the process waits in Step S 51  until the sensor data becomes equal to or more than the predetermined number of data. 
     In Step S 52 , the classification model learning unit  31  performs machine learning using the sensor data collected to be equal to or more than the predetermined number of data and the labels, and thus generates a classification learning model that classifies as a new anomaly A n+1 . 
     In Step S 53 , the classification model learning unit  31  of the server  30  transmits a message to the diagnostic device  20  that the classification learning model for classifying the new anomaly A n+1  is generated. 
     In Step S 41 , the classification unit  213  of the diagnostic device  20  determines whether to receive the message indicating that the classification learning model for classifying the new anomaly A n+1  is generated, from the server  30 . When the message is received, the process proceeds to Step S 42 . On the other hand, when the message is not received, the process waits in Step S 41  until the message is received. 
     In. Step S 42 , the classification unit  213  downloads and acquires the generated classification learning model from the server  30 . 
     The learning process of the server  30  exemplifies a mini-batch process, but may be replaced with a batch process or a real-time process instead of the mini-batch process. 
     As described above, the diagnostic device  20  according to the first embodiment acquires the sensor signal including the sensor data measured by the sensor  11  of the industrial device  10 , and diagnoses based on the acquired sensor data whether the industrial device  10  is normal or anomalous. When i is diagnosed that the industrial device  10  is anomalous, the diagnostic device  20  classifies the anomaly of the industrial device  10  based on the sensor data. When the sensor data is determined to be unknown data by the classification, the diagnostic device  20  determines that the sensor data can be transmitted to the server  30 , and transmits the sensor data to the server  30 . 
     Thus, the diagnostic device  20  can select only unknown data that has a great influence on functional improvement at a vender, and can upload the selected unknown data to the server  30 . Thereby, the diagnostic device  20  can reduce the load on the network. 
     Further, the diagnostic device  20  can reduce the user load by labelling (annotating) the selected unknown data uploaded to the server  30 . 
     The first embodiment has been described above. 
     &lt;Second Embodiment&gt; 
     Next, a second embodiment will be described. 
     In the first embodiment, the diagnostic device  20  diagnoses, using the sensor data included in the sensor signal from the sensor  11 , whether the industrial device  10  is normal or anomalous, classifies the anomaly of the industrial device  10  using the classification learning mode generated by the server  30  and the sensor data when the industrial device  10  is diagnosed to be anomalous, and transmits the sensor data to the server  30  when the sensor data is determined to be unknown data. On the other hand, in the second embodiment, a diagnostic device  20 A diagnoses, using the sensor data included in the sensor signal from the sensor  11 , whether the industrial device  10  is normal or anomalous, transmits all sensor data, in which the industrial device  10  is diagnosed to be anomalous, to a server  30 A, generates a label for the sensor data determined to be unknown data by the server  30 A out of the transmitted sensor data, and transmits the label to the server  30 A. 
     In other words, the second embodiment is different from the first embodiment in that the diagnostic device  20 A diagnoses based on the acquired sensor signal whether the industrial device  10  is normal or anomalous, transmits the sensor signal to the server  30 A when the industrial device is diagnosed to be anomalous, determines a generation timing of a label for the sensor signal based on the classification result for the anomaly of the industrial device  10  acquired from the server  30 A to generate the label, and transmits the generated label to the server  30 A. 
     Thus, the diagnostic device  20 A can select only data that has a great influence on functional improvement at a vender and that the industrial device  10  is diagnosed to be anomalous, and can upload the selected data to the server  30 A. 
     Hereinafter, the second embodiment will be described. 
       FIG.  4    is a functional bloc diagram showing a functional constitution example of a diagnosis system according to the second embodiment. Components having the same functions as the components of the diagnosis system  1  shown in  FIG.  1    are denoted by the same reference numerals, and details thereof will not be described. 
     As shown in  FIG.  4   , a diagnosis system  1  according to the second embodiment includes an industrial device  10 , a diagnostic device  20 A, and a server  30 A. 
     Similarly to the case of the first embodiment, the industrial device  10  is a machine tool, an industrial robot or the like known to those skilled in the art, and includes a sensor  11 . The industrial device  10  operates based on an operating instruction from a controller (not shown). 
