# Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

Authors: Zhiming Li
Journal: Journal of Sensors
 (2022)

---

## Abstract

The application of traditional feature extraction methods in subway mechanical fault diagnosis has attracted the attention of researchers. Based on the 5G intelligent sensor network signal processing theory, this paper constructs a fault diagnosis model for subway machinery and equipment and analyzes the effectiveness of the information redundancy calculation method proposed in this paper by using the on-site vibration data of subway units. The model is obtained by analyzing the entropy characteristics of the vector vibration signal in each bearing section of the subway machinery and equipment. The entropy value quantitatively reflects the vibration complexity of the rotor in the section from different angles and solves the problem of data quantitative analysis. In the simulation process, based on the 5G intelligent sensor network signal and the fuzzy mean clustering information fusion method, in the process of identifying the fault state of the subway unit, a relatively optimistic identification result was obtained. The experimental results show that, no matter whether the Gaussian kernel or the polynomial kernel is selected, the number of kernel principal components whose cumulative contribution rate is greater than 0.85 decreases with the increase of the kernel parameter, and the fault identification support rate is 85%, that is, the diagnosis results of 3 sets of sample data are nonrolling element faults, which significantly improves the performance of the bearing.


---

## Body

## 1. Introduction

Subway machinery is a key core equipment widely used in the transportation support industry. To carry out research on the technology of subway machinery [1] is to ensure the safe, reliable, efficient, long-term, full-load, high-quality operation of such equipment, and the occurrence of losses and catastrophic accidents has great economic and social significance [2]. The essence of the fault diagnosis process of subway machinery is the process of pattern analysis. The kernel method represented by the intelligent sensor network has brought the third revolution in the field of pattern analysis. This kind of method implicitly maps the original spatial data to the feature space through the kernel function [3–5], finds a linear relationship in the feature space, and realizes the efficient solution of nonlinear problems [6]. The failures of subway machinery often show nonlinear behavior [7], and the kernel method is especially suitable for the processing of subway machinery fault diagnosis and mode analysis problems [8].With the rapid development of technology, subway machinery is developing in the direction of large-scale, complex, high-speed, and automation, and the mechanical structure is increasingly complex [9–11]. The failure of a component may cause chain reaction which affects the operation of the entire device [12]. Some equipment in the way of assembly line operation will also affect the subsequent production, causing the entire production process to be interrupted [13]. The loss and harm caused by equipment failure are mainly manifested in the following three aspects: the maintenance and replacement costs of the equipment itself, including the waste of maintenance costs caused by insufficient maintenance and excess maintenance [14].Based on the 5G intelligent sensor network signal processing theory, this paper constructs a fault diagnosis model for subway machinery and equipment. First, the typical fault types of the bearings and gears studied in this paper are introduced. After that, the fault characteristic expression of the bearings and gears is analyzed. The experimental results are compared, and the fault diagnosis methods are found, and the possible causes are then analyzed. When a system fails, various quantities (measurable or unmeasurable) in the system, or parts of them, exhibit properties different from the normal state; the energy entropy features, time series features, and correlation dimension features of different frequency bands obtained by time-domain statistical features, wavelet packet decomposition, and empirical mode decomposition are extracted experimentally to form 11 subway mechanical fault feature libraries, which provide data for the application of nuclear methods, by introducing adaptive noise components, the one-dimensional observation signal is expanded into a multi-dimensional vector, and then, KICA (component analysis) is performed to achieve signal-to-noise separation and achieve the purpose of denoising. The KICA denoising process is not affected by the signal-to-noise ratio and has incomparable advantages over other denoising methods. The denoising example of the rotor unbalanced vibration signal verifies the effectiveness of the method. The measured signal analysis shows that it has a good identification of rolling bearing and gear faults and can be used as a sensitive feature for fault diagnosis.


## 2. Related Work

The fault diagnosis of subway machinery determines the nature, degree, category, location, cause, development trend of the fault through signal detection, feature extraction, state recognition, and diagnosis decision-making [15]. The content involves mechanical structure, electronic technology, statistical mathematics, and computer science [16]. The purpose is to make timely and accurate judgments on various early abnormal states of equipment, so as to take corresponding measures to prevent failures, and to provide guidance for the formulation of equipment inspection and maintenance systems [17], fully tap the potential of the equipment under the conditions allowed by the design, prolong the service life of the equipment, and reduce the cost of the whole life cycle of the equipment: provide a reference and basis for the modification of the equipment structure and the optimal design.Cui [18] proposed a fault detection method based on hidden Markov model (HMM), which first trains the HMM with normal and fault signals and then compares the probability of the observed data. For multifault detection, the method is not applicable when the uncertain state needs to be processed and recorded to cover all necessary information. Liu et al. [19] proposed a new Bayesian classifier model that removes the basic assumption of independence between Naive Bayesian features. Aiming at the problem that the performance improvement of the nearest neighbor classifier is limited by the noisy, redundant, and weakly correlated features, Rahman et al. [20] proposed a new feature weighting scheme based on the combination of the imputation method and the Kolmogorov-Smirnov no-parameter statistical test.The optimal bandwidth selection is used in the model to estimate the class function, and then, three indicators are used, namely, classification accuracy, area under the receiver-operating characteristic curve. Fang et al. [21] first made a preliminary review of evidence theory and expounded how to construct multisensor engine diagnosis problems under the framework of evidence theory from the aspects of fault identification framework, quality function, and evidence combination rules [22]; secondly, new methods are proposed to improve the effectiveness of quality functions in modelling and combining evidence; finally, a rational decision criterion is proposed, and a criterion for evaluating the performance of information fusion systems is proposed [23].


