Material conveying equipment motor state monitoring method, medium and electronic equipment

Abstract

The invention provides a state monitoring method for a motor of material conveying equipment, a medium and electronic equipment. The state monitoring method comprises the steps that S1, the data analysis server obtains current data in real time, wherein the current data is current data of a motor of the material conveying equipment; s2, the data analysis server performs real-time classification processing on the current data through a clustering algorithm to obtain one-hot codes after classification of the current data, if a classification result represented by the one-hot codes is no fault, the step S1 is returned, and otherwise, the step S3 is continued to be executed; and S3, the data analysis server obtains the fault type of the motor of the material conveying equipment in real time based on the one-hot code. The state monitoring method can realize real-time fault monitoring of the motor of the material conveying equipment and has the advantage of low cost.

Description

Technical Field

[0001] The present application belongs to the technical field of industrial conveying equipment, and relates to a method for monitoring the status of a motor of a material conveying equipment, and in particular to a method, medium and electronic equipment for monitoring the status of a motor of a material conveying equipment. Background Art

[0002] As the main equipment for material transfer, the normal operation of material conveying equipment has an important impact on the efficiency of material transfer. During the material transfer process, the motor of the material conveying equipment may fail, causing the equipment to shut down and seriously affecting the efficiency of material transfer. Therefore, in order to improve the efficiency of material transfer, it is necessary to monitor the motor of the material conveying equipment for faults.

[0003] At present, the condition monitoring of the motor of material conveying equipment is generally carried out by installing a large number of vibration sensors, and the fault can only be detected after it occurs. Therefore, the current condition monitoring method cannot analyze the condition of the motor of material conveying equipment in real time. Summary of the invention

[0004] The purpose of the present application is to provide a method, medium and electronic equipment for monitoring the state of the motor of a material conveying equipment, so as to solve the problem that the current method for monitoring the state of the motor of a material conveying equipment cannot analyze the state of the motor of the material conveying equipment in real time.

[0005] In a first aspect, the present application provides a condition monitoring method for a motor of a material conveying equipment, which is applied to a condition monitoring platform, wherein the condition monitoring platform includes a data analysis server, and the condition monitoring method includes: step S1: the data analysis server obtains current data in real time, and the current data is the current data of the motor of the material conveying equipment; step S2: the data analysis server performs real-time classification processing on the current data through a clustering algorithm, and obtains a unique hot encoding after classification of the current data. If the classification result represented by the unique hot encoding is no fault, it returns to S1, otherwise it continues to execute S3; step S3: the data analysis server obtains the fault type of the motor of the material conveying equipment in real time based on the unique hot encoding.

[0006] In the state monitoring method, by real-time classification of the current data acquired in real time and real-time acquisition of the fault type of the material conveying equipment motor based on the unique hot coding, real-time fault monitoring of the material conveying equipment motor can be achieved. In addition, the state monitoring method can achieve real-time fault monitoring of the material conveying equipment motor without introducing additional hardware equipment, and has the advantage of low cost.

[0007] In one embodiment of the present application, the current data includes current data at the moment when the motor of the material conveying equipment is started, current data during the speed change process of the motor of the material conveying equipment, and current data during the shutdown process of the motor of the material conveying equipment.

[0008] In one embodiment of the present application, the method for implementing the data analysis server to obtain the fault type of the material conveying equipment motor in real time based on the unique hot coding includes: the data analysis server matches the unique hot coding with the fault rules in the fault rule library of the material conveying equipment motor in real time to obtain the fault rules matched by the unique hot coding; if there are at least two fault rules matched by the unique hot coding, the data analysis server performs real-time reasoning based on the fault rules matched by the unique hot coding to obtain the fault type of the material conveying equipment motor; if there is one fault rule matched by the unique hot coding, the data analysis server obtains the fault type of the material conveying equipment motor based on the fault rule matched by the unique hot coding.

[0009] In one embodiment of the present application, the state monitoring platform also includes: an acquisition server, and the state monitoring method also includes: the acquisition server acquires raw current data in real time, and the raw current data is the original current data of the motor of the material conveying equipment; the acquisition server performs median filtering on the raw current data in real time to obtain the current data, and the current data is expressed as: y[n]=median(x[nk],…,x[n],…,x[n+k]), wherein x is the raw current data, n is the current position, k is the window size, y is the current data, and the median function represents returning the median in the median function data set.