     Similarly to the case of the first embodiment, the sensor  11  measures a state related to moving of a motor included in the industrial device  10  and movable parts (not shown) such as a spindle and an arm attached to the motor. The sensor  11  outputs sensor data, which is a measurement value measured by the sensor  11 , to the diagnostic device  20 . 
     &lt;Diagnostic Device  20 A&gt; 
     The diagnostic device  20 A includes a control unit  21   a  and a display unit  22 . Further, the control unit  21   a  includes a sensor signal acquisition unit  210 , a device diagnosis unit  212 , a transmission unit  214   a , a display control unit  215 , and a label information generation unit  216   a.    
     In the second embodiment, a function corresponding to the classification unit  213  of the first embodiment is realized as a classification unit  32  of the server  30 A to be described below. In other words, the diagnostic device  20 A according to the second embodiment does not classify the anomaly generated in the industrial device  10  using the sensor data measured by the sensor  11 . 
     Further, the display unit  22  has the same function as the display unit  22  in the first embodiment. 
     &lt;Control Unit  21   a&gt;   
     Similarly to the control unit  21  of the first embodiment, the control unit  21   a  includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM, a CMOS (Complementary Metal-Oxide-Semiconductor) memory and the like, and these components are configured to be able to communicate with each other via a bus, as is known to those skilled in the art. 
     The CPU is a processor that controls the diagnostic device  20 A as a whole. The CPU reads out a system program and an application program stored in the ROM via the bus, and controls the entire diagnostic device  20 A according to the system program and the application program. Thus, as shown in  FIG.  4   , the control unit.  21   a  is configured to realize functions of the sensor signal acquisition unit  210 , the device diagnosis unit  212 , the transmission unit  214   a , the display control unit.  215 , and the label information generation unit  216   a.    
     The sensor signal acquisition unit  210 , the device diagnosis unit  212 , and the display control unit  215  have the same functions as the sensor signal acquisition unit  210 , the device diagnosis unit  212 , and the display control unit  215  in the first embodiment. 
     When the device diagnosis unit  212  diagnoses that the industrial device  10  is anomalous, the transmission unit  214  transmits the sensor signal to the server  30 A. 
     In other words, the transmission unit  214   a  transmits only the sensor signal of the sensor data, for which industrial device  10  is diagnosed to be anomalous by the device diagnosis unit  212 , to the server  30 A, and thus a load on the network and a load on the user can be reduced. 
     Then, as will be described below, when the transmitted sensor data is classified as unknown data by the server  30 A, the transmission unit  214   a  may transmit a label generated for The sensor data by the label information generation unit  216   a , which will be described below, to the server  30 A. 
     Upon receiving the classification result for the sensor signal transmitted by the transmission unit  214   a  from the server  30 A to be described below, the label information generation unit  216   a  determines, based on the received classification result, a generation timing of a label indicating contents of the anomaly of the industrial device  10  with respect to the sensor signal and generates the label. The transmission unit  214   a  transmits the generated label to the server  30 A. 
     Specifically, the label information generation unit  216   a  generates a label indicating contents of the anomaly of the industrial device  10  in a format in which an anomaly part and an anomaly phenomenon are combined, that is, in a format of “damage of spindle bearing”, “deterioration of guide sliding surface”, “damage of tool” or the like, at a timing when the transmission unit  214   a  receives the classification result, in which the sensor data of the sensor signal transmitted by the transmission unit  21 -la is determined to be unknown data, from the server  30 A. 
     In addition, the label information generation unit  216   a  may generate a label based on the user&#39;s input such as anomalous noise and vibration generated at a time when the industrial device  10  becomes anomalous through an input device (not shown) of the diagnostic device  20 , for example. 
     Further, the label information generation unit  216   a  may input the label by displaying a screen to urge the user to input the label on the display unit  22 , 
     Further, the label information generation unit  216   a  may generate a label based on (another) sensor signal, a device operating situation, an environment situation and the like, at the acquisition time of the data to be labeled. 
     The label format is not limited to the format in which the anomaly part and the anomaly phenomenon are combined, and may be another format. 
     &lt;Server  30 A&gt; 
     Similarly to the server  30  of the first embodiment, the server  30 A is a computer device, and communicates with the diagnostic device  20 A via a network (not shown). As shown in  FIG.  4   , the server  30 A includes a classification model learning unit  31  and a classification unit  32 . 