## 3. Construction of a Fault Diagnosis Model for Subway Machinery and Equipment Based on 5G Intelligent Sensor Network Signal Processing

### 3.1. 5G Network Layers

The 5G network uses dual-core nodes to carry out embedded software design to meet the system application requirements, builds a layered network monitoring architecture for mechanical fault diagnosis wireless sensor networks, and specifies and explains the transmission format of multilevel fusion command packets and data packets. Each acquisition node needs to be able to ensure the synchronous acquisition of vibration signalsxt,k. The multichannel synchronous sampling and holding device vt−k is used to synchronously collect the vibration signals of each sensor htdt. The acquisition can only be controlled by the local clock maintained by each acquisition node, and it is difficult to ensure synchronous acquisition.
(1)∫xt,kvt−kdt=1−t,dht,sigmah′tdt=hsigma.Different eigenvalues can reflect different meanings in the signalcosa−b: the mean value mainly reflects whether the signal has a trend or not, which is the first-order moment sina/b; the absolute mean value reflects the energy of the signal, which is also the first-order moment; the variance reflects the dynamic component of the signal energy, which is the second-order center. Among them, the fault detection is to judge whether the diagnosed system has a fault from the estimation of measurable or unmeasurable variables. The variance and standard deviation together reflect the degree of dispersion of the signal; the root mean square value reflects the impact of the signal yt,t−1.
(2)sinab+cosa−b∗ra−b=1,∫yt,t−1−xt,t−1h′tdt=1−t2.The stability of the peak index and the sensitivity to shock are not good, so in the fault diagnosis, this index is gradually replaced by the kurtosis indexpt,t−1; the pulse index and the peak index are similar and can be used for detection and describe the distribution state of the signal; the margin index can be used to detect the wear of mechanical equipment; the kurtosis index is used to indicate the smoothness of the waveform, which can be used to describe the distribution of variables, is the response to the impulse characteristics of the signal pt+1,t, and is currently the fourth-order moment that best reflects the signal transformation.
(3)pt,t−1>1−d>pt+1,t,1−p∑nat,nt∗bmt,t∗expwt−c>0.The impulse index, peak index, and kurtosis index are all sensitive to shock-type faults, especially when the fault is in the early stage of development, these indicators all increase significantly, but when the fault occurs to a certain extent, as the fault degree continues to deepen, these indicators will decline. This shows that these three indicators have high sensitivity to early failure, but the stabilityat−tc is not good.
(4)∑at−tcxt−1−miue,et=0.For two types of typical parts of subway machinery failure simulation experimental datamiue,et, with the aid of the same classification model, the weighted feature set based on multimeasure optimal mixed evaluation can obtain better than the traditional set. The higher accuracy of the feature set provides direct evidence for the improved classification performance of the feature based on the proposed feature sensitivity learning method based on the multimeasure optimal mixture model.The number of input layer and output layer nodes in Figure1 is set to 5 and 10, corresponding to the number of input features and the number of output categories, respectively, and the number of hidden layer nodes is determined to be 16 according to the geometric pyramid rule. The number of training samples in each feature set of each group of experiments is 30, and the number of test samples is 20. After repeated execution 20 times, the classification results of 2 different feature sets based on 3 different classification models in this experiment are obtained, and LibSVM9 is still used for SVM (shared virtual memory) calculation, the kernel function uses Gaussian kernel function; one-to-one classification model is used for multiclass discrimination.Figure 1
5G network hierarchy topology.


### 3.2. Calculation of Fault Characteristic Frequency

The vibration acceleration time series under the fault characteristic frequency state is used as the input, and the multidomain multiclass feature sensitivity learning method based on the multimeasure optimal mixed evaluation model proposed in this chapter is analyzed and evaluated according to the same steps.In the broad sense, it is usually used as a general term for fault detection, separation, and identification; in the narrow sense, it refers specifically to fault separation and fault identification. First, by analyzing the original vibration signal in time domain, frequency domain, and time-frequency domain based on EMD (electric motor driven), a 10 time-domain statistical featurexi, 5 frequency-domain statistical features, and 5 EMD-based statistical features ∣w−x∣ are extracted.
(5)x−11−n∑xi⋅w−x−1>x−1,x1−a−b;x1−a−b¯;x1−p,p⊆X.Unlike T1, which can only obtain the spectrumx1−p, T2 can obtain the time spectrum, that is, to know the frequency components contained in the signal at each time position. Therefore, it can be used to process complex time-varying signals. The above analysis results show that in the gearbox fault simulation experimental data, the proposed feature weighting method based on the optimal mixed evaluation model is better than the traditional single-measure evaluation model in improving the clustering performance set x−t−1.
(6)xp,1−p=maxx−t,x−t−1,∑x−tx−xt−1¯−x¯t,et=1−x.Compared with wavelet transformet, which has the disadvantages of poor high-frequency frequency resolution and low-frequency time resolution, wavelet packet transform further decomposes the high-frequency part of the frequency x1−p. It is beneficial to obtain the optimal basis of the analyzed signal maxx−t−1.
(7)demaxxsp−x1−p=x−t−1x−t,gx,y=1−1n∑i=1npix,iy−lgpx−1,y−1.Through the wavelet transformgx,y, the effective signal will generate a large modulus value pix,iy, while the noise undergoes the wavelet transform, and the modulus value in the wavelet domain still shows strong randomness. Therefore, a suitable number can be selected as the threshold value xw,v. If the decomposed coefficient is greater than this threshold, it can be considered that the signal is likely to be a useful signal, and it should be retained or shrunk according to a certain scale ew−v.
(8)xw,v=ew−1e1−v−ew−v∑ew−vi.Whenever a vector is submitted, there will be an output layer neuron, and this neuron is set as the winning neuron. Then, the winning neuron will move a certain distance in the direction of the input vector and finally realize the spatial self-classification of the input vector through multiple iterations. For the experimental data of rolling bearing faults, compared with the calculation results of the three different types of divergence mean values based on the optimal feature subset of G1, the weighted feature set based on the multimeasure optimal combination sensitivity learning method proposed in this chapter sacrifices divergence.In the process of networking, after each child node in Figure2 is successfully associated with the parent node, it will send an address relation packet containing the node address, parent node address, and network depth to the coordinator, and the coordinator will then send the address relation packet to the coordinator. Upload it to the upper computer monitoring system, and draw a topology diagram on the network topology display interface based on this, display the cluster tree network topology in the network and the relationship between parent and child nodes in real time, and the fault bearing vibration signal is transmitted to the base through the bearing support components. We analyze the vibration signals of the 4 bases collected in the experiment, take the vibration signals of the normal state, the outer ring fault, and the ball fault when the rotation frequency is 30 Hz, 35 Hz, and 40 Hz, respectively, and extract the original signal.Figure 2
Calculation of fault characteristic frequency.


