Elevator traction system fault detection method based on artificial intelligence

Abstract

The invention discloses an elevator traction system fault detection method based on artificial intelligence, and belongs to the technical field of intelligent elevator operation and maintenance, and the method comprises the steps of elevator traction system data collection, elevator traction system data enhancement, fault detection model construction and elevator traction system fault detection. According to the method, the recursive generative adversarial network model is adopted for data enhancement, the time sequence dependence of the data is better captured, the synthetic data is closer to actual data in the statistical property, the authenticity of the synthetic data is improved, and therefore the deviation during model training is reduced, and the fault detection accuracy and reliability of the elevator traction system are enhanced; a parallel deep convolution coupling graph convolution network is adopted, an elevator traction system fault detection model is constructed, local and global features are fully extracted, multi-scale features are effectively processed, and a complex relation between data is captured through graph structure modeling, so that the elevator traction system fault detection precision is improved, and the fault detection efficiency is improved. And more accurate data support is provided for intelligent elevator operation and maintenance.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent elevator operation and maintenance, and specifically refers to an elevator traction system fault detection method based on artificial intelligence. Background Art

[0002] AI-based elevator traction system fault detection uses AI technology to monitor and analyze the data generated during the operation of the elevator traction system and identify faults in a timely manner. It aims to automatically analyze the status of the elevator traction system and realize intelligent fault detection, thereby improving elevator maintenance efficiency and safety and promoting the intelligent development of elevator operation and maintenance.

[0003] However, in the existing elevator traction system fault detection process, there is a shortage of elevator traction system fault data, which makes the model detection easily biased towards normal data, affecting the detection accuracy. In addition, the elevator traction system data has time series characteristics, and the traditional generative adversarial network mainly processes static data, which is difficult to effectively generate high-quality time series data, resulting in poor quality of synthetic data, further reducing the detection accuracy. Technical problems; there is a technical problem that elevator traction system failures are usually caused by the coordinated action of multiple components, and therefore require high global modeling capabilities of the detection model. In addition, elevator traction system fault detection involves multiple sensor data and has multimodal complexity. Traditional fault detection models are difficult to fully explore the complex relationship between multimodal data, and the multi-scale feature extraction capability is insufficient, which in turn affects the detection accuracy. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an elevator traction system fault detection method based on artificial intelligence. In the existing elevator traction system fault detection process, there is a shortage of elevator traction system fault data, which makes the model detection easily biased towards normal data, affecting the detection accuracy. In addition, the elevator traction system data has time series characteristics, and the traditional generative adversarial network mainly processes static data, and it is difficult to effectively generate high-quality time series data, resulting in poor quality of synthetic data, and further reducing the technical problem of detection accuracy. This scheme creatively adopts a recursive generative adversarial network model for data enhancement, and introduces a recursive neural network based on long short-term memory units to better capture the time series dependency of data. By improving the model loss function, the synthetic data is closer to the actual data in statistical characteristics, and the authenticity of the synthetic data is improved, thereby reducing the model training. Deviations in time can be reduced to enhance the accuracy and reliability of elevator traction system fault detection; in the existing elevator traction system fault detection process, there is a problem that the elevator traction system fault is usually caused by the coordinated action of multiple components, and thus the global modeling capability of the detection model is high. In addition, the elevator traction system fault detection involves a variety of sensor data and has multi-modal complexity. The traditional fault detection model is difficult to fully explore the complex relationship between multi-modal data, and the multi-scale feature extraction capability is insufficient, which in turn affects the detection accuracy. This solution creatively adopts parallel deep convolution coupled graph convolutional network to construct an elevator traction system fault detection model, fully extract local and global features, effectively process multi-scale features, and capture the complex relationship between data through graph structure modeling, thereby improving the accuracy of elevator traction system fault detection and providing more accurate data support for intelligent elevator operation and maintenance.

[0005] The technical solution adopted by the present invention is as follows: The present invention provides an elevator traction system fault detection method based on artificial intelligence, the method comprising the following steps:

[0006] Step S1: Collecting elevator traction system data;

[0007] Step S2: enhancing elevator traction system data;

[0008] Step S3: construct a fault detection model;

[0009] Step S4: Elevator traction system fault detection.

[0010] Furthermore, in step S1, the elevator traction system data is collected, specifically by installing sensors at the traction machine, motor bearings, and wire ropes of the elevator, collecting the elevator traction system data and labeling the data to obtain an elevator traction system status label, and then preprocessing the elevator traction system data to obtain the actual data of the elevator traction system.