     The server  30 A includes an arithmetic operation. processing unit such as a CPU in order to realize functional blocks of the classification model learning unit  31  and the classification unit  32 . Further, the server  30 A includes not only an auxiliary storage device such as an HDD that stores various control programs including application software and an OS, but also a main storage device such as a RAM that stores data temporarily required for the arithmetic operation processing unit to execute the programs. 
     Then, in the server  30 A, the arithmetic operation processing unit reads out the application software and the OS from the auxiliary&#39; storage device, deploys the read application software and OS to the main storage device, and performs arithmetic operation processing based on such application software and OS. Further, based on the result of arithmetic operation, various hardware in the server  30 A are controlled. Thus, the functional block of the second embodiment is realized. In other words, the second embodiment can be realized by cooperation of hardware and software. 
     In addition, each of the functions of the server  30 A may be realized using a virtual server function or the like on a cloud. 
     The classification model learning unit  31  has the same function as the classification model learning unit  31  of the first embodiment. However, the classification model learning unit  31  according to the second embodiment outputs the Generated classification learning model to the classification unit  32  to be described below. 
     The classification unit  32  classifies the anomaly of the industrial device  10 , using the sensor data of the sensor signal, for which the industrial device  10  is diagnosed to be anomalous, received from the diagnostic device  20 A, and the classification learning model generated by the classification model learning unit  31 . 
     Specifically, similarly to the classification unit  213  of the first embodiment, the classification unit  32  classifies, using a one-class classifier based on learning data of an anomaly A i  generated by the classification model learning unit  31  and the received sensor data, whether the received sensor data is classified into a class of the learning data of the anomaly A i , that is, whether the anomaly of the industrial device  10  is the anomaly A i , for example. 
     In other words, the classification unit  32  classifies the anomaly of the industrial device  10 , based on the sensor data of the industrial device  10  that is diagnosed to be anomalous by the diagnostic device  20 A. For example, the classification unit  32  uses the one-class classifier based on the learning data of each known anomaly A i  generated by the classification model learning unit  31  to determine whether the received sensor data conforms to the learning data of the anomaly A i , whereby determining whether the anomaly of the industrial device  10  is the anomaly A i . On the other hand, when it is determined not to be any one from anomaly A i  to anomaly A n , the classification unit  32  determines that the corresponding sensor data is unknown data. 
     Then, the classification unit  32  outputs the classification result to the diagnostic device  20 A. 
     Even when it is determined to be any anomaly A i , if the number of sensor data corresponding to the determined anomaly A i  is smaller than the preset number of samples, the classification unit  32  may determine that the sensor data is unknown data. 
     Further, when the classification learning model is a learned model of a neural network, the classification unit  32  may determine to be unknown data when a value of an output layer (softmax function) of the neural network is equal to or less than a predetermined value preset for all classes. 
     Further, when the classification learning model is the one-class classifier that is learned by input of known data of all classes, the classification unit  32  may determine to be unknown data when the output of the one-class classifier is larger or smaller than a preset threshold value. 
     &lt;Diagnosis Process of Diagnostic device  20 A and Collection Process of Server  30 A&gt; 
     Next, operations related to a diagnosis process of the diagnostic device  20 A and a collection process of the server  30 A will be described. 
       FIG.  5    is a flowchart illustrating a diagnosis process of the diagnostic device  20 A and a collection process of the server  30 A. 
     In Step S 61 , the sensor signal acquisition unit  210  performs the same process as in Step  11  of the first embodiment, and acquires sensor signal including sensor data measured by the sensor  11  of the industrial device  10 . 
     In Step S 62 , the device diagnosis unit  212  diagnoses, based on the sensor data of the sensor signal acquired in Step S 61 , whether the industrial device  10  is anomalous. When the industrial device  10  is anomalous, the process proceeds to Step S 63 . On the other hand, when the industrial device  10  is normal, the process returns to Step S 61 . 
     In Step S 63 , the transmission unit.  214   a  transmits the sensor data, for which the industrial device  10  is diagnosed to be anomalous in Step S 62 , to the server  30 A. 