[0010] In one embodiment of the present application, the clustering algorithm is a semi-supervised clustering algorithm, and the training method of the clustering algorithm includes: the data analysis server obtains historical current data; the data analysis server trains an initial semi-supervised clustering algorithm based on the historical current data to obtain the semi-supervised clustering algorithm.

[0011] In one embodiment of the present application, the objective function in the semi-supervised clustering algorithm training process is:

[0012]

[0013] Wherein, L represents the objective function, m represents the number of current data points, i represents the i-th current data point, j represents the j-th current data point, and y i represents the cluster label of the i-th current data point, y j represents the cluster label of the j-th current data point, W ijrepresents the similarity between the i-th current data point and the j-th current data point, λ represents the balance parameter, and L ij represents the relationship between the i-th current data point and the j-th current data point. If the i-th current data point and the j-th current data point have the same cluster label, then L ij is 1 if the value is true, otherwise it is 0.

[0014] In one embodiment of the present application, the method for generating fault rules in the fault rule base includes: the data analysis server classifies the historical current data through the semi-supervised clustering algorithm to obtain the unique hot encoding of the historical current data after classification; the data analysis server generates the fault rules in the fault rule base based on the unique hot encoding of the historical current data after classification.

[0015] In the second aspect, the present application provides a state monitoring device for a motor of a material conveying equipment, which is applied to a state monitoring platform, wherein the state monitoring platform includes: a data analysis server, and the state monitoring device includes: a current data acquisition module, which is used to acquire current data in real time, and the current data is the current data of the motor of the material conveying equipment; a classification processing module, which is used to classify the current data through a clustering algorithm, and obtain the unique hot encoding of the classified current data, if the classification result represented by the unique hot encoding is no fault, it returns to the current data acquisition module, otherwise it continues to be executed by the fault type acquisition module; the fault type acquisition module is used to obtain the fault type of the motor of the material conveying equipment based on the unique hot encoding.

[0016] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the condition monitoring method described in any one of the first aspects of the present application.

[0017] In a fourth aspect, the present application provides an electronic device, comprising: a memory storing a computer program; and a processor communicatively connected to the memory, for executing any of the status monitoring methods described in the first aspect of the present application when the computer program is called.

[0018] As described above, the material conveying equipment motor condition monitoring method, medium and electronic device described in the present application have the following beneficial effects:

[0019] In the state monitoring method, by real-time classification of the current data acquired in real time and real-time acquisition of the fault type of the material conveying equipment motor based on the unique hot coding, real-time fault monitoring of the material conveying equipment motor can be achieved. In addition, the state monitoring method can achieve real-time fault monitoring of the material conveying equipment motor without introducing additional hardware equipment, and has the advantage of low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Shown is a schematic diagram of the structure of the status monitoring platform described in an embodiment of the present application.

[0021] Figure 2 Shown is a flow chart of the state monitoring method described in an embodiment of the present application.

[0022] Figure 3 Shown is a flowchart of a method for implementing the data analysis server described in an embodiment of the present application to obtain the fault type of the motor of the material conveying equipment in real time based on the one-hot encoding.

[0023] Figure 4 Shown is a flow chart of the state monitoring method described in an embodiment of the present application.

[0024] Figure 5 Shown is a flow chart of the training method of the clustering algorithm described in an embodiment of the present application.

[0025] Figure 6 Shown is a flowchart of a method for generating fault rules in a fault rule base according to an embodiment of the present application.

[0026] Figure 7 Shown is a schematic diagram of the structure of the status monitoring device described in an embodiment of the present application.

[0027] Figure 8 Shown is a schematic diagram of the structure of the electronic device described in an embodiment of the present application.

[0028] Component number description

[0029] 110 Collection Server

[0030] 120 Analysis Server

[0031] 700 Material handling equipment motor status

[0032] Monitoring device

[0033] 710 Current Data Acquisition Module

[0034] 720 Classification Processing Module

[0035] 730 Fault type acquisition module

[0036] 800 Electronic equipment

[0037] 810 Memory

[0038] 820 processor

[0039] Steps S1-S3

[0040] Steps S21-S22

[0041] S31-S32 Steps

[0042] Steps S41-S42

[0043] Steps S51-S52 DETAILED DESCRIPTION

[0044] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0045] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0046] The technical solutions in the embodiments of the present application are described in detail below in conjunction with the drawings in the embodiments of the present application.

