Bucket wheel machine automatic monitoring and alarming system and method based on Internet of Things

Abstract

The embodiment of the invention relates to the technical field of bucket wheel machine intelligent monitoring, and provides a bucket wheel machine automatic monitoring and alarming system and method based on the Internet of Things. Data acquisition, processing and analysis algorithms based on the Internet of Things and artificial intelligence are used for carrying out time sequence analysis and mutual fusion on operation parameters of the bucket wheel machine, so that the operation state of the bucket wheel machine is automatically judged according to characterization characteristics of the operation parameters of the bucket wheel machine in time sequence; and early warning prompts can be given out in time according to the running state of the bucket wheel machine, so that the fault detection accuracy of the bucket wheel machine is effectively improved, and meanwhile, the reliability and the working efficiency of equipment are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of intelligent monitoring of bucket wheel machines, and in particular to an automatic monitoring and alarm system and method for bucket wheel machines based on the Internet of Things. Background Art

[0002] Bucket wheel excavator is a highly efficient loading and unloading machine used in large dry bulk cargo yards, capable of continuous conveying of stacking and retrieving materials. Bucket wheel excavator is widely used, mainly for stacking and retrieving operations in bulk material storage yards such as ports, docks, metallurgy, cement, etc., and is an indispensable and important equipment in modern industry.

[0003] Bucket wheel excavator is one of the main equipment in coal yard, and its stable operation is crucial to ensure the continuous supply of coal to industrial systems such as thermal power plants. The importance of bucket wheel excavator fault detection lies in the ability to promptly detect and deal with problems that may cause production interruptions, thereby avoiding greater economic losses and safety risks.

[0004] However, the traditional bucket wheel machine fault detection method usually performs regular inspections or regular maintenance on the bucket wheel machine, which cannot achieve real-time monitoring and early warning. Once a bucket wheel machine fails, it may delay the processing time and affect the normal operation of the equipment. In addition, when facing complex faults, the traditional bucket wheel machine fault detection method is often difficult to accurately diagnose the fault, which requires a lot of time and effort to troubleshoot, resulting in low detection efficiency and accuracy. Summary of the invention

[0005] The present disclosure aims to solve at least one of the problems existing in the prior art and provide an automatic monitoring and alarm system and method for a bucket wheel excavator based on the Internet of Things.

[0006] In one aspect of the present disclosure, there is provided an automatic monitoring and alarm system for a bucket wheel machine based on the Internet of Things, the automatic monitoring and alarm system for a bucket wheel machine comprising:

[0007] A bucket wheel machine operating parameter acquisition module is used to obtain a time series of bucket wheel machine operating parameters, wherein the bucket wheel machine operating parameters include a motor temperature value, a motor speed value, and a hydraulic system pressure value of the bucket wheel machine;

[0008] A data regularization module is used to regularize the time series of the bucket wheel machine operating parameters according to the time dimension and the sample dimension to obtain the corresponding motor temperature timing input vector, motor speed timing input vector and hydraulic system pressure timing input vector;

[0009] An operating parameter timing pattern feature extraction module is used to extract the bucket wheel machine operating parameter timing pattern features from the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector, respectively, to obtain the corresponding motor temperature timing pattern feature vector, motor speed timing pattern feature vector and hydraulic system pressure timing pattern feature vector;

[0010] A motor operation state feature fusion module is used to capture and fuse the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector to obtain a motor temperature-speed timing pattern fusion feature vector representing the motor operation state;

[0011] An operating state characterization module is used to perform dependency scale calculation on the motor operating state feature vector represented by the motor temperature-speed timing pattern fusion and the hydraulic system pressure timing pattern feature vector, so as to obtain a bucket wheel machine operating state characterization matrix as a bucket wheel machine operating state characterization feature;

[0012] The recognition result generating module is used to obtain the corresponding recognition result based on the characterization characteristics of the bucket wheel machine operation state, and determine whether to issue a fault warning prompt based on the recognition result.

[0013] Optionally, the operating parameter timing pattern feature extraction module is specifically used to: input the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector into a bucket wheel excavator operating parameter timing pattern feature extractor based on a causal convolutional network, respectively, to obtain the corresponding motor temperature timing pattern feature vector, the motor speed timing pattern feature vector and the hydraulic system pressure timing pattern feature vector.

[0014] Optionally, the motor operating state feature fusion module is specifically used to: input the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector into a motor operating state capturer based on a motor operating parameter timing fusion module to obtain the motor temperature-speed timing pattern fusion feature vector that represents the motor operating state.

[0015] Optionally, the motor operation status feature fusion module includes:

[0016] A characterization feature construction unit, used to construct corresponding motor temperature timing pattern characterization feature vectors and motor speed timing pattern characterization feature vectors based on the motor temperature timing pattern feature vectors and the motor speed timing pattern feature vectors respectively;

[0017] The motor parameter characteristic interaction unit is used to add the motor temperature timing pattern characterization characteristic vector and the motor speed timing pattern characterization characteristic vector by position, so as to obtain the motor temperature-speed timing pattern fusion characterization motor operation state characteristic vector.

[0018] Optionally, the characterization feature construction unit is specifically used to:

[0019] Performing point convolution encoding on the motor temperature timing pattern feature vector to obtain a convolution motor temperature timing pattern feature vector, and multiplying the convolution motor temperature timing pattern feature vector by a first hyperparameter by position to obtain a motor temperature timing pattern characterization feature vector;

[0020] The motor speed timing pattern feature vector is point-convolutionally encoded to obtain a convolution motor speed timing pattern feature vector, and the convolution motor speed timing pattern feature vector is positionally multiplied with a second hyperparameter to obtain the motor speed timing pattern characterization feature vector.