### 3.3. Intelligent Sensor Signal Analysis

The multisensor information fusion method can obtain more information due to the expansion of the space-time range and can describe the object to be diagnosed more comprehensively. 12 time-domain statistics, pulse factor, and margin factor are used as feature quantities, and the extracted multisensor feature quantities are fused and analyzed to diagnose rotor failures in normal operation and 3 mm cracks and 5 mm cracks. Taking the rotor crack vibration signals collected in the experiment in 3 states, form a 4-dimensional eigenvector with 1 statistic of 4 sensors on the base in each set of data, and extract the eigenvectors of 100 sets of data in each state which are used for training the SVM model, and the rest of the samples are used for testing in Table1.Table 1
Dataset description.
DatasetSample aSample bSample cSample d10.670.560.030.2220.120.260.010.5530.400.180.010.9440.040.520.030.8150.820.070.000.0460.380.290.010.60In order to analyze the redundancy between the signals of two eddy current displacement sensors in the same section of subway machinery and between the signals of vibration sensors in different sections, taking the vibration data collected by the l-cap unit as an example, the vibration signals collected by each sensor were analyzed. The vibration amplitudes in theY direction at the 1-band bearing, the 3# bearing, and the 5-support bearing are all larger than the X-direction, and the vibration amplitudes in the two directions at the 3# bearing and the 5-support bearing are quite different. The vibration amplitudes in the X direction and the Y direction at the bearing and the clamp bearing are relatively similar. According to the vibration trends in the direction at each bearing, it can be found that the vibration intensity of the 3# bearing is the largest, followed by the 5-group bearing, and the 2-support bearing again. Among them, the fault detection is to judge whether the diagnosed system has a fault from the estimation of measurable or unmeasurable variables. From the vibration trend in each bearing section, it can be concluded that the vibration intensities of the vibration sensors in two directions in the same section are sometimes similar and sometimes different.Gears and gearboxes are important components in mechanical equipment. About 80% of the failures in the transmission machinery in Figure3 and about 10% of the failures in the rotating machinery are caused by gears. Therefore, the running status of the gears is monitored and analyzed. The difficulty in diagnosing gears is that the vibration signal passes through many links in the transmission (gear-shaft-bearing-bearing seat-gearbox-measurement point), the attenuation loss of characteristic information is large, and the vibration of various parts is complex. Under the action, there are many interference excitations in the signal. Therefore, the vibration signal components of the field test are complex, and the characteristic information reflecting the gear state is relatively weak. If a single sensor signal is analyzed, it is necessary to rely on more detailed signal analysis technology to achieve the purpose of improving the signal-to-noise ratio and effectively extracting fault features. Compared with a single sensor, multiple sensors expand the scope of time and space, obtain more information, and describe the diagnostic object more comprehensively.Figure 3
Intelligent sensor signal analysis.


### 3.4. Indication of Mechanical Equipment Failure

In order to analyze the redundancy between the signals of two eddy current displacement sensors in the same section of subway machinery and between the signals of vibration sensors in different sections, taking the collected vibration data as an example, the similarity of the vibration signals collected by each sensor is carried out. It can be seen that the correlation entropy of the two sensor signals in each section is 0.95, the correlation entropy value of the two sensor signals in the 5-way bearing section is the largest. If measured according to the redundancy index based on correlation entropy, then the redundancy of the two sensor signals in this section is the largest, but the actual situation is exactly the opposite, the information provided by the two direction sensors in the 5-support bearing section is very different. By extracting the signal and calculating the similarity value between the eigenvectors of the two sensor signals, the similarity value is the smallest in all sections.The sudden change point is called the singular point. Compared with the stationary signal, the singular point contains fault information. Therefore, if the redundant information between sensor signals is measured according to the redundancy calculation based on correlation entropy, there is a great limitation in the diagnosis and identification of subway machinery faults based on the fusion of feature level and decision level.The network self-organizing topology in Figure4 is completed. Before starting the acquisition, it is necessary to set the sampling parameters through the parameter design window according to the specific application requirements. There are four sampling frequencies to choose from, the signal amplification can be set to 1x, 2x, 5x, 10x, and 20x, and the vibration data collected after setting the file name is saved on the SD card with the corresponding file ID; set the trigger voltage and range. After the acquisition parameters are configured, start the acquisition operation, and send the command parameters to the gateway node through the serial port. The terminal node divides the data packets and transmits them to the gateway node in turn. The upper computer monitoring system is responsible for saving these data as files in a specific format for further analysis and application.Figure 4
Failure representation of mechanical equipment.