[0011] Further, in step S2, the enhanced elevator traction system data is used to alleviate the data imbalance problem, specifically, based on the elevator traction system state label and the actual data of the elevator traction system, a recursive generative adversarial network model is used to perform data enhancement to obtain the elevator traction system enhanced data;

[0012] The recursive generative adversarial network model includes a generator and a discriminator;

[0013] The method of enhancing the elevator traction system data comprises the following steps:

[0014] Step S21: constructing a generator for synthesizing data, specifically receiving minority class data in the actual data of the elevator traction system as the actual system data, introducing a recursive neural network based on long short-term memory units into the generator, learning the temporal dependency features in the data, and generating the synthesized data of the elevator traction system;

[0015] Step S22: constructing a discriminator for distinguishing actual data from synthetic data, specifically receiving minority class data in the actual data of the elevator traction system as the actual data of the system, and receiving the synthetic data of the elevator traction system, introducing a recursive neural network based on long short-term memory units into the discriminator to judge the authenticity of the data, and obtaining a discrimination result;

[0016] Step S23: improving the model loss function, specifically, constructing a generator loss function by combining the moment matching loss function, and constructing a discriminator loss function by introducing a gradient penalty term;

[0017] The moment matching loss function is used to retain the statistical characteristics of the actual data, and the calculation formula is:

[0018] ;

[0019] Where, L mm is the moment matching loss function, t max is the maximum time step, used to represent the time series step of the data, is the square of the L2 norm, t is the time step index, is the mean of the actual data of the system at the tth time step, is the mean value of the synthetic data of the elevator traction system at the tth time step, is the standard deviation of the actual data of the system at time step t, is the standard deviation of the synthetic data of the elevator traction system at the tth time step, G(z) is the synthetic data of the elevator traction system, and z is a noise data point, specifically a data point randomly sampled from the noise distribution;

[0020] The calculation formula of the generator loss function is:

[0021] ;

[0022] Where, L RGAN is the generator loss function, is the first weight of the generator, is the expectation of the synthetic data of the elevator traction system, which is used to measure the authenticity of the synthetic data of the elevator traction system. z is the noise distribution, D(·) is the discriminator scoring function, which is used to represent the authenticity of the synthetic data of the elevator traction system. is the second weight of the generator;

[0023] The calculation formula of the discriminator loss function is:

[0024] ;

[0025] Where, L D is the discriminator loss function, is the expectation of the actual data of the system, p x is the actual data distribution of the system, x is the actual data point, specifically refers to the data point randomly sampled from the actual data distribution of the system, is the gradient penalty weight, is the expectation of the interpolated data, is an interpolation data distribution, which specifically refers to a data distribution obtained by linearly interpolating the actual system data and the synthetic data of the elevator traction system, are interpolated data points, is the gradient of the discriminator with respect to the interpolated data, is the L2 norm;

[0026] Step S24: training the recursive generative adversarial network model, specifically, training the recursive generative adversarial network model by alternately training the generator and the discriminator to obtain the recursive generative adversarial network model;

[0027] Step S25: data enhancement, specifically synthesizing the minority class data in the actual data of the elevator traction system through a recursive generative adversarial network model to generate elevator traction system synthetic data, and then merging the elevator traction system synthetic data and the elevator traction system actual data to obtain the elevator traction system enhanced data.

[0028] Further, in step S3, the fault detection model is constructed to construct an elevator traction system fault detection model, specifically, based on the elevator traction system state label and the elevator traction system enhanced data, a parallel deep convolution coupled graph convolution network is used to construct the elevator traction system fault detection model;

[0029] The parallel deep convolution coupled graph convolution network includes an input layer, a parallel deep convolution subnet, a feature fusion attention block, a parallel graph convolution subnet and a classification output layer;

[0030] The parallel deep convolutional subnetwork is used to capture multi-scale features in parallel;

[0031] The feature fusion attention block is used to fuse multi-scale features and focus on key features;

[0032] The parallel graph convolutional subnetwork is used to further capture the relationship between features;

[0033] The construction of the fault detection model comprises the following steps:

[0034] Step S31: constructing an input layer, specifically receiving the elevator traction system enhanced data through the input layer to construct a fault detection input sample;