[0021] Optionally, the operation status characterization module includes:

[0022] A motor pressure timing correlation unit, used for calculating the product between the motor temperature-speed timing pattern fusion characterizing the motor operation state feature vector and the transposed vector of the hydraulic system pressure timing pattern feature vector, so as to obtain a motor state pressure timing correlation input matrix;

[0023] A correlation value calculation unit, used for calculating the sum of the elements on the diagonal line of the motor state pressure timing correlation input matrix to obtain the motor state pressure timing correlation value;

[0024] The operating state characterization unit is used to divide the eigenvalues ​​of each position of the motor state pressure timing correlation input matrix by the motor state pressure timing correlation value to obtain the bucket wheel machine operating state characterization matrix composed of multiple motor state pressure timing correlation trace coefficients.

[0025] Optionally, the recognition result generating module includes:

[0026] A fault judgment unit, used for inputting the bucket wheel machine operation state characterization matrix into a bucket wheel machine fault identifier based on a classifier to obtain the corresponding identification result, wherein the identification result is used to indicate whether the bucket wheel machine has a fault;

[0027] The early warning prompt generating unit is used to issue the fault early warning prompt in response to the recognition result indicating that the bucket wheel machine has a fault.

[0028] Optionally, the bucket wheel machine automatic monitoring and alarm system further includes a training module;

[0029] The training module is used to:

[0030] When the operating parameter timing pattern feature extraction module is specifically used to input the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector into the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolution network respectively, the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolution network is trained;

[0031] When the motor operation state feature fusion module is specifically used to input the motor temperature timing mode feature vector and the motor speed timing mode feature vector into the motor operation state capturer based on the motor operation parameter timing fusion module, the motor operation state capturer based on the motor operation parameter timing fusion module is trained;

[0032] When the recognition result generation module includes a fault judgment unit, the classifier-based bucket wheel machine fault identifier is trained.

[0033] Optionally, the training module includes:

[0034] A training data acquisition unit, used to acquire training data and a true value of whether the bucket wheel machine has a fault; wherein the training data includes a time series of bucket wheel machine operation training parameters, and the bucket wheel machine operation training parameters include a training motor temperature value, a training motor speed value, and a training hydraulic system pressure value of the bucket wheel machine;

[0035] A training data regularization unit is used to regularize the time series of the bucket wheel machine operation training parameters according to the time dimension and the sample dimension to obtain a training motor temperature timing input vector, a training motor speed timing input vector and a training hydraulic system pressure timing input vector;

[0036] A training parameter timing pattern feature extraction unit, used to input the training motor temperature timing input vector, the training motor speed timing input vector and the training hydraulic system pressure timing input vector into the bucket wheel excavator operating parameter timing pattern feature extractor based on the causal convolutional network, respectively, to obtain corresponding training motor temperature timing pattern feature vector, training motor speed timing pattern feature vector and training hydraulic system pressure timing pattern feature vector;

[0037] A training parameter timing fusion unit, used for inputting the training motor temperature timing pattern feature vector and the training motor speed timing pattern feature vector into the motor operation state capturer based on the motor operation parameter timing fusion module, so as to obtain a training motor temperature-speed timing pattern fusion feature vector representing the motor operation state;

[0038] A training feature association unit is used to perform dependency scale calculation on the training motor temperature-speed timing pattern fusion characterization motor operation state feature vector and the training hydraulic system pressure timing pattern feature vector to obtain a training bucket wheel machine operation state characterization matrix;

[0039] A classification loss unit, used for inputting the training bucket wheel machine operation state characterization matrix into the bucket wheel machine fault identifier based on the classifier to obtain a prediction value of whether the bucket wheel machine has a fault, and determining a classification loss function value according to the prediction value of whether the bucket wheel machine has a fault and a true value of whether the bucket wheel machine has a fault;

[0040] A training unit is used to train the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolutional network, the motor operating state capturer based on the motor operating parameter timing fusion module, and the bucket wheel machine fault identifier based on the classifier based on the classification loss function value and through back propagation of gradient descent.

[0041] Another aspect of the present disclosure provides an automatic monitoring and alarm method for a bucket wheel machine based on the Internet of Things, the automatic monitoring and alarm method for a bucket wheel machine comprising:

[0042] Acquire a time series of bucket wheel machine operating parameters, wherein the bucket wheel machine operating parameters include a motor temperature value, a motor speed value, and a hydraulic system pressure value of the bucket wheel machine;

[0043] Regularize the time series of the bucket wheel machine operating parameters according to the time dimension and the sample dimension to obtain the corresponding motor temperature timing input vector, motor speed timing input vector and hydraulic system pressure timing input vector;

[0044] Extracting bucket wheel machine operation parameter timing pattern features from the motor temperature timing input vector, the motor speed timing input vector, and the hydraulic system pressure timing input vector, respectively, to obtain corresponding motor temperature timing pattern feature vectors, motor speed timing pattern feature vectors, and hydraulic system pressure timing pattern feature vectors;

[0045] Capturing and fusing the motor operating state of the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector to obtain a motor temperature-speed timing pattern fusion feature vector representing the motor operating state;

[0046] The motor temperature-speed timing pattern is fused to represent the motor operating state feature vector and the hydraulic system pressure timing pattern feature vector to perform dependency scale calculation to obtain a bucket wheel machine operating state representation matrix as a bucket wheel machine operating state representation feature;

[0047] Based on the characteristics characterizing the bucket wheel excavator's operating status, a corresponding recognition result is obtained, and based on the recognition result, it is determined whether to issue a fault warning prompt.

[0048] Compared with the prior art, the present invention obtains the time series of bucket wheel machine operating parameters, and utilizes data collection, processing and analysis algorithms based on the Internet of Things and artificial intelligence to perform time series analysis and mutual integration of the bucket wheel machine operating parameters, thereby automatically judging the operating status of the bucket wheel machine according to the characterization characteristics of each operating parameter of the bucket wheel machine in the time series, and can issue early warning prompts in time according to the operating status of the bucket wheel machine, thereby effectively improving the accuracy of bucket wheel machine fault detection, and at the same time improving the reliability and work efficiency of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0050] Figure 1 A schematic diagram of the structure of an automatic monitoring and alarm system for a bucket wheel excavator based on the Internet of Things provided in one embodiment of the present disclosure;

[0051] Figure 2 A schematic diagram of data flow of an automatic monitoring and alarm system for a bucket wheel machine based on the Internet of Things provided by another embodiment of the present disclosure;