## 3.1. 5G Network Layers

The 5G network uses dual-core nodes to carry out embedded software design to meet the system application requirements, builds a layered network monitoring architecture for mechanical fault diagnosis wireless sensor networks, and specifies and explains the transmission format of multilevel fusion command packets and data packets. Each acquisition node needs to be able to ensure the synchronous acquisition of vibration signalsxt,k. The multichannel synchronous sampling and holding device vt−k is used to synchronously collect the vibration signals of each sensor htdt. The acquisition can only be controlled by the local clock maintained by each acquisition node, and it is difficult to ensure synchronous acquisition.
(1)∫xt,kvt−kdt=1−t,dht,sigmah′tdt=hsigma.Different eigenvalues can reflect different meanings in the signalcosa−b: the mean value mainly reflects whether the signal has a trend or not, which is the first-order moment sina/b; the absolute mean value reflects the energy of the signal, which is also the first-order moment; the variance reflects the dynamic component of the signal energy, which is the second-order center. Among them, the fault detection is to judge whether the diagnosed system has a fault from the estimation of measurable or unmeasurable variables. The variance and standard deviation together reflect the degree of dispersion of the signal; the root mean square value reflects the impact of the signal yt,t−1.
(2)sinab+cosa−b∗ra−b=1,∫yt,t−1−xt,t−1h′tdt=1−t2.The stability of the peak index and the sensitivity to shock are not good, so in the fault diagnosis, this index is gradually replaced by the kurtosis indexpt,t−1; the pulse index and the peak index are similar and can be used for detection and describe the distribution state of the signal; the margin index can be used to detect the wear of mechanical equipment; the kurtosis index is used to indicate the smoothness of the waveform, which can be used to describe the distribution of variables, is the response to the impulse characteristics of the signal pt+1,t, and is currently the fourth-order moment that best reflects the signal transformation.
(3)pt,t−1>1−d>pt+1,t,1−p∑nat,nt∗bmt,t∗expwt−c>0.The impulse index, peak index, and kurtosis index are all sensitive to shock-type faults, especially when the fault is in the early stage of development, these indicators all increase significantly, but when the fault occurs to a certain extent, as the fault degree continues to deepen, these indicators will decline. This shows that these three indicators have high sensitivity to early failure, but the stabilityat−tc is not good.
(4)∑at−tcxt−1−miue,et=0.For two types of typical parts of subway machinery failure simulation experimental datamiue,et, with the aid of the same classification model, the weighted feature set based on multimeasure optimal mixed evaluation can obtain better than the traditional set. The higher accuracy of the feature set provides direct evidence for the improved classification performance of the feature based on the proposed feature sensitivity learning method based on the multimeasure optimal mixture model.The number of input layer and output layer nodes in Figure1 is set to 5 and 10, corresponding to the number of input features and the number of output categories, respectively, and the number of hidden layer nodes is determined to be 16 according to the geometric pyramid rule. The number of training samples in each feature set of each group of experiments is 30, and the number of test samples is 20. After repeated execution 20 times, the classification results of 2 different feature sets based on 3 different classification models in this experiment are obtained, and LibSVM9 is still used for SVM (shared virtual memory) calculation, the kernel function uses Gaussian kernel function; one-to-one classification model is used for multiclass discrimination.Figure 1
5G network hierarchy topology.


## 3.2. Calculation of Fault Characteristic Frequency

The vibration acceleration time series under the fault characteristic frequency state is used as the input, and the multidomain multiclass feature sensitivity learning method based on the multimeasure optimal mixed evaluation model proposed in this chapter is analyzed and evaluated according to the same steps.In the broad sense, it is usually used as a general term for fault detection, separation, and identification; in the narrow sense, it refers specifically to fault separation and fault identification. First, by analyzing the original vibration signal in time domain, frequency domain, and time-frequency domain based on EMD (electric motor driven), a 10 time-domain statistical featurexi, 5 frequency-domain statistical features, and 5 EMD-based statistical features ∣w−x∣ are extracted.
(5)x−11−n∑xi⋅w−x−1>x−1,x1−a−b;x1−a−b¯;x1−p,p⊆X.Unlike T1, which can only obtain the spectrumx1−p, T2 can obtain the time spectrum, that is, to know the frequency components contained in the signal at each time position. Therefore, it can be used to process complex time-varying signals. The above analysis results show that in the gearbox fault simulation experimental data, the proposed feature weighting method based on the optimal mixed evaluation model is better than the traditional single-measure evaluation model in improving the clustering performance set x−t−1.
(6)xp,1−p=maxx−t,x−t−1,∑x−tx−xt−1¯−x¯t,et=1−x.Compared with wavelet transformet, which has the disadvantages of poor high-frequency frequency resolution and low-frequency time resolution, wavelet packet transform further decomposes the high-frequency part of the frequency x1−p. It is beneficial to obtain the optimal basis of the analyzed signal maxx−t−1.
(7)demaxxsp−x1−p=x−t−1x−t,gx,y=1−1n∑i=1npix,iy−lgpx−1,y−1.Through the wavelet transformgx,y, the effective signal will generate a large modulus value pix,iy, while the noise undergoes the wavelet transform, and the modulus value in the wavelet domain still shows strong randomness. Therefore, a suitable number can be selected as the threshold value xw,v. If the decomposed coefficient is greater than this threshold, it can be considered that the signal is likely to be a useful signal, and it should be retained or shrunk according to a certain scale ew−v.
(8)xw,v=ew−1e1−v−ew−v∑ew−vi.Whenever a vector is submitted, there will be an output layer neuron, and this neuron is set as the winning neuron. Then, the winning neuron will move a certain distance in the direction of the input vector and finally realize the spatial self-classification of the input vector through multiple iterations. For the experimental data of rolling bearing faults, compared with the calculation results of the three different types of divergence mean values based on the optimal feature subset of G1, the weighted feature set based on the multimeasure optimal combination sensitivity learning method proposed in this chapter sacrifices divergence.In the process of networking, after each child node in Figure2 is successfully associated with the parent node, it will send an address relation packet containing the node address, parent node address, and network depth to the coordinator, and the coordinator will then send the address relation packet to the coordinator. Upload it to the upper computer monitoring system, and draw a topology diagram on the network topology display interface based on this, display the cluster tree network topology in the network and the relationship between parent and child nodes in real time, and the fault bearing vibration signal is transmitted to the base through the bearing support components. We analyze the vibration signals of the 4 bases collected in the experiment, take the vibration signals of the normal state, the outer ring fault, and the ball fault when the rotation frequency is 30 Hz, 35 Hz, and 40 Hz, respectively, and extract the original signal.Figure 2
Calculation of fault characteristic frequency.


