Research on diagnosis algorithm of mechanical equipment brake friction fault based on MCNN-SVM

Zhang, Xunjie; Zhang, Min; Xiang, Zaiyu; Mo, Jiliang

doi:10.1016/j.measurement.2021.110065

Cited by 20 publications

(20 citation statements)

References 40 publications

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“…Based on this principle, the vibration signal collected using the acceleration sensor can be analyzed to monitor the health status of the friction block. 32 The vibration acceleration of the friction block is collected on the self-developed high-speed train brake scale test bench (Figure 7). The test bench mainly consists of a control system, pad-on-disc system, loading system, and signal acquisition and analysis system (Figure 7(a)).…”

Section: Methodsmentioning

confidence: 99%

Health status monitoring of high-speed train brake pads considering noise under variable working conditions

Kang

Zhang

et al. 2023

Structural Health Monitoring

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The brake pads of high-speed trains operate under complex and variable conditions, and the collected brake signals are easily affected by noise, making monitoring the health status of brake pads more difficult. A multi-representation adaptation network for online monitoring the health status of high-speed train brake pads, which are affected by noise under variable working conditions, is proposed in this study. First, a parameter-sharing deep residual network is used to extract the friction block features of the source and target domain data. Then, the features are mapped to different low-dimensional feature spaces through the inception adaptation module, and multiple representations are obtained. The network applies conditional maximum mean discrepancy to align representations of the source and target domains, thus learning multiple domain-invariant representations. Hence, the network acquires more knowledge of the friction block status and attenuates the interference of noise signals on the status monitoring. The friction block vibration data were collected from various brake disc speeds, and variable working condition-transfer experiments under the influence of noise were performed on the brake friction and bearing datasets. The results show that the proposed network outperforms other transfer methods, which can better extract and identify the status features of the friction block under the noise interference.

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Section: Methodsmentioning

confidence: 99%

Health status monitoring of high-speed train brake pads considering noise under variable working conditions

Kang

Zhang

et al. 2023

Structural Health Monitoring

Self Cite

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“…The value range of i is from 1 to N, and the value range of a is from 1 to m 2 . (a) The original signal L is converted into an original basis matrix (see equation ( 13)); then, we obtain the original normalized matrix (ONM) from the maximum and minimum values using equation ( 14): (14) where ONM i is the original term normalized matrix for sample i. (b) The trend term signal T is transformed into a trend basis matrix (see equation (15)); then, we obtain the trend normalized matrix (TNM) from the maximum and minimum values using equation ( 16):…”

Section: Rgb Image Conversion and Time Series Splicingmentioning

confidence: 99%

“…It builds different layer types to meet specific tasks, making it easy to automatically learn features and implement fault recognition from preprocessed data. Various DL models have been applied to fault recognition tasks, such as autoencoders [11], the residual network (ResNet) [12], long short-term memory (LSTM) [13], and the convolutional neural network (CNN) [14]. They are available for different types of data, such as spatial feature data and temporal feature data, in fault recognition.…”

Section: Introductionmentioning

confidence: 99%

Fault pattern recognition of rolling bearing based on smoothness prior approach and dual-input depth spatial–temporal fusion

Zhang¹,

Meng

2022

Meas. Sci. Technol.

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Accurate fault diagnosis is critical for the safe and stable operation of mechanical equipment. Current deep learning (DL) based fault diagnosis can extract various and deep discriminative features efficiently. Different DL are applicable to different data types, and the extracted features are also different. In addition, equipment working condition is complex and noise from the working environment is inevitable. If only a single network or single input is used, it is difficult to extract the information that can comprehensively describe the fault features, which will affect the diagnosis accuracy especially when multiple faults (more than 10 types) are considered. Hence, this study proposes a method for fault diagnosis based on the smoothness prior approach (SPA) and dual-input depth spatial-temporal fusion network. First, an original signal is decomposed via SPA and two different types of input are constructed. Second, a dual-input depth spatial-temporal fusion network is proposed to extract deeper information by simultaneously learning temporal and spatial features. These two types of features are fused using two-dimensional convolutional neural network to complete classification tasks. The average recognition accuracy of the proposed network can be maintained above 99% on two commonly used benchmark vibration datasets. Compared with several state-of-the-art diagnosis methods under different noise level experiment, the proposed method can achieve higher diagnosis accuracy for each dataset under different working conditions.

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“…Under the premise of classification by using the one-vs-all method, there exist two classes of transition areas between hyperplanes [12,13]. For these areas, some discriminative methods are proposed, such as a "voting" scheme, in which the area is marked as the category with the highest number of points [14].…”

Section: Introductionmentioning

confidence: 99%

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

Han

Wang

et al. 2023

Applied Sciences

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In this paper, a classification recognition algorithm for tower mechanical faults is proposed, and a multiclass central segmentation hyperplane support vector machine (CSH-SVM) is proposed to improve the existing multiclass support vector machine for problems in which a certain sample satisfies multiple hyperplanes at the same time. The tilt angle change and wind direction data were extracted using the tilt sensors and anemometers attached to the tower, and the temperature and humidity sensors, as well as real-time rainfall and water accumulation information, were combined to construct a sample of the original dataset during the operation of the tower. The unbalanced samples were improved using the synthetic minority oversampling technique (SMOTE) algorithm to construct a balanced dataset suitable for machine learning and improve the prediction accuracy of machine learning. At the same time, the support vector machine hyperplane under the one-vs-all classification principle was additionally computed, and the new hyperplane was computed via the existing hyperplane not only to solve the classification problem of the transition area under the one-vs-all classification so that the samples located in this area no longer meet two hyperplane equations at the same time, but also to reduce the probability of incorrect classification to a certain extent. Through verification, CSH-SVM can classify 15 out of 77 misclassified samples into the correct category with slightly higher computational power than the traditional one-vs-all classification SVM, which can improve the classification prediction accuracy for unbalanced tower mechanical failure datasets and make an accurate judgment on the current state of the tower through the tower data as to when the tower may generate mechanical failure, thus reducing economic loss and personal safety threats.

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Research on diagnosis algorithm of mechanical equipment brake friction fault based on MCNN-SVM

Cited by 20 publications

References 40 publications

Health status monitoring of high-speed train brake pads considering noise under variable working conditions

Health status monitoring of high-speed train brake pads considering noise under variable working conditions

Fault pattern recognition of rolling bearing based on smoothness prior approach and dual-input depth spatial–temporal fusion

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

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