[0052] Figure 3 A structural block diagram of a motor operation status feature fusion module in an automatic monitoring and alarm system for a bucket wheel machine based on the Internet of Things provided by another embodiment of the present disclosure;

[0053] Figure 4 A structural block diagram of an operating status characterization module in an automatic monitoring and alarm system for a bucket wheel machine based on the Internet of Things provided by another embodiment of the present disclosure;

[0054] Figure 5 A structural block diagram of a training module in an automatic monitoring and alarm system for a bucket wheel machine based on the Internet of Things provided by another embodiment of the present disclosure;

[0055] Figure 6 A flowchart of an automatic monitoring and alarm method for a bucket wheel excavator based on the Internet of Things provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the embodiments of the present disclosure will be described in detail below in conjunction with the accompanying drawings. However, it can be understood by those skilled in the art that in each embodiment of the present disclosure, many technical details are proposed in order to enable readers to better understand the present disclosure. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed for protection in the present disclosure can also be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present disclosure. The various embodiments can be combined and referenced with each other without contradiction.

[0057] Unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0058] Although the present disclosure makes various references to certain modules in the system according to embodiments of the present disclosure, any number of different modules can be used and run on a user terminal and / or server. The modules are illustrative only, and different aspects of the systems and methods can use different modules.

[0059] The present disclosure uses a flow chart to illustrate the operations performed according to the method of the embodiments of the present disclosure. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0060] In the existing technology, the Internet of Things can collect information about objects or processes that need to be monitored, connected, and interacted in real time through various information sensors, radio frequency identification technology, global positioning system and other technologies, and realize ubiquitous connection between objects and objects, and objects and people. In the automatic monitoring and alarm system of bucket wheel machine, the Internet of Things technology plays a vital role. For example, by installing sensors and monitoring devices, bucket wheel machine can realize functions such as automatic addressing, automatic coal panning, and automatic stacking and reclaiming, which improves work efficiency and safety. For another example, using the Internet of Things technology, managers can view the operating status and operation data of bucket wheel machine in real time in the remote monitoring center. Therefore, the application of the Internet of Things in bucket wheel machine monitoring not only improves the accuracy and efficiency of bucket wheel machine operation, but also provides strong support for modern industrial automation.

[0061] Based on this, in view of the problems existing in the traditional bucket wheel machine fault detection method, the present disclosure provides an automatic monitoring and alarm system and method for bucket wheel machine based on the Internet of Things, and its technical concept is: obtaining the time series of bucket wheel machine operating parameters, wherein the bucket wheel machine operating parameters include motor temperature value, motor speed value and hydraulic system pressure value, and using the data acquisition, processing and analysis algorithm based on the Internet of Things and artificial intelligence to perform time series analysis and mutual fusion of bucket wheel machine operating parameters, so as to automatically judge whether the bucket wheel machine has a fault according to the time series characterization characteristics of each operating parameter of the bucket wheel machine, and determine whether to issue a corresponding fault warning prompt based on whether the bucket wheel machine has a fault. In this way, the automatic monitoring and alarm system of the bucket wheel machine can automatically judge the operating status of the bucket wheel machine and issue a warning prompt in time, thereby improving the accuracy of bucket wheel machine fault detection, and at the same time improving the reliability and work efficiency of the equipment.

[0062] One embodiment of the present disclosure relates to an automatic monitoring and alarm system 100 for a bucket wheel machine based on the Internet of Things, and its structure is as follows: Figure 1 As shown, the data flow is as follows Figure 2 shown.

[0063] Combined Figure 1 and Figure 2 The bucket wheel machine automatic monitoring and alarm system 100 based on the Internet of Things provided in this embodiment includes a bucket wheel machine operating parameter acquisition module 110, a data regularization module 120, an operating parameter time series pattern feature extraction module 130, a motor operating state feature fusion module 140, an operating state characterization module 150, and an identification result generation module 160.

[0064] The bucket wheel machine operating parameter acquisition module 110 is used to obtain the time series of bucket wheel machine operating parameters, and the bucket wheel machine operating parameters include the motor temperature value, motor speed value and hydraulic system pressure value of the bucket wheel machine.

[0065] Specifically, the bucket wheel machine operation parameter acquisition module 110 can obtain the time series of bucket wheel machine operation parameters by acquiring the time series of bucket wheel machine operation parameters collected by the sensor group deployed in the bucket wheel machine. Considering that the motor temperature value, motor speed value and hydraulic system pressure value are three key operation parameters of the bucket wheel machine, in order to help monitor the operation status of the bucket wheel machine to ensure that the equipment operates according to the predetermined performance and safety standards, the bucket wheel machine operation parameters in this embodiment include the motor temperature value, motor speed value and hydraulic system pressure value of the bucket wheel machine. Among them, the motor temperature value refers to the temperature generated by the motor in the bucket wheel machine during operation, which is an important indicator for evaluating whether the motor is overheated. Overheating of the motor may cause damage to the motor or reduce its efficiency. The motor speed value refers to the rotation speed of the motor in the bucket wheel machine, which is usually measured in revolutions per minute (RPM), which is related to the operating speed and output of the bucket wheel machine. The hydraulic system pressure value refers to the pressure level in the hydraulic system of the bucket wheel machine. The hydraulic system of the bucket wheel machine is responsible for providing power and performing various mechanical actions, and its pressure value is an important indicator of the normal operation and load capacity of the hydraulic system of the bucket wheel machine.

[0066] The data regularization module 120 is used to regularize the time series of bucket wheel machine operating parameters according to the time dimension and the sample dimension to obtain the corresponding motor temperature timing input vector, motor speed timing input vector and hydraulic system pressure timing input vector.

[0067] Specifically, considering that the bucket wheel machine operating parameters all have characteristic information in the time dimension, and the motor temperature value, motor speed value and hydraulic system pressure value are different types of physical parameters, i.e., different sample dimensions, which have different characteristic information in time series, therefore, in order to retain the time order of the bucket wheel machine operating parameters to capture the dynamic characteristics of each parameter data in the time dimension, the data regularization module 120 performs data regularization on the time series of the bucket wheel machine operating parameters according to the time dimension and the sample dimension to obtain the corresponding motor temperature timing input vector, motor speed timing input vector and hydraulic system pressure timing input vector, so that each bucket wheel machine operating parameter can be independently analyzed for its timing characteristics, so as to more accurately capture and extract the timing fluctuations and periodic changes in each operating parameter.