[0047] See also Figure 1 , Figure 1 1 is a schematic diagram of the structure of the state monitoring platform. The state monitoring platform includes a collection server 110 and a data analysis server 120. The collection server 110 is electrically connected to the motor of the material conveying equipment and is used to collect the motor current. The collection server 110 includes a digital-to-analog conversion module, which is used to process the acquired analog motor current to obtain the digital motor current. Figure 1 The 24 motors in the material conveying equipment are only used to illustrate the structure of the status monitoring platform. This embodiment does not explicitly limit the number of motors in the material conveying equipment and can be flexibly set according to actual conditions.

[0048] Optionally, the acquisition server 110 may refer to a server for acquiring and storing data, which is usually a core component of a data acquisition system and is used to collect data from various data sources, including sensors, instruments, equipment, databases, networks, and files.

[0049] Optionally, the data analysis server 120 may refer to a server for performing large-scale data analysis, which is usually used as a core component of a large-scale data analysis system to store, process and analyze massive amounts of data to extract valuable information and insights.

[0050] See also Figure 2 This embodiment provides a method for monitoring the state of a motor of a material conveying equipment, which is applied to a state monitoring platform. The state monitoring platform includes a data analysis server. The state monitoring method includes:

[0051] Step S1: The data analysis server acquires current data in real time, and the current data is current data of the motor of the material conveying equipment.

[0052] Optionally, the material conveying equipment may be a stacker-reclaimer, the current data of the motor of the material conveying equipment may refer to the current data of the running motor of the material conveying equipment, the running motor of the material conveying equipment may refer to the motor of the running mechanism for controlling the conveyor belt, and the status monitoring platform may be deployed on the running mechanism of the stacker-reclaimer.

[0053] Optionally, the current data includes current data at the moment when the motor of the material conveying equipment is started, current data during a speed change process of the motor of the material conveying equipment, and current data during a shutdown process of the motor of the material conveying equipment.

[0054] Step S2: The data analysis server performs real-time classification processing on the current data through a clustering algorithm to obtain the one-hot encoding of the classified current data. If the classification result represented by the one-hot encoding is fault-free, the server returns to S1, otherwise, the server continues to execute S3.

[0055] One-Hot Encoding is a commonly used categorical data preprocessing method used to convert categorical data into digital data. One-Hot Encoding maps each categorical variable to a unique binary vector, where the length of the vector is equal to the number of values ​​of the categorical variable. In one-hot encoding, only one element of each categorical variable is 1, and the rest are 0, indicating that the sample belongs to the corresponding category. For example, for a categorical variable with 3 categories, the one-hot encoded vector may be [1,0,0], [0,1,0], or [0,0,1].

[0056] Optionally, the one-hot encoding after the current data classification may refer to the digital data after the current data is classified by the clustering algorithm. For example, the one-hot encoding may be expressed as The value of the first row of the one-hot encoding can represent the classification of the degree of fault. For example, the current data can be divided into three categories: no fault, slight fault and severe fault. When there is no fault, the first row of the one-hot encoding can be (0, 0, 1), when there is a slight fault, the first row of the one-hot encoding can be (1, 0, 0), and when there is a severe fault, the first row of the one-hot encoding can be (0, 1, 0). The value of the second row of the one-hot encoding can represent the classification of the cause of the fault. For example, after the first classification, the current data is classified as having a slight fault. The current data is further classified for the second time. The cause of the fault can be divided into three categories: cause A, cause B and cause C. When the value of the second row of the one-hot encoding is (1, 0, 0), the classification result is cause A. When the value of the second row of the one-hot encoding is (0, 1, 0), the classification result is cause B. When the value of the second row of the one-hot encoding is (0, 0, 1), the classification result is cause C. The value of the third row of the one-hot encoding can represent the classification of the fault type. The fault type can be divided into three categories: fault type a, fault type b, and fault type c. After the current data undergoes the second classification, the current data is classified for the third time. When the value of the third row of the one-hot encoding is (1, 0, 0), it means that the classification result is fault type a. When the value of the third row of the one-hot encoding is (0, 1, 0), it means that the classification result is fault type b. When the value of the third row of the one-hot encoding is (0, 0, 1), it means that the classification result is fault type c. The one-hot encoding indicates that there is a minor fault, the fault cause B, and the fault type a.