     In Step S 71 , the classification unit  32  of the server  30 A receives the sensor data, which is transmitted in. Step S 63 , from the diagnostic device  20 A. 
     In Step S 72 , the classification unit  32  classifies the anomaly of the industrial device  10  based on the sensor data received in Step S 71 . 
     In Step S 73 , the classification unit  32  transmits the classification result classified in Step S 72  to the diagnostic device  20 A. 
     In Step S 64 , the display control unit  215  of the diagnostic device  20 A receives the classification result from the server  30 A. 
     In Step S 65 , the display control unit  215  performs the same process as in Step  14  of the first embodiment, and displays the diagnosis result and the classification result on the display unit  22 . 
     In Step S 66 , the label information generation unit  216   a  determines, based on the classification result received in Step S 64 , whether the sensor data transmitted in Step S 63  is classified as unknown data by the server  30 A. When the sensor data is classified as unknown data, the process proceeds to Step S 67 . On the other hand, when the sensor data is not unknown data, that is, when the sensor data is classified as any data from the anomaly A 1  to the anomaly A n , the process returns to Step S 61 . 
     In Step S 67 , the label information generation unit  216   a  determines a generation timing of a label for the sensor data classified as unknown data by the server  30 A, and generates a label for the sensor data. 
     In. Step S 68 , when the user presses the “transmit” button of the user interface displayed on the display unit  22 , the transmission unit  214   a  transmits the label generated in Step S 67  to the server  30 A. Then., the process returns to Step S 61 . 
     In Step S 74 , the classification model learning unit.  31  receives the label of the sensor data classified as unknown data in Step S 72 , from the diagnostic device  20 A, and stores the received sensor data and label in the storage zone corresponding to the contents of the label in the storage zone of the storage unit (not shown) of the server  30 A. 
     The diagnostic device  20 A performs the process related to the acquisition of the sensor signal and the process related. to the label generation and transmission of the unknown data in a time-series manner, but may execute the above process in parallel or individually. 
     Further, the server  30 A performs the processes of Steps S 71  to S 73  and the process of Step S 74  in a time-series manner, but may execute the above process in parallel or individually. 
     &lt;Learning Process of Server  30 A&gt; 
     Next, an operation related to a learning process of the server  30 A will be described. 
       FIG.  6    is a flowchart illustrating a learning process of the server  30 A. 
     In Step S 81 , the classification model learning unit  31  performs the same process as in Step S 1  of the first embodiment, and determines whether the sensor data collected by the collection process in  FIG.  5    is equal to or more than the predetermined number of preset data. When the collected sensor data is equal to or more than, the predetermined number of data, the process proceeds to Step S 82 . On the other hand, when the collected sensor data is less than the predetermined. number of data, the process waits in Step S 81  until the sensor data becomes equal to or more than the predetermined number of data. 
     In Step S 82 , the classification model learning unit  31  performs the same process as in Step  52  of the first embodiment, and performs machine learning using the sensor data collected to be equal to or more than the predetermined number of data and the labels, and thus generates a classification learning model that classifies as a new anomaly and outputs the generated classification learning model co the classification unit  32 . 
     The learning process of the server  30 A exemplifies a mini-batch process, but may be replaced with a batch process or a real-time process instead of the mini-batch process. 
     As described above, the diagnostic device  20 A according to the second embodiment acquires the sensor signal including the sensor data measured by the sensor  11  of the industrial device  10 , and diagnoses based on the acquired sensor data whether the industrial device  10  is normal or anomalous. When it is diagnosed that the industrial device  10  is anomalous, the diagnostic device  20 A transmits the acquired sensor signal to the server  30 A. When the sensor data transmitted by the server  30 A is determined to be unknown data, the diagnostic device  20 A generates a label for the sensor data and transmits the generated label to the server  30 A. 
     Thus, the diagnostic device  20 A can select only data diagnosed as an anomaly of the industrial device  10  having a Great influence on functional improvement at a vender, and can upload the selected data to the server  30 A. Thereby, the diagnostic device  20 A can reduce the load on the network. 
     Further, the diagnostic device  20 A can reduce the user load by labelling (annotating) the data determined to be unknown by the server  30 A among the transmitted data diagnosed as an anomaly of the industrial device  10 . 