[0068] The operating parameter timing pattern feature extraction module 130 is used to extract the bucket wheel excavator operating parameter timing pattern features of the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector respectively, so as to obtain the corresponding motor temperature timing pattern feature vector, the motor speed timing pattern feature vector and the hydraulic system pressure timing pattern feature vector.

[0069] Exemplarily, the operating parameter timing pattern feature extraction module 130 is specifically used to: input the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector into the bucket wheel excavator operating parameter timing pattern feature extractor based on the causal convolutional network, so as to obtain the corresponding motor temperature timing pattern feature vector, motor speed timing pattern feature vector and hydraulic system pressure timing pattern feature vector.

[0070] Specifically, considering that the motor temperature value, the motor speed value and the hydraulic system pressure value are all collected and obtained in chronological order, that is, the data value at the current time point is affected by the previous time point and has nothing to do with the future time, therefore, in order to be able to capture the important causal pattern characteristics of time from the motor temperature value, the motor speed value and the hydraulic system pressure value, the operating parameter timing pattern feature extraction module 130 is specifically used to input the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector into the bucket wheel excavator operating parameter timing pattern feature extractor based on the causal convolutional network to obtain the corresponding motor temperature timing pattern feature vector, motor speed timing pattern feature vector and hydraulic system pressure timing pattern feature vector.

[0071] Among them, the causal convolutional network is a convolutional network that takes into account the temporal causal relationship and can maintain the causality of time series data, that is, in time series data, past data will affect future data. Therefore, the use of causal convolution can better capture this causal relationship. Specifically, the bucket wheel machine operating parameter time series pattern feature extractor based on the causal convolutional network can better capture and mine the important time series causal pattern features in the motor temperature time series input vector, the motor speed time series input vector and the hydraulic system pressure time series input vector, thereby helping to understand the regularity and time series characteristics of the bucket wheel machine operating parameters.

[0072] The motor operation state feature fusion module 140 is used to capture and fuse the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector to obtain the motor temperature-speed timing pattern fusion feature vector representing the motor operation state.

[0073] Exemplarily, the motor operating state feature fusion module 140 is specifically used to: input the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector into the motor operating state capturer based on the motor operating parameter timing fusion module to obtain the motor temperature-speed timing pattern fusion feature vector representing the motor operating state.

[0074] Specifically, considering that the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector respectively represent the characteristic information of the motor temperature and the motor speed on the time scale, and there is interrelated interactive information about the motor operating state between the motor temperature and the motor speed, therefore, in order to more comprehensively and comprehensively characterize the timing characteristic information of the motor operating state, so as to facilitate more accurate fault judgment in the subsequent process, the motor operating state feature fusion module 140 is specifically used to input the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector into the motor operating state capturer based on the motor operating parameter timing fusion module for fusion, so as to comprehensively consider the pattern characteristics of the motor temperature and the motor speed in the timing, and generate a more comprehensive motor temperature-speed timing pattern fusion representation of the motor operating state feature vector for representing the overall state of the motor based on the characteristic correlation between the motor temperature and the motor speed in the timing pattern, so as to integrate different aspects of information about the motor working state, thereby capturing the comprehensive operating state timing characteristics of the motor, improving the grasp of the motor operating state, and thus achieving more accurate fault prediction in the subsequent process.

[0075] For example, Figure 3 As shown, the motor operation state feature fusion module 140 includes a characterization feature construction unit 141 and a motor parameter feature interaction unit 142 .

[0076] The characterization feature construction unit 141 is used to construct corresponding motor temperature timing pattern characterization feature vectors and motor speed timing pattern characterization feature vectors based on the motor temperature timing pattern feature vectors and the motor speed timing pattern feature vectors.

[0077] The motor parameter feature interaction unit 142 is used to add the motor temperature timing pattern characterization feature vector and the motor speed timing pattern characterization feature vector by position, so as to obtain the motor temperature-speed timing pattern fusion characterization motor operation state feature vector.

[0078] Among them, the characterization feature construction unit 141 is specifically used for: performing point convolution encoding on the motor temperature timing pattern feature vector to obtain the convolution motor temperature timing pattern feature vector, and multiplying the convolution motor temperature timing pattern feature vector by the first hyperparameter by position to obtain the motor temperature timing pattern characterization feature vector; performing point convolution encoding on the motor speed timing pattern feature vector to obtain the convolution motor speed timing pattern feature vector, and multiplying the convolution motor speed timing pattern feature vector by the second hyperparameter by position to obtain the motor speed timing pattern characterization feature vector.

[0079] In other words, the motor operation state feature fusion module 140 can be specifically used to: use the motor operation state capturer based on the motor operation parameter timing fusion module to fuse the motor temperature timing mode feature vector and the motor speed timing mode feature vector with the following fusion formula to obtain the motor temperature-speed timing mode fusion characterization motor operation state feature vector, wherein the fusion formula is:

[0080] V c =α*Conv 1×1 (V 1 )+β*Conv 1×1 (V 2 )

[0081] Among them, V 1 、V 2 They represent the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector respectively, Conv 1×1 (·) indicates point convolution encoding of the vector in the brackets, α and β represent the first and second hyperparameters respectively, V c It represents the characteristic vector of the motor operating state represented by the fusion of the motor temperature-speed timing pattern.

[0082] The operating state characterization module 150 is used to perform dependency scale calculation on the motor operating state feature vector and the hydraulic system pressure timing pattern feature vector by fusing the motor temperature-speed timing pattern to obtain the bucket wheel machine operating state characterization matrix as the bucket wheel machine operating state characterization feature.