## 3.3. Intelligent Sensor Signal Analysis

The multisensor information fusion method can obtain more information due to the expansion of the space-time range and can describe the object to be diagnosed more comprehensively. 12 time-domain statistics, pulse factor, and margin factor are used as feature quantities, and the extracted multisensor feature quantities are fused and analyzed to diagnose rotor failures in normal operation and 3 mm cracks and 5 mm cracks. Taking the rotor crack vibration signals collected in the experiment in 3 states, form a 4-dimensional eigenvector with 1 statistic of 4 sensors on the base in each set of data, and extract the eigenvectors of 100 sets of data in each state which are used for training the SVM model, and the rest of the samples are used for testing in Table1.Table 1
Dataset description.
DatasetSample aSample bSample cSample d10.670.560.030.2220.120.260.010.5530.400.180.010.9440.040.520.030.8150.820.070.000.0460.380.290.010.60In order to analyze the redundancy between the signals of two eddy current displacement sensors in the same section of subway machinery and between the signals of vibration sensors in different sections, taking the vibration data collected by the l-cap unit as an example, the vibration signals collected by each sensor were analyzed. The vibration amplitudes in theY direction at the 1-band bearing, the 3# bearing, and the 5-support bearing are all larger than the X-direction, and the vibration amplitudes in the two directions at the 3# bearing and the 5-support bearing are quite different. The vibration amplitudes in the X direction and the Y direction at the bearing and the clamp bearing are relatively similar. According to the vibration trends in the direction at each bearing, it can be found that the vibration intensity of the 3# bearing is the largest, followed by the 5-group bearing, and the 2-support bearing again. Among them, the fault detection is to judge whether the diagnosed system has a fault from the estimation of measurable or unmeasurable variables. From the vibration trend in each bearing section, it can be concluded that the vibration intensities of the vibration sensors in two directions in the same section are sometimes similar and sometimes different.Gears and gearboxes are important components in mechanical equipment. About 80% of the failures in the transmission machinery in Figure3 and about 10% of the failures in the rotating machinery are caused by gears. Therefore, the running status of the gears is monitored and analyzed. The difficulty in diagnosing gears is that the vibration signal passes through many links in the transmission (gear-shaft-bearing-bearing seat-gearbox-measurement point), the attenuation loss of characteristic information is large, and the vibration of various parts is complex. Under the action, there are many interference excitations in the signal. Therefore, the vibration signal components of the field test are complex, and the characteristic information reflecting the gear state is relatively weak. If a single sensor signal is analyzed, it is necessary to rely on more detailed signal analysis technology to achieve the purpose of improving the signal-to-noise ratio and effectively extracting fault features. Compared with a single sensor, multiple sensors expand the scope of time and space, obtain more information, and describe the diagnostic object more comprehensively.Figure 3
Intelligent sensor signal analysis.


## 3.4. Indication of Mechanical Equipment Failure

In order to analyze the redundancy between the signals of two eddy current displacement sensors in the same section of subway machinery and between the signals of vibration sensors in different sections, taking the collected vibration data as an example, the similarity of the vibration signals collected by each sensor is carried out. It can be seen that the correlation entropy of the two sensor signals in each section is 0.95, the correlation entropy value of the two sensor signals in the 5-way bearing section is the largest. If measured according to the redundancy index based on correlation entropy, then the redundancy of the two sensor signals in this section is the largest, but the actual situation is exactly the opposite, the information provided by the two direction sensors in the 5-support bearing section is very different. By extracting the signal and calculating the similarity value between the eigenvectors of the two sensor signals, the similarity value is the smallest in all sections.The sudden change point is called the singular point. Compared with the stationary signal, the singular point contains fault information. Therefore, if the redundant information between sensor signals is measured according to the redundancy calculation based on correlation entropy, there is a great limitation in the diagnosis and identification of subway machinery faults based on the fusion of feature level and decision level.The network self-organizing topology in Figure4 is completed. Before starting the acquisition, it is necessary to set the sampling parameters through the parameter design window according to the specific application requirements. There are four sampling frequencies to choose from, the signal amplification can be set to 1x, 2x, 5x, 10x, and 20x, and the vibration data collected after setting the file name is saved on the SD card with the corresponding file ID; set the trigger voltage and range. After the acquisition parameters are configured, start the acquisition operation, and send the command parameters to the gateway node through the serial port. The terminal node divides the data packets and transmits them to the gateway node in turn. The upper computer monitoring system is responsible for saving these data as files in a specific format for further analysis and application.Figure 4
Failure representation of mechanical equipment.


## 4. Application and Analysis of Fault Diagnosis Model for Subway Machinery and Equipment Based on 5G Intelligent Sensor Network Signal Processing

### 4.1. 5G Network Data Preprocessing

Take 8 time-domain statistics of a single sensor network (the first 8 features with high diagnostic accuracy) to form an 8-dimensional feature vector to form a data sample. In order to avoid the influence of the magnitude difference of different feature quantities on the SVM classification results, the feature vectors are normalized before SVM training and testing. In the multisensor information fusion, the same feature quantity of multiple sensors is used as the feature vector, there is no order of magnitude difference, and no normalization is required, so the process is simpler. The diagnostic accuracy of multisensor information fusion is higher than that of a single sensor.By observing the low-frequency part, the type of node failure can be judged; the occurrence time of the point is included in the high frequency part. By selecting the diagnostic accuracy of the sensors at different positions, it is possible to roughly understand the location where the sensors are installed better, and the results can provide the optimal placement of the sensors. In the method, since the arrangement of the sensors covers the entire surface of the gearbox, the arrangement position of Table2 has little influence on the diagnosis results, and the diagnosis results are more reliable.Table 2
5G network data preprocessing.
Network typeFusion caseSensor typeDiagnostic resultLocation pointAccuracy valueAPittingHigh frequencyDislocationBack section0.55883BLine etchWirelessDislocationBack section0.29502CLine etchWirelessDislocationFront section0.41198DPittingWirelessFractureFront section0.67301EPittingHigh frequencyFractureFront section0.43221The closeness values of the 9 groups of samples to be identified and each fault standard mode are shown in the figure. From the closeness value between the sample to be identified and various failure modes, it can be seen that the state identification results obtained by each sample according to the principle of maximum closeness are consistent with its own target state, and the fault identification rate reaches 100%. Thus, the effectiveness of the subway machinery fault identification method based on vector vibration signal entropy characteristics and fuzzyC-means clustering algorithm can be proved. The similarity function value between the two sensor signal eigenvectors in the 3-support bearing is the largest, indicating that the signal characteristic information provided by the two signals is very similar, followed by the similarity function value of the two sensor signals in the 2-bearing cross-section. This redundancy index value can more truly reflect the magnitude of redundancy between signals and has more reliable guidance for the diagnosis and identification of subway machinery faults using feature-level and decision-level fusion.