[0035] Step S32: construct a parallel deep convolutional subnetwork, specifically, setting a first deep convolutional subnetwork and a second deep convolutional subnetwork in the parallel deep convolutional subnetwork, wherein the first deep convolutional subnetwork and the second deep convolutional subnetwork are parallel structures, and extracting multi-scale features of the fault detection input sample through the parallel deep convolutional subnetwork to obtain the global features and local features of the fault detection, including the following steps:

[0036] Step S321: constructing a first deep convolutional subnet, specifically, setting three convolutional layers in the first deep convolutional subnet, setting a batch normalization layer after each convolutional layer, setting the convolution kernel size in the first deep convolutional subnet to 7×1, and extracting global features for fault detection through the first deep convolutional subnet;

[0037] Step S322: construct a second deep convolutional subnetwork, specifically, set three convolutional layers in the second deep convolutional subnetwork, set a batch normalization layer after each convolutional layer, set the convolution kernel size in the second deep convolutional subnetwork to 3×1, and extract local features for fault detection through the second deep convolutional subnetwork;

[0038] Step S33: construct a feature fusion attention block, specifically, setting a feature fusion layer, an attention mechanism layer and a fully connected layer in the feature fusion attention block, processing the global features and local features of fault detection through the feature fusion attention block, and obtaining the fault detection fusion enhanced features, including the following steps:

[0039] Step S331: constructing a feature fusion layer, specifically, in the feature fusion layer, performing a dot product operation on the global features of fault detection and the local features of fault detection to obtain a fusion feature of fault detection;

[0040] Step S332: constructing an attention mechanism layer, specifically, in the attention mechanism layer, applying a channel attention mechanism to perform weighted calculation on the fault detection fusion feature to obtain the fault detection attention enhancement feature;

[0041] Step S333: construct a fully connected layer, specifically, perform feature dimension reduction on the fault detection attention enhancement feature through the fully connected layer to obtain the fault detection fusion enhancement feature;

[0042] Step S34: Constructing a parallel graph convolutional subnetwork, including the following steps:

[0043] Step S341: constructing a graph structure, specifically, taking the fault detection fusion enhancement feature of each sample as a node feature, establishing a node feature matrix, establishing an adjacency matrix by measuring the similarity between each node feature, and performing a normalization operation to obtain a fault detection adjacency matrix;

[0044] Step S342: constructing a parallel first-order graph convolution layer, a second-order graph convolution layer, and a third-order graph convolution layer, processing the fault detection adjacency matrix in parallel, extracting multi-level information, performing feature concatenation on the extracted results, and performing feature dimension reduction through a fully connected layer to obtain multi-level features for fault detection;

[0045] Step S35: constructing a classification output layer, specifically setting a fully connected layer and a softmax activation function in the classification output layer, classifying multi-level features of fault detection, and obtaining a model output result;

[0046] Step S36: model training, specifically, constructing a parallel deep convolutional coupled graph convolutional network by constructing the input layer, constructing the parallel deep convolutional subnet, constructing the feature fusion attention block, constructing the parallel graph convolutional subnet and constructing the classification output layer, and performing model training to obtain the elevator traction system fault detection model.

[0047] Furthermore, in step S4, the elevator traction system fault detection is specifically to perform elevator traction system fault detection through an elevator traction system fault detection model to obtain the elevator traction system status, and generate an elevator traction system fault detection report based on the elevator traction system status to assist management personnel in maintaining the elevator traction system.

[0048] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0049] (1) In view of the scarcity of elevator traction system fault data in the existing elevator traction system fault detection process, the model detection is easily biased towards normal data, affecting the detection accuracy. In addition, the elevator traction system data has time series characteristics, and the traditional generative adversarial network mainly processes static data, which is difficult to effectively generate high-quality time series data, resulting in poor quality of synthetic data and further reducing the detection accuracy. This solution creatively uses a recursive generative adversarial network model for data enhancement. By introducing a recursive neural network based on long short-term memory units, the temporal dependency of the data is better captured. By improving the model loss function, the synthetic data is closer to the actual data in terms of statistical characteristics, improving the authenticity of the synthetic data, thereby reducing the deviation during model training and enhancing the accuracy and reliability of elevator traction system fault detection.