[0083] Specifically, considering that the motor temperature-speed timing pattern fusion characterization motor operating state feature vector contains comprehensive timing feature information of motor temperature and motor speed, it can more comprehensively describe the motor operating state, and the hydraulic system pressure is also one of the important parameters for bucket wheel machine operation. The hydraulic system pressure timing pattern feature vector can provide important timing information about the hydraulic system state. At the same time, there are some dependency relationships between the motor operating state characteristics and the hydraulic system pressure timing pattern characteristics. For example, in some cases, changes in motor speed may cause fluctuations in hydraulic system pressure, thereby reflecting changes in the overall working state of the bucket wheel machine. Therefore, in order to better understand and describe the operating state of the bucket wheel machine, and thus obtain a more comprehensive and accurate representation of the operating state of the bucket wheel machine, the operating state characterization module 150 is used to perform dependency scale calculations on the motor temperature-speed timing pattern fusion characterization motor operating state feature vector and the hydraulic system pressure timing pattern feature vector to obtain the bucket wheel machine operating state characterization matrix. Through dependency scale calculation, the temporal correlation and interdependence between different operating parameters of bucket wheel machine can be quantified, which helps to more comprehensively understand the overall operating status of bucket wheel machine, so as to form a more comprehensive and global bucket wheel machine operating status characterization matrix, provide stronger support for subsequent fault diagnosis analysis and decision-making, and help to discover potential problems or abnormal situations.

[0084] For example, Figure 4 As shown, the operating state characterization module 150 includes a motor pressure timing association unit 151 , an association value calculation unit 152 , and an operating state characterization unit 153 .

[0085] The motor pressure timing association unit 151 is used to calculate the product of the motor temperature-speed timing pattern fusion characterizing the motor operation state feature vector and the transposed vector of the hydraulic system pressure timing pattern feature vector to obtain the motor state pressure timing association input matrix.

[0086] The correlation value calculation unit 152 is used to calculate the sum of the elements on the diagonal line of the motor state pressure timing correlation input matrix to obtain the motor state pressure timing correlation value.

[0087] The operating state characterization unit 153 is used to divide the eigenvalues ​​of each position of the motor state pressure timing correlation input matrix by the motor state pressure timing correlation value to obtain a bucket wheel machine operating state characterization matrix composed of multiple motor state pressure timing correlation trace coefficients.

[0088] In other words, the operating state characterization module 150 can be specifically used to: use the following correlation formula to perform dependency scale calculation on the motor temperature-speed timing pattern fusion characterization motor operating state feature vector and the hydraulic system pressure timing pattern feature vector to obtain the bucket wheel machine operating state characterization matrix, wherein the correlation formula is:

[0089]

[0090] Among them, V c The motor temperature-speed timing pattern fusion represents the motor operating state feature vector, V s represents the hydraulic system pressure time series pattern eigenvector, tr(·) represents the sum of the elements on the diagonal of the matrix in the brackets, M V Represents the bucket wheel machine operating state characterization matrix.

[0091] The recognition result generation module 160 is used to obtain corresponding recognition results based on the bucket wheel machine operation status characterization features, and determine whether to issue a fault warning prompt based on the recognition results.

[0092] Exemplarily, the recognition result generation module 160 includes a fault judgment unit and an early warning prompt generation unit.

[0093] The fault judgment unit is used to input the bucket wheel machine operation state characterization matrix into the bucket wheel machine fault identifier based on the classifier to obtain the corresponding identification result, and the identification result is used to indicate whether the bucket wheel machine has a fault.

[0094] The early warning prompt generating unit is used for issuing a fault early warning prompt in response to the recognition result indicating that the bucket wheel machine has a fault.

[0095] Specifically, the recognition result generation module 160 uses the motor temperature-speed timing pattern fusion characterization motor operating state feature vector and the hydraulic system pressure timing pattern feature vector to perform dependency calculation to obtain the bucket wheel machine operating state characterization feature for classification processing, automatically determines whether the bucket wheel machine has a fault based on the classification result, and issues a corresponding fault warning prompt when the bucket wheel machine has a fault. In this way, the bucket wheel machine automatic monitoring alarm system 100 can automatically determine the operating state of the bucket wheel machine and issue a warning prompt in time when the bucket wheel machine fails, which not only improves the accuracy of bucket wheel machine fault detection, but also improves the reliability and work efficiency of the equipment.

[0096] It should be noted that before applying the deep neural network model for inference, the deep neural network model needs to be trained first so that the deep neural network can realize specific functional functions. To this end, the automatic monitoring and alarm system 100 for bucket wheel machine based on the Internet of Things also includes a training module 200. The training module 200 is used for: when the operating parameter timing pattern feature extraction module is specifically used to input the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector into the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolution network, the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolution network is trained; when the motor operating state feature fusion module is specifically used to input the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector into the motor operating state capturer based on the motor operating parameter timing fusion module, the motor operating state capturer based on the motor operating parameter timing fusion module is trained; when the recognition result generation module includes a fault judgment unit, the bucket wheel machine fault identifier based on the classifier is trained. That is, the training module 200 is used to train the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolutional network, the motor operating state capturer based on the motor operating parameter timing fusion module, and the bucket wheel machine fault identifier based on the classifier in the above-mentioned implementation.

[0097] For example, Figure 5 As shown, the training module 200 includes a training data acquisition unit 210, a training data regularization unit 220, a training parameter time series pattern feature extraction unit 230, a training parameter time series fusion unit 240, a training feature association unit 250, a classification loss unit 260, and a training unit 270.

[0098] The training data acquisition unit 210 is used to obtain the training data and the real value of whether the bucket wheel machine has a fault; wherein the training data includes the time series of the bucket wheel machine operation training parameters, and the bucket wheel machine operation training parameters include the bucket wheel machine's training motor temperature value, training motor speed value and training hydraulic system pressure value.

[0099] The training data regularization unit 220 is used to regularize the time series of bucket wheel machine operation training parameters according to the time dimension and the sample dimension to obtain the training motor temperature timing input vector, the training motor speed timing input vector and the training hydraulic system pressure timing input vector.