### 4.2. Intelligent Sensor Signal Fitting

Arrange 4 terminal nodes (numbers 2, 3, 5, and 6), 2 cluster head nodes (numbers 1 and 4), 1 gateway node near the input bearing housing and output bearing housing of the intelligent sensing signal drive. The hierarchical coordination address allocation algorithm is used to form a cluster tree network structure, turn on the experimental bench, set the rotational speed, and wait for the gearbox to run stably before collecting vibration signals. The vibration data is stored on the SD card of the sensor node, and multilevel fusion processing of the wireless sensor network is performed at the same time. 4 terminal nodes (numbered 2, 3, 5, and 6), 2 cluster head nodes (numbered 1 and 4), and 1 gateway node are arranged near the drive input bearing housing and the output bearing housing. Turn on the experimental bench, set the rotational speed, and wait for the gearbox to run stably before collecting vibration signals. The vibration data is stored on the SD card of the sensor node, and multilevel fusion processing of the wireless sensor network is performed at the same time.The diagnostic accuracy of the total test samples can reflect the sensitivity of the feature quantities to gear faults. As can be seen in Figure5, the crest factor is the most sensitive to gear faults. Using its constituent eigenvectors, the diagnostic accuracy is 93.33%, followed by the sum of the squares of the amplitudes, the root amplitude, the mean value, and diagnostic accuracy are all above 80%. This feature is especially suitable for qualitative fault diagnosis (judging whether there is a fault) when the new equipment still lacks fault samples, so it is necessary to carefully select the feature quantities, such as the peak factor; the diagnostic accuracy of samples in various states is high.Figure 5
Smart sensor signal fitting.


### 4.3. Simulation of Fault Diagnosis of Subway Machinery and Equipment

The equipment fault experimental data comes from the bearing fault diagnosis test bench, and three acceleration sensors are arranged, and the positions are as shown. The bearings used in the experiment include normal bearings (no fault), bearings with faulty outer ring (damage area about 7 mm2, depth 0.2 mm), with faulty ring (damage area about 3.8 mm2, depth 0.1 mm) and rolling bearings body failure bearing (damage area is about 3 mm2, depth is 0.1 mm). The bearing model is 6308, the number of rolling elements is 8, and the rolling element diameter is 15 mm. The cluster head node receives 120 sets of samples from each terminal node and inputs it into the 5G network for network learning to generate a fault diagnosis classifier. To reduce the learning pressure of the cluster head node network, the training target error is set to 0.05. The input layer of the 5G network is determined by the feature dimension as 5; set the hidden layer to 3 and 1 bias neuron. Considering the three operating states of the gearbox, the output layer is designed to be 3, namely, normal state, tooth root damage, and bearing inner ring damage. By comparing the high frequency signal distribution of the resolution of each layer, the detail peak time is the generation time of the singular point, that is, the fault occurrence time.Although fuzzy set theory based on stochastic framework in Figure6 is a powerful tool for analyzing uncertain information, the fuzzy data set is a one-element array, that is, it can only express two states of agreement or disagreement, while bearing failure is an uncertain state. First of all, it is a troublesome task to determine the energy ratio of each frequency component in the signal spectrum, especially when the frequency components are rich, which makes it often inconvenient in actual diagnosis; another example is for some faults. The eigenvector obtained from the spectrum is not necessarily the common feature of the fault. For example, for the rubbing fault of the moving and static parts, the rubbing signal is generated within a short time of the rubbing of the moving and static parts. The feature vector obtained from the signal spectrum is used, but the wavelet is used to directly extract the features of the time domain signal, and then, the extracted features are formed as input for the learned network to judge. The characteristic of this method is that only the signal in the time domain is used.Figure 6
Equipment fault diagnosis structure.Selecting the damaged operation state of the bearing inner ring at the drive input end as the unknown state for 5G network testing in Figure7, the terminal nodes in the cluster construct a set of characteristic information samples under the operation state of the gearbox at the same time, and the cluster head node reads the corresponding 5G data from the SD card classifier parameters; input the feature information samples into the classifier to obtain the probability distribution function of the pattern to be recognized. The gateway node uses the fusion decision theory to make fusion decisions for each evidence body and uses the fusion function as the basis for decision-making to identify the fault type. 30 groups of tests were carried out, among which the probability distribution and fusion decision results of three groups of tests are as shown, and only four decimal places are reserved.Figure 7
Distribution of operating states of mechanical equipment failures.


### 4.4. Example Application and Analysis

Taking the vibration signals of the rolling bearing in 4 states collected by the experiment, 110 sets of 440 sets of data in each state, and 1 time-domain statistic of 8 sensors is extracted from each set of data to form an 8-dimensional feature vector, forming a440×8 data sample set, a total of 200 samples of 50 groups in each state are used for training the SVM model, and the remaining samples are used for testing. The kernel function selects Gaussian kernel function, adopts cross-validation and network search method to optimize the selection of penalty factor C and kernel function parameter g, and adopts one-to-one for multiclass discrimination.Therefore, the fault occurrence time is the content of concern. Once the fault occurrence time is determined, the “redundant” data after the fault time can be effectively restored or discarded. The input of the model is different types of time series signals collected by multiple sensors, and the output is the diagnosis result, which realizes the end-to-end diagnosis. Secondly, during the operation of the entire model, the adaptive adjustment of the internal structure can be achieved without manual participation. The adaptive steps include the conversion, the automatic fusion of time series signals of different sizes into pictures of a given size, and the training of ResNet, which are all conducive to the sharing of parameters in Table3.Table 3
Analysis of vibration signal of subway machinery.
Vibration signal setAdaptive selection codesDifferent sizes intoxt,t−1Define rand_01 ((float)rand())Optimize the selectionsina/bConstintnumofdims=30;Adopts cross-validationConstintnumofparticles=50;Network search methodcosa−bUsing namespace std;A given sizeh′tdtTypedef void (∗fitnessfunc))Pictures ofew−1Float fitnesses[numofparticles]Penalty factorc(Float)rand_maxKernel function parametergFloat x[numofparticles][numofdims]Data-driven randomc−tVoid fitnessfunc(float x[numofparticles][numofdims])Intuitionistic fuzzy setsra−bFloat fitnesses[numofparticles]At present, most of the fault diagnosis work is carried out under the same operating environment, that is, the same experimental platform and the same background temperature. Due to the same operating environment,n−m sets of data and m sets of data have high consistency and similarity and are considered to be essentially consistent. Therefore, this paper replaces the bearing with a known fault and then follows the above steps. The diagnosis results around the 1X eigenfrequency show that there is an outer ring fault in the rolling bearing, which shows that the experimental record is good.Compared with gears, the diagnostic accuracy of rolling bearings is obviously higher. The multisensor signals collected in Figure8 contain more fault information. Among the dimensioned time domain statistics, the mean value, the sum of the squares of the amplitudes, the root mean square value, the root square amplitude value, and the standard deviation are all 100% accurate for the diagnosis of rolling bearings. In the dimensionless, the shape factor is the most sensitive to the fault information, and in the gear fault diagnosis, the crest factor is the most sensitive to gear fault information, while the shape factor has a lower diagnostic accuracy for gears. In contrast, the remaining four sets based on the single-measure model achieved classification accuracies of 81.67%, 89.17%, 82.50%, and 70.83%, respectively. The above results show that, in the gearbox fault experimental data, the feature weighting method based on the multimeasure optimal mixed evaluation model proposed in this chapter can improve the classification performance of the original feature set to the greatest extent and can achieve better performance and the feature weighting method of the single-measure evaluation model such as distance and information can better improve the classification performance.Figure 8
Signal characteristic analysis of sensor network.