[0050] (2) In view of the technical problem that in the existing elevator traction system fault detection process, the elevator traction system fault is usually caused by the coordinated action of multiple components, which requires high global modeling capabilities of the detection model. In addition, the elevator traction system fault detection involves multiple sensor data and has multi-modal complexity. The traditional fault detection model is difficult to fully explore the complex relationship between multi-modal data and the multi-scale feature extraction capability is insufficient, which in turn affects the detection accuracy. This scheme creatively adopts parallel deep convolution coupled graph convolution network to construct an elevator traction system fault detection model, fully extract local and global features, effectively process multi-scale features, and capture the complex relationship between data through graph structure modeling, thereby improving the accuracy of elevator traction system fault detection and providing more accurate data support for intelligent elevator operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a flow chart of an elevator traction system fault detection method based on artificial intelligence provided by the present invention;

[0052] Figure 2 is a schematic flow chart of step S2;

[0053] Figure 3 is a schematic flow chart of step S3;

[0054] Figure 4 It is a flowchart diagram of step S33.

[0055] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0057] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0058] Example 1, see Figure 1 The present invention provides an elevator traction system fault detection method based on artificial intelligence, which comprises the following steps:

[0059] Step S1: Collecting elevator traction system data;

[0060] Step S2: enhancing elevator traction system data;

[0061] Step S3: construct a fault detection model;

[0062] Step S4: Elevator traction system fault detection.

[0063] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the elevator traction system data is collected by installing sensors on the elevator traction machine, motor bearings, and wire ropes to collect the elevator traction system data and perform data labeling to obtain the elevator traction system status label. Then, the elevator traction system data is preprocessed to obtain the actual data of the elevator traction system.

[0064] The elevator traction system status label includes normal operation, current overload, abnormal vibration, abnormal temperature, abnormal acceleration and abnormal speed;

[0065] The data preprocessing includes noise filtering, data cleaning, Z-score standardization, data alignment and data dimension reduction;

[0066] The noise filtering is to filter the data noise by wavelet transform;

[0067] The data cleaning includes removing duplicate values, eliminating outliers and filling missing values;

[0068] The data alignment aligns the timestamps in the data through dynamic time planning;

[0069] The data dimension reduction is to reduce the data dimension by principal component analysis;

[0070] The elevator traction system data includes vibration signals, current signals, voltage signals, temperature, noise, acceleration and traction machine speed.

[0071] Example 3, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S2, the elevator traction system data is enhanced to alleviate the data imbalance problem. Specifically, the recursive generative adversarial network model is used to enhance the data based on the elevator traction system state label and the actual data of the elevator traction system to obtain the elevator traction system enhanced data;

[0072] The recursive generative adversarial network model includes a generator and a discriminator;

[0073] The method of enhancing the elevator traction system data comprises the following steps:

[0074] Step S21: constructing a generator for synthesizing data, specifically receiving minority class data in the actual data of the elevator traction system as the actual system data, introducing a recursive neural network based on long short-term memory units into the generator, learning the temporal dependency features in the data, and generating the synthesized data of the elevator traction system;

[0075] Step S22: constructing a discriminator for distinguishing actual data from synthetic data, specifically receiving minority class data in the actual data of the elevator traction system as the actual data of the system, and receiving the synthetic data of the elevator traction system, introducing a recursive neural network based on long short-term memory units into the discriminator to judge the authenticity of the data, and obtaining a discrimination result;

[0076] Step S23: improving the model loss function, specifically, constructing a generator loss function by combining the moment matching loss function, and constructing a discriminator loss function by introducing a gradient penalty term, wherein the gradient penalty term is used to constrain the discriminator to avoid overfitting;

[0077] The moment matching loss function is used to retain the statistical characteristics of the actual data, and the calculation formula is:

[0078] ;

[0079] Where, L mm is the moment matching loss function, t max is the maximum time step, used to represent the time series step of the data, is the square of the L2 norm, t is the time step index, is the mean of the actual data of the system at the tth time step, is the mean value of the synthetic data of the elevator traction system at the tth time step, is the standard deviation of the actual data of the system at time step t, is the standard deviation of the synthetic data of the elevator traction system at the tth time step, G(z) is the synthetic data of the elevator traction system, and z is a noise data point, specifically a data point randomly sampled from the noise distribution;

[0080] The calculation formula of the generator loss function is:

[0081] ;

[0082] Where, L RGAN is the generator loss function, is the first weight of the generator, is the expectation of the synthetic data of the elevator traction system, which is used to measure the authenticity of the synthetic data of the elevator traction system. z is the noise distribution, D(·) is the discriminator scoring function, which is used to represent the authenticity of the synthetic data of the elevator traction system. is the second weight of the generator;

[0083] The calculation formula of the discriminator loss function is:

[0084] ;

[0085] Where, L D is the discriminator loss function, is the expectation of the actual data of the system, p x is the actual data distribution of the system, x is the actual data point, specifically refers to the data point randomly sampled from the actual data distribution of the system, is the gradient penalty weight, is the expectation of the interpolated data, is an interpolation data distribution, which specifically refers to a data distribution obtained by linearly interpolating the actual system data and the synthetic data of the elevator traction system, are interpolated data points, is the gradient of the discriminator with respect to the interpolated data, is the L2 norm;

[0086] Step S24: training the recursive generative adversarial network model, specifically, training the recursive generative adversarial network model by alternately training the generator and the discriminator to obtain the recursive generative adversarial network model;

[0087] Step S25: data enhancement, specifically, synthesizing minority class data in the actual data of the elevator traction system through a recursive generative adversarial network model to generate elevator traction system synthetic data, and then merging the elevator traction system synthetic data with the actual data of the elevator traction system to obtain elevator traction system enhanced data;

[0088] Table 1 is a performance comparison table of the elevator traction system fault detection model before and after the elevator traction system data is enhanced in Example 4 of the present invention. As shown in the table, the F1 score is the harmonic mean of the precision and the recall rate. The higher the F1 score, the better the model performance.

[0089] Table 1 Performance comparison of elevator traction system fault detection model before and after enhancing elevator traction system data

[0090]

[0091] By performing the above operations, in order to solve the technical problem that in the existing elevator traction system fault detection process, there is a shortage of elevator traction system fault data, which makes the model detection easily biased towards normal data, affecting the detection accuracy. In addition, the elevator traction system data has time series characteristics, and the traditional generative adversarial network mainly processes static data, and it is difficult to effectively generate high-quality time series data, resulting in poor quality of synthetic data, further reducing the detection accuracy, this scheme creatively adopts a recursive generative adversarial network model for data enhancement, and introduces a recursive neural network based on long short-term memory units to better capture the time series dependency of the data. By improving the model loss function, the synthetic data is closer to the actual data in statistical characteristics, and the authenticity of the synthetic data is improved, thereby reducing the deviation during model training and enhancing the accuracy and reliability of elevator traction system fault detection.

[0092] Example 4, see Figure 1 , Figure 3 and Figure 4 , this embodiment is based on the above embodiment. In step S3, the fault detection model is constructed to construct an elevator traction system fault detection model. Specifically, the elevator traction system fault detection model is constructed by using a parallel deep convolution coupled graph convolution network based on the elevator traction system state label and the elevator traction system enhanced data;

[0093] The parallel deep convolution coupled graph convolution network includes an input layer, a parallel deep convolution subnet, a feature fusion attention block, a parallel graph convolution subnet and a classification output layer;

[0094] The parallel deep convolutional subnetwork is used to capture multi-scale features in parallel;

[0095] The feature fusion attention block is used to fuse multi-scale features and focus on key features;

[0096] The parallel graph convolutional subnetwork is used to further capture the relationship between features;

[0097] The construction of the fault detection model comprises the following steps:

[0098] Step S31: constructing an input layer, specifically receiving the elevator traction system enhanced data through the input layer to construct a fault detection input sample;

[0099] Step S32: construct a parallel deep convolutional subnetwork, specifically, setting a first deep convolutional subnetwork and a second deep convolutional subnetwork in the parallel deep convolutional subnetwork, wherein the first deep convolutional subnetwork and the second deep convolutional subnetwork are parallel structures, and extracting multi-scale features of the fault detection input sample through the parallel deep convolutional subnetwork to obtain the global features and local features of the fault detection, including the following steps:

[0100] Step S321: constructing a first deep convolutional subnet, specifically, setting three convolutional layers in the first deep convolutional subnet, setting a batch normalization layer after each convolutional layer, setting the convolution kernel size in the first deep convolutional subnet to 7×1, and extracting global features for fault detection through the first deep convolutional subnet;

[0101] Step S322: construct a second deep convolutional subnetwork, specifically, set three convolutional layers in the second deep convolutional subnetwork, set a batch normalization layer after each convolutional layer, set the convolution kernel size in the second deep convolutional subnetwork to 3×1, and extract local features for fault detection through the second deep convolutional subnetwork;

[0102] Step S33: construct a feature fusion attention block, specifically, setting a feature fusion layer, an attention mechanism layer and a fully connected layer in the feature fusion attention block, processing the global features and local features of fault detection through the feature fusion attention block, and obtaining the fault detection fusion enhanced features, including the following steps:

[0103] Step S331: constructing a feature fusion layer, specifically, in the feature fusion layer, performing a dot product operation on the global features of fault detection and the local features of fault detection to obtain a fusion feature of fault detection;

[0104] Step S332: constructing an attention mechanism layer, specifically, in the attention mechanism layer, applying a channel attention mechanism to perform weighted calculation on the fault detection fusion feature to obtain the fault detection attention enhancement feature;

[0105] Step S333: construct a fully connected layer, specifically, perform feature dimension reduction on the fault detection attention enhancement feature through the fully connected layer to obtain the fault detection fusion enhancement feature;

[0106] Step S34: construct a parallel graph convolution subnetwork, including the following steps:

[0107] Step S341: constructing a graph structure, specifically, taking the fault detection fusion enhancement feature of each sample as a node feature, establishing a node feature matrix, establishing an adjacency matrix by measuring the similarity between each node feature, and performing a normalization operation to obtain a fault detection adjacency matrix;

[0108] By measuring the similarity between the features of each node, establishing an adjacency matrix, and performing a normalization operation, the calculation formula of the fault detection adjacency matrix is ​​obtained as follows:

[0109] ;

[0110] Wherein, A is the fault detection adjacency matrix, Nor(·) is the normalization operation, specifically the symmetric normalization operation, M is the node feature matrix, the size of the node feature matrix is ​​N×U, N is the number of nodes, the number of nodes is equal to the number of samples, U is the feature dimension of each node, T is the transposition operation, Used to measure the similarity between each node feature;

[0111] Step S342: construct a parallel first-order graph convolution layer, a second-order graph convolution layer, and a third-order graph convolution layer, process the fault detection adjacency matrix in parallel, extract multi-level information, perform feature concatenation on the extracted results, and perform feature dimension reduction through a fully connected layer to obtain multi-level features for fault detection. The calculation formula is:

[0112] ;

[0113] ;

[0114] In the formula, F k is the extraction result of the k-th order graph convolutional layer, LeakyReLU(·) is the ReLU activation function with leakage, k is the aggregation order index, when k=1, it means the aggregation of first-order neighbor information, W k is the learning weight of the k-th graph convolutional layer, F final is the multi-level feature of fault detection, FC(·) is the fully connected layer function, and concat(·) is the feature concatenation operation;

[0115] The first-order graph convolution layer is used to aggregate first-order neighbor information;

[0116] The second-order graph convolution layer is used to aggregate second-order neighbor information;

[0117] The third-order graph convolution layer is used to aggregate third-order neighbor information;

[0118] Step S35: constructing a classification output layer, specifically setting a fully connected layer and a softmax activation function in the classification output layer, classifying multi-level features of fault detection, and obtaining a model output result;

[0119] Step S36: model training, specifically, constructing a parallel deep convolution coupled graph convolution network by constructing the input layer, constructing the parallel deep convolution subnet, constructing the feature fusion attention block, constructing the parallel graph convolution subnet and constructing the classification output layer, and performing model training to obtain an elevator traction system fault detection model;

[0120] By performing the above operations, in view of the fact that in the existing elevator traction system fault detection process, the elevator traction system fault is usually caused by the coordinated action of multiple components, and thus the global modeling capability of the detection model is high, and the elevator traction system fault detection involves a variety of sensor data and has multi-modal complexity. The traditional fault detection model is difficult to fully explore the complex relationship between multi-modal data, and the multi-scale feature extraction capability is insufficient, which in turn affects the detection accuracy. Technical problems, this scheme creatively adopts parallel deep convolution coupled graph convolution network to construct an elevator traction system fault detection model, fully extracts local and global features, effectively processes multi-scale features, and captures the complex relationship between data through graph structure modeling, thereby improving the accuracy of elevator traction system fault detection and providing more accurate data support for intelligent elevator operation and maintenance.

[0121] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S4, the elevator traction system fault detection is specifically to perform elevator traction system fault detection through an elevator traction system fault detection model to obtain the elevator traction system state, and generate an elevator traction system fault detection report according to the elevator traction system state to assist management personnel in maintaining the elevator traction system;

[0122] The elevator traction system fault detection report includes detection date, elevator information, elevator traction system status and maintenance suggestions.