[0100] The training parameter timing pattern feature extraction unit 230 is used to input the training motor temperature timing input vector, the training motor speed timing input vector and the training hydraulic system pressure timing input vector into the bucket wheel excavator operating parameter timing pattern feature extractor based on the causal convolutional network, so as to obtain the corresponding training motor temperature timing pattern feature vector, the training motor speed timing pattern feature vector and the training hydraulic system pressure timing pattern feature vector.

[0101] The training parameter timing fusion unit 240 is used to input the training motor temperature timing pattern feature vector and the training motor speed timing pattern feature vector into the motor operation state capturer based on the motor operation parameter timing fusion module to obtain the training motor temperature-speed timing pattern fusion feature vector representing the motor operation state.

[0102] The training feature association unit 250 is used to perform dependency scale calculation on the training motor temperature-speed timing pattern fusion characterization motor operation state feature vector and the training hydraulic system pressure timing pattern feature vector to obtain the training bucket wheel machine operation state characterization matrix.

[0103] The classification loss unit 260 is used to input the training bucket wheel machine operation state characterization matrix into the bucket wheel machine fault identifier based on the classifier to obtain a prediction value of whether the bucket wheel machine has a fault, and determine the classification loss function value according to the prediction value of whether the bucket wheel machine has a fault and the actual value of whether the bucket wheel machine has a fault;

[0104] The training unit 270 is used to train the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolutional network, the motor operating state capturer based on the motor operating parameter timing fusion module, and the bucket wheel machine fault identifier based on the classifier based on the classification loss function value and through back propagation of gradient descent.

[0105] Exemplarily, when the training unit 270 trains the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolutional network, the motor operating state capturer based on the motor operating parameter timing fusion module, and the bucket wheel machine fault identifier based on the classifier based on the classification loss function value and through back propagation of gradient descent, it can further introduce a distribution noise loss function value, wherein the distribution noise loss function value can be calculated by the following steps: inputting the training bucket wheel machine operating state characterization matrix into the bucket wheel machine fault identifier based on the classifier to obtain a class probability value, which can be used as a prediction value of whether the bucket wheel machine has a fault, and the true value of whether the bucket wheel machine has a fault. =1, calculate the absolute value of the difference between the class probability value and 1 to obtain the probability difference; determine the feature mean and feature variance of each eigenvalue of the training bucket wheel machine operation state characterization matrix as the feature set; determine the first product of each eigenvalue and the class probability value and the second product of the probability difference and the feature mean in the training bucket wheel machine operation state characterization matrix; sum the square of the difference between the first product and the second product corresponding to each eigenvalue in the training bucket wheel machine operation state characterization matrix and divide it by the length of the training bucket wheel machine operation state characterization matrix to obtain the division result; subtract the product of the sum of the squares of the class probability value and the probability difference and the feature variance from the division result to obtain the distribution noise loss function value. After obtaining the distribution noise loss function value, the training unit 270 can use the weighted sum of the classification loss function value and the multi-distribution noise loss function value as the loss function value, and train the bucket wheel machine operation parameter timing pattern feature extractor based on the causal convolution network, the motor operation state capturer based on the motor operation parameter timing fusion module, and the bucket wheel machine fault identifier based on the classifier through back propagation of gradient descent.

[0106] In particular, when performing dependency scale calculations on the training motor temperature-speed timing pattern fusion representation of the motor operating state feature vector and the training hydraulic system pressure timing pattern feature vector, the correlation differences in the time domain distribution of different source data will lead to out-of-distribution noise in the association representation set, resulting in class distribution noise in the training bucket wheel machine operating state representation matrix as a whole, thereby affecting the accuracy of the recognition result. The above-mentioned distribution noise loss function value is obtained by inputting the classifier-based bucket wheel machine fault identifier into the training bucket wheel machine operating state representation matrix through the class probability distribution characteristics expressed by the class probability value and probability difference, to set the approximate variance expectation characteristics of the out-of-distribution noise of the feature set of the training bucket wheel machine operating state representation matrix relative to the Bernoulli sampling instance of the feature set.

[0107] Therefore, by utilizing the distribution noise loss function value to use statistical noise distribution to measure the distribution feature noise based on the class probability distribution characteristics expressed by the class probability value and the probability difference value, the possible sampling noise features can be degraded based on the probabilistic crossover random selection characteristics of the expected variance when training each eigenvalue of the bucket wheel machine operating state characterization matrix in the classification process, thereby realizing the class distribution noise reduction of self-supervised learning in the process of model parameter updating to improve the accuracy of the recognition results.

[0108] Compared with the prior art, the automatic monitoring and alarm system for bucket wheel machine based on Internet of Things provided in the embodiment of the present disclosure obtains the time series of the bucket wheel machine operating parameters, and utilizes the data collection, processing and analysis algorithms based on Internet of Things and artificial intelligence to perform time series analysis and mutual fusion of the bucket wheel machine operating parameters. In this way, the operating status of the bucket wheel machine can be automatically judged according to the characterization characteristics of each operating parameter of the bucket wheel machine in the time series, and early warning prompts can be issued in time according to the operating status of the bucket wheel machine, which effectively improves the accuracy of bucket wheel machine fault detection, and at the same time improves the reliability and work efficiency of the equipment.

[0109] Exemplarily, the bucket wheel machine automatic monitoring and alarm system 100 based on the Internet of Things provided in the embodiments of the present disclosure can be implemented in various wireless terminals. For example, the wireless terminal can be a server with an automatic monitoring and alarm algorithm for bucket wheel machine based on the Internet of Things. In a possible implementation, the bucket wheel machine automatic monitoring and alarm system 100 based on the Internet of Things provided in the embodiments of the present disclosure can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the bucket wheel machine automatic monitoring and alarm system 100 based on the Internet of Things can be a software module in the operating system of the wireless terminal, or can be an application developed for the wireless terminal; of course, the bucket wheel machine automatic monitoring and alarm system 100 based on the Internet of Things can also be one of the many hardware modules of the wireless terminal.