## 4.1. 5G Network Data Preprocessing

Take 8 time-domain statistics of a single sensor network (the first 8 features with high diagnostic accuracy) to form an 8-dimensional feature vector to form a data sample. In order to avoid the influence of the magnitude difference of different feature quantities on the SVM classification results, the feature vectors are normalized before SVM training and testing. In the multisensor information fusion, the same feature quantity of multiple sensors is used as the feature vector, there is no order of magnitude difference, and no normalization is required, so the process is simpler. The diagnostic accuracy of multisensor information fusion is higher than that of a single sensor.By observing the low-frequency part, the type of node failure can be judged; the occurrence time of the point is included in the high frequency part. By selecting the diagnostic accuracy of the sensors at different positions, it is possible to roughly understand the location where the sensors are installed better, and the results can provide the optimal placement of the sensors. In the method, since the arrangement of the sensors covers the entire surface of the gearbox, the arrangement position of Table2 has little influence on the diagnosis results, and the diagnosis results are more reliable.Table 2
5G network data preprocessing.
Network typeFusion caseSensor typeDiagnostic resultLocation pointAccuracy valueAPittingHigh frequencyDislocationBack section0.55883BLine etchWirelessDislocationBack section0.29502CLine etchWirelessDislocationFront section0.41198DPittingWirelessFractureFront section0.67301EPittingHigh frequencyFractureFront section0.43221The closeness values of the 9 groups of samples to be identified and each fault standard mode are shown in the figure. From the closeness value between the sample to be identified and various failure modes, it can be seen that the state identification results obtained by each sample according to the principle of maximum closeness are consistent with its own target state, and the fault identification rate reaches 100%. Thus, the effectiveness of the subway machinery fault identification method based on vector vibration signal entropy characteristics and fuzzyC-means clustering algorithm can be proved. The similarity function value between the two sensor signal eigenvectors in the 3-support bearing is the largest, indicating that the signal characteristic information provided by the two signals is very similar, followed by the similarity function value of the two sensor signals in the 2-bearing cross-section. This redundancy index value can more truly reflect the magnitude of redundancy between signals and has more reliable guidance for the diagnosis and identification of subway machinery faults using feature-level and decision-level fusion.


## 4.2. Intelligent Sensor Signal Fitting

Arrange 4 terminal nodes (numbers 2, 3, 5, and 6), 2 cluster head nodes (numbers 1 and 4), 1 gateway node near the input bearing housing and output bearing housing of the intelligent sensing signal drive. The hierarchical coordination address allocation algorithm is used to form a cluster tree network structure, turn on the experimental bench, set the rotational speed, and wait for the gearbox to run stably before collecting vibration signals. The vibration data is stored on the SD card of the sensor node, and multilevel fusion processing of the wireless sensor network is performed at the same time. 4 terminal nodes (numbered 2, 3, 5, and 6), 2 cluster head nodes (numbered 1 and 4), and 1 gateway node are arranged near the drive input bearing housing and the output bearing housing. Turn on the experimental bench, set the rotational speed, and wait for the gearbox to run stably before collecting vibration signals. The vibration data is stored on the SD card of the sensor node, and multilevel fusion processing of the wireless sensor network is performed at the same time.The diagnostic accuracy of the total test samples can reflect the sensitivity of the feature quantities to gear faults. As can be seen in Figure5, the crest factor is the most sensitive to gear faults. Using its constituent eigenvectors, the diagnostic accuracy is 93.33%, followed by the sum of the squares of the amplitudes, the root amplitude, the mean value, and diagnostic accuracy are all above 80%. This feature is especially suitable for qualitative fault diagnosis (judging whether there is a fault) when the new equipment still lacks fault samples, so it is necessary to carefully select the feature quantities, such as the peak factor; the diagnostic accuracy of samples in various states is high.Figure 5
Smart sensor signal fitting.