[0057] Step S3: the data analysis server obtains the fault type of the motor of the material conveying equipment in real time based on the one-hot encoding.

[0058] Optionally, the fault type of the material conveying equipment motor may refer to the deviation motor fault type of the material conveying equipment, and the fault type of the material conveying equipment motor may include any one or more of: motor stator fault, motor rotor fault, motor short circuit, motor insulation degradation, etc.

[0059] Optionally, the state monitoring method further includes: obtaining the current of each motor in the material conveying equipment; and comparing the current of each motor to determine the abnormal motor. Since the material conveying equipment may generally have 24 or 32 running motors, etc., the failure of a single motor generally does not affect the overall running function. By comparing the current of each motor, the abnormal motor, i.e. the faulty motor, can be quickly located, thereby improving the overall monitoring efficiency.

[0060] According to the above description, the state monitoring method of the motor of the material conveying equipment described in this embodiment is applied to a state monitoring platform, and the state monitoring platform includes a data analysis server. The state monitoring method includes: Step S1: The data analysis server obtains current data in real time, and the current data is the current data of the motor of the material conveying equipment; Step S2: The data analysis server performs real-time classification processing on the current data through a clustering algorithm, and obtains the unique hot encoding after the classification of the current data. If the classification result represented by the unique hot encoding is no fault, return to S1, otherwise continue to execute S3; Step S3: The data analysis server obtains the fault type of the motor of the material conveying equipment in real time based on the unique hot encoding.

[0061] In the state monitoring method, by real-time classification of the current data acquired in real time and real-time acquisition of the fault type of the material conveying equipment motor based on the unique hot coding, real-time fault monitoring of the material conveying equipment motor can be achieved. In addition, the state monitoring method can achieve real-time fault monitoring of the material conveying equipment motor without introducing additional hardware equipment, and has the advantage of low cost.

[0062] See also Figure 3 This embodiment provides a method for the data analysis server to obtain the fault type of the motor of the material conveying equipment in real time based on the one-hot encoding, including:

[0063] S21, the data analysis server matches the one-hot encoding with the fault rules in the fault rule library of the motor of the material conveying equipment in real time to obtain the fault rules matched by the one-hot encoding.

[0064] Optionally, the fault rule refers to a knowledge representation form used to describe the type, cause and solution of equipment faults, and the fault rule base may refer to the database that stores and manages the motor fault information of the material conveying equipment, and may contain information such as the type, cause, solution and the like of the motor fault of the material conveying equipment.

[0065] S22, if there are at least two fault rules matched by the unique hot encoding, the data analysis server performs real-time reasoning based on the fault rules matched by the unique hot encoding to obtain the fault type of the material conveying equipment motor; if there is one fault rule matched by the unique hot encoding, the data analysis server obtains the fault type of the material conveying equipment motor based on the fault rule matched by the unique hot encoding.

[0066] For example, if the fault rules matched by the unique hot coding have two rules, namely, "the fault type may be a motor stator fault or a motor rotor fault", the data analysis server can infer based on the fault rules matched by the unique hot coding, and specifically exclude and verify "motor stator fault" and "motor rotor fault" to determine the real cause of the fault. When the fault rule matched by the unique hot coding has only one rule, the fault type of the motor of the material conveying equipment can be directly obtained.

[0067] See also Figure 4 This embodiment provides a state monitoring method, wherein the state monitoring platform further includes: a collection server, and the state monitoring method further includes:

[0068] S31, the acquisition server acquires raw current data in real time, the raw current data being the original current data of the motor of the material conveying equipment;

[0069] S32, the acquisition server performs median filtering on the original current data in real time to obtain the current data, and the current data is expressed as: y[n]=median(x[nk],…,x[n],…,x[n+k]), wherein x is the original current data, n is the current position, k is the window size, y is the current data, and the median function represents returning the median value in the median function data set.