[0110] Exemplarily, the bucket wheel machine automatic monitoring and alarm system 100 based on the Internet of Things and the wireless terminal may also be separate devices, and the bucket wheel machine automatic monitoring and alarm system 100 based on the Internet of Things may be connected to the wireless terminal via a wired and / or wireless network, and transmit interactive information in accordance with an agreed data format.

[0111] Another embodiment of the present disclosure relates to an automatic monitoring and alarm method for a bucket wheel machine based on the Internet of Things, such as Figure 6 As shown, the following steps are included:

[0112] Step S110, obtaining the time series of bucket wheel machine operating parameters, the bucket wheel machine operating parameters including the motor temperature value, motor speed value and hydraulic system pressure value of the bucket wheel machine.

[0113] Step S120, regularize the time series of bucket wheel machine operating parameters according to the time dimension and the sample dimension to obtain the corresponding motor temperature timing input vector, motor speed timing input vector and hydraulic system pressure timing input vector.

[0114] Step S130, extracting the timing pattern features of bucket wheel excavator operating parameters from the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector, respectively, to obtain corresponding motor temperature timing pattern feature vectors, motor speed timing pattern feature vectors and hydraulic system pressure timing pattern feature vectors.

[0115] Step S140 , capturing and fusing the motor operating state of the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector to obtain a motor temperature-speed timing pattern fusion feature vector representing the motor operating state.

[0116] Step S150, performing dependency scale calculation on the motor temperature-speed timing pattern fusion characterizing the motor operation state feature vector and the hydraulic system pressure timing pattern feature vector to obtain a bucket wheel machine operation state characterization matrix as a bucket wheel machine operation state characterization feature.

[0117] Step S160, based on the bucket wheel machine operating status characterization features, obtain the corresponding recognition result, and determine whether to issue a fault warning prompt based on the recognition result.

[0118] The specific steps of the automatic monitoring and alarm method for bucket wheel excavators based on the Internet of Things provided in the embodiments of the present disclosure can be found in the specific implementation methods of each module in the automatic monitoring and alarm system for bucket wheel excavators based on the Internet of Things provided in the embodiments of the present disclosure, and will not be repeated here.

[0119] Compared with the prior art, the automatic monitoring and alarm method for bucket wheel machine based on Internet of Things provided in the embodiment of the present disclosure obtains the time series of the bucket wheel machine operating parameters, and utilizes the data collection, processing and analysis algorithms based on Internet of Things and artificial intelligence to perform time series analysis and mutual fusion of the bucket wheel machine operating parameters. In this way, the operating status of the bucket wheel machine can be automatically judged according to the characterization characteristics of each operating parameter of the bucket wheel machine in the time series, and early warning prompts can be issued in time according to the operating status of the bucket wheel machine, which effectively improves the accuracy of bucket wheel machine fault detection, and at the same time improves the reliability and work efficiency of the equipment.

[0120] Those skilled in the art will appreciate that the above-mentioned embodiments are specific embodiments for implementing the present disclosure, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present disclosure.

Claims

1. An automatic monitoring and alarm system for bucket wheel excavators based on the Internet of Things, characterized in that: The bucket wheel machine automatic monitoring and alarm system comprises: A bucket wheel machine operating parameter acquisition module is used to obtain a time series of bucket wheel machine operating parameters, wherein the bucket wheel machine operating parameters include a motor temperature value, a motor speed value, and a hydraulic system pressure value of the bucket wheel machine; A data regularization module is used to regularize the time series of the bucket wheel machine operating parameters according to the time dimension and the sample dimension to obtain the corresponding motor temperature timing input vector, motor speed timing input vector and hydraulic system pressure timing input vector; An operating parameter timing pattern feature extraction module is used to extract the bucket wheel machine operating parameter timing pattern features from the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector, respectively, to obtain the corresponding motor temperature timing pattern feature vector, motor speed timing pattern feature vector and hydraulic system pressure timing pattern feature vector; A motor operation state feature fusion module is used to capture and fuse the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector to obtain a motor temperature-speed timing pattern fusion feature vector representing the motor operation state; An operating state characterization module is used to perform dependency scale calculation on the motor operating state feature vector represented by the motor temperature-speed timing pattern fusion and the hydraulic system pressure timing pattern feature vector, so as to obtain a bucket wheel machine operating state characterization matrix as a bucket wheel machine operating state characterization feature; The recognition result generating module is used to obtain the corresponding recognition result based on the characterization characteristics of the bucket wheel machine operation state, and determine whether to issue a fault warning prompt based on the recognition result.

2. The bucket wheel machine automatic monitoring and alarm system according to claim 1 is characterized in that: The operating parameter timing pattern feature extraction module is specifically used to: input the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector into the bucket wheel excavator operating parameter timing pattern feature extractor based on the causal convolutional network respectively, so as to obtain the corresponding motor temperature timing pattern feature vector, the motor speed timing pattern feature vector and the hydraulic system pressure timing pattern feature vector.

3. The automatic monitoring and alarm system for bucket wheel excavator according to claim 1 is characterized in that: The motor operation state feature fusion module is specifically used to: input the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector into the motor operation state capturer based on the motor operation parameter timing fusion module to obtain the motor temperature-speed timing pattern fusion feature vector representing the motor operation state.

4. The bucket wheel machine automatic monitoring and alarm system according to claim 3 is characterized in that: The motor operation state feature fusion module includes: A characterization feature construction unit, used to construct corresponding motor temperature timing pattern characterization feature vectors and motor speed timing pattern characterization feature vectors based on the motor temperature timing pattern feature vectors and the motor speed timing pattern feature vectors respectively; The motor parameter characteristic interaction unit is used to add the motor temperature timing pattern characterization characteristic vector and the motor speed timing pattern characterization characteristic vector by position, so as to obtain the motor temperature-speed timing pattern fusion characterization motor operation state characteristic vector.

5. The automatic monitoring and alarm system for bucket wheel excavator according to claim 4 is characterized in that: The characterization feature construction unit is specifically used for: Performing point convolution encoding on the motor temperature timing pattern feature vector to obtain a convolution motor temperature timing pattern feature vector, and multiplying the convolution motor temperature timing pattern feature vector by a first hyperparameter by position to obtain a motor temperature timing pattern characterization feature vector; The motor speed timing pattern feature vector is point-convolutionally encoded to obtain a convolution motor speed timing pattern feature vector, and the convolution motor speed timing pattern feature vector is positionally multiplied with a second hyperparameter to obtain the motor speed timing pattern characterization feature vector.