## 4.3. Simulation of Fault Diagnosis of Subway Machinery and Equipment

The equipment fault experimental data comes from the bearing fault diagnosis test bench, and three acceleration sensors are arranged, and the positions are as shown. The bearings used in the experiment include normal bearings (no fault), bearings with faulty outer ring (damage area about 7 mm2, depth 0.2 mm), with faulty ring (damage area about 3.8 mm2, depth 0.1 mm) and rolling bearings body failure bearing (damage area is about 3 mm2, depth is 0.1 mm). The bearing model is 6308, the number of rolling elements is 8, and the rolling element diameter is 15 mm. The cluster head node receives 120 sets of samples from each terminal node and inputs it into the 5G network for network learning to generate a fault diagnosis classifier. To reduce the learning pressure of the cluster head node network, the training target error is set to 0.05. The input layer of the 5G network is determined by the feature dimension as 5; set the hidden layer to 3 and 1 bias neuron. Considering the three operating states of the gearbox, the output layer is designed to be 3, namely, normal state, tooth root damage, and bearing inner ring damage. By comparing the high frequency signal distribution of the resolution of each layer, the detail peak time is the generation time of the singular point, that is, the fault occurrence time.Although fuzzy set theory based on stochastic framework in Figure6 is a powerful tool for analyzing uncertain information, the fuzzy data set is a one-element array, that is, it can only express two states of agreement or disagreement, while bearing failure is an uncertain state. First of all, it is a troublesome task to determine the energy ratio of each frequency component in the signal spectrum, especially when the frequency components are rich, which makes it often inconvenient in actual diagnosis; another example is for some faults. The eigenvector obtained from the spectrum is not necessarily the common feature of the fault. For example, for the rubbing fault of the moving and static parts, the rubbing signal is generated within a short time of the rubbing of the moving and static parts. The feature vector obtained from the signal spectrum is used, but the wavelet is used to directly extract the features of the time domain signal, and then, the extracted features are formed as input for the learned network to judge. The characteristic of this method is that only the signal in the time domain is used.Figure 6
Equipment fault diagnosis structure.Selecting the damaged operation state of the bearing inner ring at the drive input end as the unknown state for 5G network testing in Figure7, the terminal nodes in the cluster construct a set of characteristic information samples under the operation state of the gearbox at the same time, and the cluster head node reads the corresponding 5G data from the SD card classifier parameters; input the feature information samples into the classifier to obtain the probability distribution function of the pattern to be recognized. The gateway node uses the fusion decision theory to make fusion decisions for each evidence body and uses the fusion function as the basis for decision-making to identify the fault type. 30 groups of tests were carried out, among which the probability distribution and fusion decision results of three groups of tests are as shown, and only four decimal places are reserved.Figure 7
Distribution of operating states of mechanical equipment failures.


## 4.4. Example Application and Analysis

Taking the vibration signals of the rolling bearing in 4 states collected by the experiment, 110 sets of 440 sets of data in each state, and 1 time-domain statistic of 8 sensors is extracted from each set of data to form an 8-dimensional feature vector, forming a440×8 data sample set, a total of 200 samples of 50 groups in each state are used for training the SVM model, and the remaining samples are used for testing. The kernel function selects Gaussian kernel function, adopts cross-validation and network search method to optimize the selection of penalty factor C and kernel function parameter g, and adopts one-to-one for multiclass discrimination.Therefore, the fault occurrence time is the content of concern. Once the fault occurrence time is determined, the “redundant” data after the fault time can be effectively restored or discarded. The input of the model is different types of time series signals collected by multiple sensors, and the output is the diagnosis result, which realizes the end-to-end diagnosis. Secondly, during the operation of the entire model, the adaptive adjustment of the internal structure can be achieved without manual participation. The adaptive steps include the conversion, the automatic fusion of time series signals of different sizes into pictures of a given size, and the training of ResNet, which are all conducive to the sharing of parameters in Table3.Table 3
Analysis of vibration signal of subway machinery.
Vibration signal setAdaptive selection codesDifferent sizes intoxt,t−1Define rand_01 ((float)rand())Optimize the selectionsina/bConstintnumofdims=30;Adopts cross-validationConstintnumofparticles=50;Network search methodcosa−bUsing namespace std;A given sizeh′tdtTypedef void (∗fitnessfunc))Pictures ofew−1Float fitnesses[numofparticles]Penalty factorc(Float)rand_maxKernel function parametergFloat x[numofparticles][numofdims]Data-driven randomc−tVoid fitnessfunc(float x[numofparticles][numofdims])Intuitionistic fuzzy setsra−bFloat fitnesses[numofparticles]At present, most of the fault diagnosis work is carried out under the same operating environment, that is, the same experimental platform and the same background temperature. Due to the same operating environment,n−m sets of data and m sets of data have high consistency and similarity and are considered to be essentially consistent. Therefore, this paper replaces the bearing with a known fault and then follows the above steps. The diagnosis results around the 1X eigenfrequency show that there is an outer ring fault in the rolling bearing, which shows that the experimental record is good.Compared with gears, the diagnostic accuracy of rolling bearings is obviously higher. The multisensor signals collected in Figure8 contain more fault information. Among the dimensioned time domain statistics, the mean value, the sum of the squares of the amplitudes, the root mean square value, the root square amplitude value, and the standard deviation are all 100% accurate for the diagnosis of rolling bearings. In the dimensionless, the shape factor is the most sensitive to the fault information, and in the gear fault diagnosis, the crest factor is the most sensitive to gear fault information, while the shape factor has a lower diagnostic accuracy for gears. In contrast, the remaining four sets based on the single-measure model achieved classification accuracies of 81.67%, 89.17%, 82.50%, and 70.83%, respectively. The above results show that, in the gearbox fault experimental data, the feature weighting method based on the multimeasure optimal mixed evaluation model proposed in this chapter can improve the classification performance of the original feature set to the greatest extent and can achieve better performance and the feature weighting method of the single-measure evaluation model such as distance and information can better improve the classification performance.Figure 8
Signal characteristic analysis of sensor network.


## 5. Conclusion

In this paper, a fault diagnosis model of subway mechanical equipment is constructed, and a fault diagnosis experiment based on fault characteristic frequency is designed. By comparing the theoretical calculation results and experimental observation results, it is proposed that it may be a noise signal. First, the adaptive wavelet packet algorithm based on heuristic threshold and norm entropy is used to denoise and reconstruct the noisy signal of the bearing; then, the corresponding 6-dimensional data samples of the six time-domain feature index components in the reconstructed signal are extracted, to realize the dimensionality reduction and compression of the data; finally, the network is trained unsupervised with the extracted data samples to realize the fault diagnosis of the bearing in the high-noise sound environment. In this paper, according to the physical characteristics between the perceived quantities of nodes, the node groups of WSN are divided into two categories: node groups with redundant relationship and isolated nodes without redundant relationship with each other. The SVM performs information fusion on the weak signals of the multisensors of the base and effectively realizes the fault diagnosis of rotor cracks and rolling bearings. The installation method of the base sensor is universal, which can overcome the problems of inconvenient installation of on-site sensors and has great application and promotion prospects.



---

*Source: 3266205-2022-09-17.xml*