[0070] Optionally, the current position may refer to a current sampling point in the current data, and the window may refer to a set of data points within a certain range around the current sampling point for calculating the median. The window size may be an odd number. The median function data set may refer to x[nk],…,x[n],…,x[n+k].

[0071] See also Figure 5 This embodiment provides a training method for the clustering algorithm, where the clustering algorithm is a semi-supervised clustering algorithm, including:

[0072] S41, the data analysis server obtains historical current data.

[0073] Optionally, the historical current data may refer to current data of the motor of the material conveying equipment at historical moments, and the current data at historical moments may refer to data of the motor current of the motor of the material conveying equipment at historical moments after being processed by median filtering.

[0074] S42, the data analysis server trains an initial semi-supervised clustering algorithm based on the historical current data to obtain the semi-supervised clustering algorithm.

[0075] Optionally, the initial semi-supervised clustering algorithm may refer to an untrained semi-supervised clustering algorithm.

[0076] Optionally, the objective function in the semi-supervised clustering algorithm training process is:

[0077]

[0078] Wherein, L represents the objective function, m represents the number of current data points, i represents the i-th current data point, j represents the j-th current data point, and y i represents the cluster label of the i-th current data point, y j represents the cluster label of the j-th current data point, W ij represents the similarity between the i-th current data point and the j-th current data point, λ represents the balance parameter, and L ij represents the relationship between the i-th current data point and the j-th current data point. If the i-th current data point and the j-th current data point have the same cluster label, then L ij is 1, otherwise it is 0. The current data point may be the current data at each moment in the historical current data. For example, the historical current data includes: the current data of the past minute. The current data points may be 60, that is, one current data point corresponds to one current data point per second of current data. The specific value of λ may be flexibly set according to actual conditions, and this embodiment does not explicitly limit this.

[0079] See also Figure 6 , the method for generating the fault rules in the fault rule base includes:

[0080] S51, the data analysis server classifies the historical current data by using the semi-supervised clustering algorithm to obtain a unique hot encoding of the classified historical current data.

[0081] S52, the data analysis server generates fault rules in the fault rule library based on the one-hot encoding of the classified historical current data.

[0082] Optionally, the method for generating the fault rules in the fault rule base based on the unique hot coding of the classified historical current data includes: generating the fault rules in the fault rule base based on the rule engine and the unique hot coding of the classified historical current data. The content of the rule engine is not repeated in this embodiment.

[0083] The protection scope of the status monitoring method described in the embodiment of the present application is not limited to the execution order of the steps listed in the present embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0084] like Figure 7 As shown, this embodiment provides a state monitoring device 700, which is applied to a state monitoring platform. The state monitoring platform includes a data analysis server. The state monitoring device 700 includes:

[0085] The current data acquisition module 710 is used to acquire current data in real time, where the current data is the current data of the motor of the material conveying equipment.

[0086] The classification processing module 720 is used to perform real-time classification processing on the current data through a clustering algorithm, obtain the one-hot encoding of the classified current data, and return to the current data acquisition module if the classification result represented by the one-hot encoding is fault-free, otherwise the fault type acquisition module continues to execute.

[0087] The fault type acquisition module 730 is used to acquire the fault type of the motor of the material conveying equipment in real time based on the one-hot encoding.

[0088] In the state monitoring device 700 provided in this embodiment, the current data acquisition module and Figure 2 The classification processing module 720 corresponds to step S1 of the state monitoring method shown in FIG. Figure 2 The fault type acquisition module 730 corresponds to step S2 of the state monitoring method shown in FIG. Figure 2 The step S3 of the status monitoring method shown corresponds to that shown in FIG.

[0089] In the several embodiments provided in the present application, it should be understood that the disclosed device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0090] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0091] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0092] This embodiment provides an electronic device, the electronic device comprising a memory storing a computer program; a processor connected to the memory for communication, and executing the computer program when the computer program is called. Figure 2 The condition monitoring method shown.

[0093] The present application embodiment also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by a program to instruct the processor, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state hard disk (SSD)), etc.

[0094] The present application embodiment may also provide a computer program product, the computer program product including one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiment of the present application is generated in whole or in part. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer or data center.

[0095] When the computer program product is executed by a computer, the computer executes the method described in the above method embodiment. The computer program product may be a software installation package, and when the above method is required, the computer program product may be downloaded and executed on a computer.