6. The automatic monitoring and alarm system for bucket wheel excavator according to claim 1 is characterized in that: The operating status characterization module includes: A motor pressure timing correlation unit, used for calculating the product between the motor temperature-speed timing pattern fusion characterizing the motor operation state feature vector and the transposed vector of the hydraulic system pressure timing pattern feature vector, so as to obtain a motor state pressure timing correlation input matrix; A correlation value calculation unit, used for calculating the sum of the elements on the diagonal line of the motor state pressure timing correlation input matrix to obtain the motor state pressure timing correlation value; The operating state characterization unit is used to divide the eigenvalues ​​of each position of the motor state pressure timing correlation input matrix by the motor state pressure timing correlation value to obtain the bucket wheel machine operating state characterization matrix composed of multiple motor state pressure timing correlation trace coefficients.

7. The automatic monitoring and alarm system for bucket wheel excavator according to claim 1 is characterized in that: The recognition result generating module comprises: A fault judgment unit, used for inputting the bucket wheel machine operation state characterization matrix into a bucket wheel machine fault identifier based on a classifier to obtain the corresponding identification result, wherein the identification result is used to indicate whether the bucket wheel machine has a fault; The early warning prompt generating unit is used to issue the fault early warning prompt in response to the recognition result indicating that the bucket wheel machine has a fault.

8. The bucket wheel machine automatic monitoring and alarm system according to any one of claims 2, 3 and 7, characterized in that: The bucket wheel machine automatic monitoring and alarm system also includes a training module; The training module is used to: When the operating parameter timing pattern feature extraction module is specifically used to input the motor temperature timing input vector, the motor speed timing input vector and the hydraulic system pressure timing input vector into the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolution network respectively, the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolution network is trained; When the motor operation state feature fusion module is specifically used to input the motor temperature timing mode feature vector and the motor speed timing mode feature vector into the motor operation state capturer based on the motor operation parameter timing fusion module, the motor operation state capturer based on the motor operation parameter timing fusion module is trained; When the recognition result generation module includes a fault judgment unit, the classifier-based bucket wheel machine fault identifier is trained.

9. The bucket wheel machine automatic monitoring and alarm system according to claim 8, characterized in that: The training module comprises: A training data acquisition unit, used to acquire training data and a true value of whether the bucket wheel machine has a fault; wherein the training data includes a time series of bucket wheel machine operation training parameters, and the bucket wheel machine operation training parameters include a training motor temperature value, a training motor speed value, and a training hydraulic system pressure value of the bucket wheel machine; A training data regularization unit is used to regularize the time series of the bucket wheel machine operation training parameters according to the time dimension and the sample dimension to obtain a training motor temperature timing input vector, a training motor speed timing input vector and a training hydraulic system pressure timing input vector; A training parameter timing pattern feature extraction unit, used to input the training motor temperature timing input vector, the training motor speed timing input vector and the training hydraulic system pressure timing input vector into the bucket wheel excavator operating parameter timing pattern feature extractor based on the causal convolutional network, respectively, to obtain corresponding training motor temperature timing pattern feature vector, training motor speed timing pattern feature vector and training hydraulic system pressure timing pattern feature vector; A training parameter timing fusion unit, used for inputting the training motor temperature timing pattern feature vector and the training motor speed timing pattern feature vector into the motor operation state capturer based on the motor operation parameter timing fusion module, so as to obtain a training motor temperature-speed timing pattern fusion feature vector representing the motor operation state; A training feature association unit is used to perform dependency scale calculation on the training motor temperature-speed timing pattern fusion characterization motor operation state feature vector and the training hydraulic system pressure timing pattern feature vector to obtain a training bucket wheel machine operation state characterization matrix; A classification loss unit, used for inputting the training bucket wheel machine operation state characterization matrix into the bucket wheel machine fault identifier based on the classifier to obtain a prediction value of whether the bucket wheel machine has a fault, and determining a classification loss function value according to the prediction value of whether the bucket wheel machine has a fault and a true value of whether the bucket wheel machine has a fault; A training unit is used to train the bucket wheel machine operating parameter timing pattern feature extractor based on the causal convolutional network, the motor operating state capturer based on the motor operating parameter timing fusion module, and the bucket wheel machine fault identifier based on the classifier based on the classification loss function value and through back propagation of gradient descent.

10. An automatic monitoring and alarm method for bucket wheel excavator based on Internet of Things, characterized in that: The automatic monitoring and alarm method for bucket wheel machine comprises: Acquire a time series of bucket wheel machine operating parameters, wherein the bucket wheel machine operating parameters include a motor temperature value, a motor speed value, and a hydraulic system pressure value of the bucket wheel machine; Regularize the time series of the bucket wheel machine operating parameters according to the time dimension and the sample dimension to obtain the corresponding motor temperature timing input vector, motor speed timing input vector and hydraulic system pressure timing input vector; Extracting bucket wheel machine operation parameter timing pattern features from the motor temperature timing input vector, the motor speed timing input vector, and the hydraulic system pressure timing input vector, respectively, to obtain corresponding motor temperature timing pattern feature vectors, motor speed timing pattern feature vectors, and hydraulic system pressure timing pattern feature vectors; Capturing and fusing the motor operating state of the motor temperature timing pattern feature vector and the motor speed timing pattern feature vector to obtain a motor temperature-speed timing pattern fusion feature vector representing the motor operating state; The motor temperature-speed timing pattern is fused to represent the motor operating state feature vector and the hydraulic system pressure timing pattern feature vector to perform dependency scale calculation to obtain a bucket wheel machine operating state representation matrix as a bucket wheel machine operating state representation feature; Based on the characteristics characterizing the bucket wheel excavator's operating status, a corresponding recognition result is obtained, and based on the recognition result, it is determined whether to issue a fault warning prompt.

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