[0123] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0124] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0125] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An elevator traction system fault detection method based on artificial intelligence, characterized in that: The method comprises the following steps: Step S1: Collect elevator traction system data to obtain the elevator traction system status label and the elevator traction system actual data; Step S2: enhancing the elevator traction system data, using a recursive generative adversarial network model to perform data enhancement according to the elevator traction system state label and the actual data of the elevator traction system, to obtain the elevator traction system enhanced data; the recursive generative adversarial network model includes a generator and a discriminator; the generator loss function is constructed by combining the moment matching loss function, and the discriminator loss function is constructed by introducing a gradient penalty term; Step S3: construct a fault detection model, based on the elevator traction system state label and the elevator traction system enhanced data, using a parallel deep convolution coupled graph convolutional network to construct an elevator traction system fault detection model; The parallel deep convolution coupled graph convolution network includes an input layer, a parallel deep convolution subnet, a feature fusion attention block, a parallel graph convolution subnet and a classification output layer; The parallel deep convolutional subnetwork is used to capture multi-scale features in parallel; The feature fusion attention block is used to fuse multi-scale features and focus on key features; The parallel graph convolutional subnetwork is used to further capture the relationship between features; Step S4: Elevator traction system fault detection.

2. The elevator traction system fault detection method based on artificial intelligence according to claim 1 is characterized in that: In step S3, the construction of the fault detection model is used to construct the elevator traction system fault detection model, including the following steps: Step S31: constructing an input layer, specifically receiving the elevator traction system enhanced data through the input layer to construct a fault detection input sample; Step S32: construct a parallel deep convolutional subnetwork, specifically, setting a first deep convolutional subnetwork and a second deep convolutional subnetwork in the parallel deep convolutional subnetwork, wherein the first deep convolutional subnetwork and the second deep convolutional subnetwork are parallel structures, and extracting multi-scale features of the fault detection input samples through the parallel deep convolutional subnetwork to obtain global features and local features of fault detection; Step S33: construct a feature fusion attention block, specifically, setting a feature fusion layer, an attention mechanism layer and a fully connected layer in the feature fusion attention block, and processing the global features and local features of fault detection through the feature fusion attention block to obtain the fault detection fusion enhanced features; Step S34: construct a parallel graph convolution subnetwork, including the following steps: Step S341: constructing a graph structure, specifically, taking the fault detection fusion enhancement feature of each sample as a node feature, establishing a node feature matrix, establishing an adjacency matrix by measuring the similarity between each node feature, and performing a normalization operation to obtain a fault detection adjacency matrix; Step S342: constructing a parallel first-order graph convolution layer, a second-order graph convolution layer, and a third-order graph convolution layer, processing the fault detection adjacency matrix in parallel, extracting multi-level information, performing feature concatenation on the extracted results, and performing feature dimension reduction through a fully connected layer to obtain multi-level features for fault detection; Step S35: constructing a classification output layer, specifically setting a fully connected layer and a softmax activation function in the classification output layer, classifying multi-level features of fault detection, and obtaining a model output result; Step S36: model training, specifically, constructing a parallel deep convolutional coupled graph convolutional network by constructing the input layer, constructing the parallel deep convolutional subnet, constructing the feature fusion attention block, constructing the parallel graph convolutional subnet and constructing the classification output layer, and performing model training to obtain the elevator traction system fault detection model.

3. The elevator traction system fault detection method based on artificial intelligence according to claim 2 is characterized in that: In step S32, the construction of a parallel deep convolutional subnetwork includes the following steps: Step S321: constructing a first deep convolutional subnet, specifically, setting three convolutional layers in the first deep convolutional subnet, setting a batch normalization layer after each convolutional layer, setting the convolution kernel size in the first deep convolutional subnet to 7×1, and extracting global features for fault detection through the first deep convolutional subnet; Step S322: construct a second deep convolutional subnetwork, specifically, set three convolutional layers in the second deep convolutional subnetwork, set a batch normalization layer after each convolutional layer, set the convolution kernel size in the second deep convolutional subnetwork to 3×1, and extract local features for fault detection through the second deep convolutional subnetwork.