[0096] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0097] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for monitoring the state of a motor of a material conveying equipment, characterized in that: Applied to a condition monitoring platform, the condition monitoring platform includes a data analysis server, and the condition monitoring method includes: Step S1: the data analysis server acquires current data in real time, the current data being current data of the motor of the material conveying equipment; Step S2: the data analysis server performs real-time classification processing on the current data through a clustering algorithm to obtain the one-hot encoding of the classified current data. If the classification result represented by the one-hot encoding is no fault, the server returns to S1, otherwise, the server continues to execute S3; Step S3: the data analysis server obtains the fault type of the motor of the material conveying equipment in real time based on the one-hot encoding.

2. The condition monitoring method according to claim 1, characterized in that: The current data includes current data at the moment of starting the motor of the material conveying equipment, current data during the speed change process of the motor of the material conveying equipment, and current data during the shutdown process of the motor of the material conveying equipment.

3. The condition monitoring method according to claim 1, characterized in that: The method for implementing the data analysis server to obtain the fault type of the motor of the material conveying equipment in real time based on the one-hot encoding includes: The data analysis server matches the one-hot code with the fault rules in the fault rule library of the motor of the material conveying equipment in real time to obtain the fault rules matched by the one-hot code; If there are at least two fault rules matched by the unique hot encoding, the data analysis server performs real-time reasoning based on the fault rules matched by the unique hot encoding to obtain the fault type of the material conveying equipment motor. If there is one fault rule matched by the unique hot encoding, the data analysis server obtains the fault type of the material conveying equipment motor based on the fault rule matched by the unique hot encoding.

4. The condition monitoring method according to claim 1, characterized in that: The state monitoring platform further includes a collection server, and the state monitoring method further includes: The acquisition server acquires raw current data in real time, wherein the raw current data is the raw current data of the motor of the material conveying equipment; The acquisition server performs median filtering on the raw current data in real time to obtain the current data, and the current data is expressed as: y[n]=median(x[nk],…,x[n],…,x[n+k]), where x is the raw current data, n is the current position, k is the window size, y is the current data, and the median function represents returning the median in the median function data set.

5. The condition monitoring method according to claim 1, characterized in that: The clustering algorithm is a semi-supervised clustering algorithm, and the training method of the clustering algorithm includes: The data analysis server acquires historical current data; The data analysis server trains an initial semi-supervised clustering algorithm based on the historical current data to obtain the semi-supervised clustering algorithm.

6. The condition monitoring method according to claim 5, characterized in that: The objective function in the training process of the semi-supervised clustering algorithm is: Wherein, L represents the objective function, m represents the number of current data points, i represents the i-th current data point, j represents the j-th current data point, and y i represents the cluster label of the i-th current data point, y j represents the cluster label of the j-th current data point, W ij represents the similarity between the i-th current data point and the j-th current data point, λ represents the balance parameter, and L ij represents the relationship between the i-th current data point and the j-th current data point. If the i-th current data point and the j-th current data point have the same cluster label, then L ij is 1 if the value is true, otherwise it is 0.

7. The condition monitoring method according to claim 5, characterized in that: The method for generating the fault rules in the fault rule base includes: The data analysis server classifies the historical current data by using the semi-supervised clustering algorithm to obtain a unique hot encoding of the classified historical current data; The data analysis server generates the fault rules in the fault rule library based on the one-hot encoding of the classified historical current data.

8. A state monitoring device for a motor of a material conveying equipment, characterized in that: Applied to a condition monitoring platform, the condition monitoring platform comprises: a data analysis server, the condition monitoring device comprises: A current data acquisition module, used to acquire current data in real time, wherein the current data is the current data of the motor of the material conveying equipment; A classification processing module, used to perform real-time classification processing on the current data through a clustering algorithm, obtain the one-hot encoding of the classified current data, and return to the current data acquisition module if the classification result represented by the one-hot encoding is fault-free, otherwise continue to be executed by the fault type acquisition module; A fault type acquisition module is used to acquire the fault type of the motor of the material conveying equipment in real time based on the one-hot encoding.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the condition monitoring method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device comprises: A memory storing a computer program; A processor is communicatively connected to the memory, and executes the state monitoring method according to any one of claims 1 to 7 when calling the computer program.

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