     The second embodiment has been described above. 
     Although the first and second embodiments have been described above, the diagnostic device  20  or  20 A, and the server  30  or  30 A are not limited to the above-described embodiments, and may be modified and improved within a range in which the object can be achieved. 
     &lt;Modification Example 1&gt; 
     In the first and second embodiments, the diagnostic device  20  or  20 A is exemplified as a device different from the industrial device  10 , but the industrial device  10  may be provided with a part or all of the functions of the diagnostic device  20  or  20 A. 
     Alternatively, the server may include a part or all of the sensor signal acquisition unit  210 , the device diagnosis unit  212 , the classification unit.  213 , the transmission unit  214 , the display control unit  215 , and the label information generation unit  216  of the diagnostic device  20 , or a part or all of the sensor signal acquisition unit  210 , the device diagnosis unit  212 , the transmission unit  214   a , the display control unit  215 , and the label information generation unit  216   a  of the diagnostic device  20 , for example. Further, each function of the diagnostic device  20  or  20 A may be realized using a function of a virtual server or the like on the cloud. 
     Further, the diagnostic device  20  or  20 A may be a distributed processing system in which the function of the diagnostic device  20  or  20 A is appropriately distributed to a plurality of servers. 
     &lt;Modification Example 2&gt; 
     In addition, for example, in the first and second embodiments described above, the diagnostic device  20  or  20 A is connected to one industrial device  10 , but may be connected to a plurality of industrial devices  10  without being limited thereto. 
     &lt;Modification Example 3&gt; 
     Further, for example, in the first and second embodiments described above, the server  30  or  30 A is connected to one diagnostic device  20  or  20 A, but is not limited thereto. For example, as shown in  FIG.  7   , a server  30 B may store a classification learning model generated by a classification model learning unit  31  of the server  30 B for each of industrial devices  10 A( 1 ) to  10 A(m), and may share the classification learning model with m diagnostic devices  20 B( 1 ) to  20  (m) connected to a network  60  (m is an integer of 2 or more). Thus, the classification learning model can be applied even when new industrial device and diagnostic device are arranged. 
     Each of the diagnostic devices  20 B ( 1 ) to  20 B (m) is connected to each of the industrial devices  10 A( 1 ) to  10 A(m). 
     Further, each of the industrial devices  10 A( 1 ) to  10 A (m) corresponds to the industrial device  10  of the first and second embodiments, and may be the same model or different models from each other. Each of the diagnostic devices  20 B( 1 ) to  20 B(m) corresponds to the diagnostic device  20  of the first embodiment or the diagnostic device  20 A of the second embodiment. The server  30 B corresponds to the server  30  of the first embodiment, or the server  30 A of the second embodiment. 
     Each of the functions included in the diagnostic device  20  or  20 A and the server  30  or  30 A of the first and second embodiments can be realized by hardware, software, or a combination thereof. Here, it means that the realizing of such a function by the software is realized when a computer reads and executes a program. 
     The program may be stored and supplied to a computer using various types of non-transitory computer readable media. The non-transitory computer readable media include various types of tangible storage media. Examples of the non-transitory computer readable media include a magnetic recording medium (for example, a flexible disk, a magnetic tape, and a hard disk drive) , a magneto-optic recording medium (for example, a magneto-optic disk), a CD-ROM (Read Only Memory), a CD-R, a CD-R/W, and a semiconductor memory (for example, a mask ROM, a PROM (Programmable ROM), an EPROM (Erasable PROM), a flash. ROM, and a RAM). Further, these programs may be supplied to computers using various types of transitory computer readable media. Examples of the transitory computer readable media include an electrical signal, an optical signal, and an electromagnetic wave. The transitory computer readable media can supply programs to a computer through a wired communication line, for example, electric wires and optical fibers, or a wireless communication line. 
     In addition, the steps of describing the program to be recorded on the recording medium include not only a process performed sequentially in a time-series manner but also a process executed in parallel or individually without being necessarily processed in a time-series manner. 
     In other words, the diagnostic device, the server, and the diagnostic method of the present disclosure can take various embodiments having the following configurations. 