4. The elevator traction system fault detection method based on artificial intelligence according to claim 3 is characterized in that: Step S33: construct a feature fusion attention block, specifically, setting a feature fusion layer, an attention mechanism layer and a fully connected layer in the feature fusion attention block, processing the global features and local features of fault detection through the feature fusion attention block, and obtaining the fault detection fusion enhanced features, including the following steps: Step S331: constructing a feature fusion layer, specifically, in the feature fusion layer, performing a dot product operation on the global features of fault detection and the local features of fault detection to obtain a fusion feature of fault detection; Step S332: constructing an attention mechanism layer, specifically, in the attention mechanism layer, applying a channel attention mechanism to perform weighted calculation on the fault detection fusion feature to obtain the fault detection attention enhancement feature; Step S333: construct a fully connected layer, specifically, perform feature dimension reduction on the fault detection attention enhancement feature through the fully connected layer to obtain the fault detection fusion enhancement feature.

5. The method for detecting elevator traction system faults based on artificial intelligence according to claim 4, characterized in that: In step S2, the elevator traction system data is enhanced to alleviate the data imbalance problem, specifically, based on the elevator traction system state label and the actual data of the elevator traction system, a recursive generative adversarial network model is used to perform data enhancement to obtain the elevator traction system enhanced data; The recursive generative adversarial network model includes a generator and a discriminator; The method of enhancing the elevator traction system data comprises the following steps: Step S21: constructing a generator for synthesizing data, specifically receiving minority class data in the actual data of the elevator traction system as the actual system data, introducing a recursive neural network based on long short-term memory units into the generator, learning the temporal dependency features in the data, and generating the synthesized data of the elevator traction system; Step S22: constructing a discriminator for distinguishing actual data from synthetic data, specifically receiving minority class data in the actual data of the elevator traction system as the actual data of the system, and receiving the synthetic data of the elevator traction system, introducing a recursive neural network based on long short-term memory units into the discriminator to judge the authenticity of the data, and obtaining a discrimination result; Step S23: improving the model loss function, specifically, constructing a generator loss function by combining the moment matching loss function, and constructing a discriminator loss function by introducing a gradient penalty term; The moment matching loss function is used to retain the statistical characteristics of the actual data, and the calculation formula is: ； Where, L mm is the moment matching loss function, t max is the maximum time step, used to represent the time series step of the data, is the square of the L2 norm, t is the time step index, is the mean of the actual data of the system at the tth time step, is the mean value of the synthetic data of the elevator traction system at the tth time step, is the standard deviation of the actual data of the system at time step t, is the standard deviation of the synthetic data of the elevator traction system at the tth time step, G(z) is the synthetic data of the elevator traction system, and z is a noise data point, specifically a data point randomly sampled from the noise distribution; The calculation formula of the generator loss function is: ； Where, L RGAN is the generator loss function, is the first weight of the generator, is the expectation of the synthetic data of the elevator traction system, which is used to measure the authenticity of the synthetic data of the elevator traction system. z is the noise distribution, D(·) is the discriminator scoring function, which is used to represent the authenticity of the synthetic data of the elevator traction system. is the second weight of the generator; The calculation formula of the discriminator loss function is: ； Where, L D is the discriminator loss function, is the expectation of the actual data of the system, p x is the actual data distribution of the system, x is the actual data point, specifically refers to the data point randomly sampled from the actual data distribution of the system, is the gradient penalty weight, is the expectation of the interpolated data, is an interpolation data distribution, which specifically refers to a data distribution obtained by linearly interpolating the actual system data and the synthetic data of the elevator traction system, are interpolated data points, is the gradient of the discriminator with respect to the interpolated data, is the L2 norm; Step S24: training the recursive generative adversarial network model, specifically, training the recursive generative adversarial network model by alternately training the generator and the discriminator to obtain the recursive generative adversarial network model; Step S25: data enhancement, specifically synthesizing the minority class data in the actual data of the elevator traction system through a recursive generative adversarial network model to generate elevator traction system synthetic data, and then merging the elevator traction system synthetic data and the elevator traction system actual data to obtain the elevator traction system enhanced data.

6. The method for detecting faults of an elevator traction system based on artificial intelligence according to claim 5, characterized in that: In step S1, the elevator traction system data is collected, specifically by installing sensors at the traction machine, motor bearings, and wire ropes of the elevator, collecting the elevator traction system data and labeling the data to obtain the elevator traction system status label, and then preprocessing the elevator traction system data to obtain the elevator traction system actual data.

7. The method for detecting elevator traction system faults based on artificial intelligence according to claim 6, characterized in that: In step S4, the elevator traction system fault detection is specifically to perform elevator traction system fault detection through an elevator traction system fault detection model to obtain the elevator traction system status, and generate an elevator traction system fault detection report based on the elevator traction system status to assist management personnel in maintaining the elevator traction system.

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