     (1) An aspect of the diagnostic device  20  of the present disclosure provides a diagnostic device that is communicatively connected to a server  30  configured to learn an anomaly of an industrial device  10  and to generate a classification learning model for the anomaly, the diagnostic device including: a sensor signal acquisition unit  210  configured to acquire a sensor signal including a measurement value measured by at least one sensor  11  arranged in the industrial device  10 ; a device diagnosis unit  212  configured. to diagnose based on the acquired sensor signal whether the industrial device  10  is normal or anomalous; a classification unit  213  configured to classify the anomaly of the industrial device based on the sensor signal and the classification learning model when the device diagnosis unit  212  diagnoses that the industrial device  10  is anomaly; and a transmission unit  214  configured to determine, based on at least one of a diagnosis result by the device diagnosis unit  212  and a classification result by the classification unit  213 , whether to transmit the sensor signal to the server  30 , and transmits the sensor signal to the server  30  when it is determined that the sensor signal can be transmitted. 
     According to the diagnostic device  20 , it is possible to select only the data having a great influence on the functional improvement at the vender and to upload the selected data on the server. 
     (2) in the diagnostic device  20  according to (1) described above, the classification unit  213  may classify the sensor signal as unknown data when at least the anomaly of the industrial device  10  is riot classifiable based on the sensor signal or the anomaly is smaller than a preset number of samples, and the transmission unit  214  may transmit the sensor signal classified as unknown data to the server  30 . 
     Thereby, the diagnostic device  20  can reduce the load on the network by transmitting only the sensor signal of the sensor data, which is determined to be unknown data in the server  30 , to the server  30 . 
     (3) In the diagnostic device  20  according to (1) or (2) described above, the device diagnosis unit  212  may be a one-class classifier that learns characteristics of the sensor signal in a normal state in advance and detects the anomaly of the industrial device  10  based on a degree of deviation from the characteristics in the normal state 
     Thereby, the diagnostic device  20  can easily diagnose the anomaly of the industrial device  10  based on the sensor data. 
     (4) In the diagnostic: device  20  according to any one of (1) to (3) described above, the diagnostic device  20  may further include a label information generation unit  216  configured to determine, based on the classification result by the classification unit  213 , a generation timing of a label indicating a content of the anomaly of the industrial device  10  with respect to the sensor signal and generates the label, in which the transmission unit  214  may transmit the sensor signal and the label to the server  30 . 
     Thereby, the diagnostic device  20  can reduce the load on the user by labeling only the sensor signal of the sensor data determined to be unknown data in the server  30 . 
     (5) In the diagnostic device  20  according to (4) described above, the classification unit  213  may acquire, from the server  30 , the classification learning model generated by the server  30  based on the sensor signal and the label transmitted by the transmission unit  214 . 
     Thereby, the diagnostic device  20  can easily diagnose the anomaly or the industrial device  10  based on the sensor data. 
     (6) In the diagnostic device  20  according to (5), the classification learning model may be updated whenever the server  30  receives a new sensor signal from the diagnostic device  20 , and the classification unit  213  may classify the anomaly of the industrial device  10  using the updated classification learning model. 
     Thereby, the diagnostic device  20  can improve the accuracy of classification. 
     (7) In the diagnostic device  20  according to any one of (1) to (6) described above, the diagnostic device  20  may further include a display control unit  215  configured to display, on a display unit  22 , a user interface that prompts to transmit the sensor signal when the transmission unit  214  transmits the sensor signal. 
     Thereby, the diagnostic device  20  can transmit the sensor signal to the server  30  at the, timing desired by the user. 
     (8) In the diagnostic device  20  according to (7) described above, the display control unit  215  may display at least one of the diagnosis result by the device diagnosis unit  212  and the classification result by the classification unit  213  on the display unit  22 . 
     Thereby, the user can confirm whether the anomaly occurs in the industrial device  10  and the occurrence of the anomaly. 
     (9) An aspect of the diagnostic device  20 A of the present disclosure provides a diagnostic device that is communicatively connected to a server  30 A including a classification learning model to which a sensor signal indicating an anomaly acquired by an industrial device  10  is input to classify the anomaly and configured to learn a classification of the sensor signal that is not classifiable and to update the classification learning model, the diagnostic device including: a sensor signal acquisition unit  210  configured to acquire a sensor signal including a measurement value measured by at least one sensor  11  arranged in the industrial device  10 ; a device diagnosis unit  212  configured to diagnose based on the acquired sensor signal whether the industrial device  10  is normal or anomalous; a transmission unit  214   a  configured to transmit the sensor signal to the server  30 A when the device diagnosis unit  212  diagnoses that the industrial device  10  is anomalous; and a label information generation unit  216   a  configured to determine, based on a classification result for the sensor signal acquired from the server  30 A, a generation timing of a label indicating a content of the anomaly of the industrial device  10  with respect to the sensor signal and generates the label, in which the transmission unit  214   a  transmits the label to the server  30 A. 
     According to the diagnostic device  20 A, the same effect as in (1) described above can be obtained. 
     (10) An aspect of the server  30  of the present disclosure provides a server that is communicatively connected to the diagnostic device  20  according to any one of (1) to (8) describe above, the server including a classification model learning unit  31  configured to learn the anomaly of the industrial device  10  using the sensor signal received from the diagnostic device  20 , generates the classification learning model, and transmits the generated classification learning model to the diagnostic device  20 . 
     According to the server  30 , it is possible to receive only the data that has a great influence on the functional improvement at the vender. 
     (11) An aspect of the server  30 A of the present disclosure provides a server that is communicatively connected to the diagnostic device  20 A according to (9) described above, the server including: a classification model learning unit  31  configured to learn the anomaly of the industrial device  10  using a sensor signal received from the diagnostic device  20 A and generates the classification learning model; and a classification unit  32  configured to classify the anomaly of the industrial device  10  based on the sensor signal and the classification learning model. 
     According to the server  30 A, it is possible to receive only the data that has a great influence on the functional improvement at the vender. 
     (12) An aspect of the diagnostic method of the present disclosure provides a diagnostic method using a diagnostic device  20  that is communicatively connected to a server  30  configured to learn an anomaly of an industrial device  10  and to generate a classification learning model for the anomaly, the diagnostic method including: a sensor signal acquisition step of acquiring a sensor signal including a measurement value measured by at least one sensor arranged in the industrial device  10 ; a device diagnosis step of diagnosing, based on the acquired sensor signal, whether the industrial device at as normal or anomalous; a classification step of classifying the anomaly of the industrial device  10  based on the sensor signal and the classification learning model when the industrial device  10  is diagnosed to be anomalous; and a transmission step of determining, based on at least one of a diagnosis result in the device diagnosis step and a classification result in the classification step, whether to transmit the sensor signal to the server  30 , and transmitting the sensor signal to the server  30  when it is determined that the sensor signal can be transmitted. 
     According to the diagnostic method, the same effect as in (1) described above can be obtained. 
     (13) An aspect of the diagnostic method of the present disclosure provides a diagnostic method using a diagnostic device  20 A that is communicatively connected to a server  30 A including a classification learning model to which a sensor signal indicating an anomaly acquired by an industrial device  10  is input to classify the anomaly and configured to learn a classification of the sensor signal that is not classifiable and to update the classification learning model, the diagnostic method including: a sensor signal acquisition step of acquiring a sensor signal including a measurement value measured by at least one sensor  11  arranged in the industrial device  10 ; a device diagnosis step of diagnosing, based on the acquired sensor signal, whether the industrial device  10  is normal or anomalous; a transmission step of transmitting the sensor signal to the server  30 A when the industrial device  10  is diagnosed to be anomalous; and a label information generation step of determining, based on a classification result for the sensor signal acquired from the server  30 A, generation timing of a label indicating a content of the anomaly of the industrial device  10  with respect to the sensor signal and generating the label, in which the transmission step includes transmitting the label to the server  30 A. 
     According to the diagnostic method, the same effect as in (1) described above can be obtained. 
     EXPLANATION OF REFERENCE NUMERALS 
       1  diagnosis system 
       10  industrial device 
       11  sensor 
       20 ,  20 A diagnostic device 
       21 ,  21   a  control unit 
       211  sensor signal acquisition unit 
       212  device diagnosis unit 
       213  classification unit 
       214 ,  214   a  transmission unit 
       215  display control unit 
       216 ,  216   a  label information generation unit 
       22  display unit 
       30 ,  30 A server 
       31  classification model learning unit 
       32  classification unit