Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

Wang, Ran; Liu, Fengkai; Xiong, Hu; Jin, Chen

doi:10.1109/access.2020.3024647

Cited by 4 publications

(4 citation statements)

References 32 publications

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“…All nodes used are collected independently, and the network and data transmission protocol stack adopted do not support the synchronous acquisition of vibration signals from multiple sensor nodes [6]. Wang et al [7] adopt a tree-like network topology to obtain vibration signals based on Imote2 wireless sensor network nodes and extract quality parameters and frequency components exceeding the amplitude threshold from sensor nodes. Data fusion is performed on the data fusion node based on quality parameters, and the quality evaluation and fusion results are output.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Data fusion is performed on the data fusion node based on quality parameters, and the quality evaluation and fusion results are output. is method promotes the transmission of uncertain information about measured values between the source sensor node and the gateway node, reduces the potential attenuation of acquired or transmitted diagnostic information, and does not consider the synchronous acquisition of vibration signals [7].…”

Section: Literature Reviewmentioning

confidence: 99%

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Application of Wireless Sensor Network Data Fusion Technology in Mechanical Fault Diagnosis

Zhao¹,

Shen²,

Jing³

2022

Mathematical Problems in Engineering

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In order to solve the problem that a large number of vibration signals cannot be transmitted in real time in the application of wireless sensor networks (WSNs) in mechanical fault diagnosis, a mechanical fault diagnosis method based on multilevel and hierarchical information fusion of WSNs was proposed. In this method, the cluster tree network structure is used to expand the coverage of network monitoring, and WSNs information fusion is divided into three levels: data-level fusion, feature-level fusion, and decision-level fusion. The terminal node performs data-level fusion on the original vibration information to extract feature information; the cluster-head node performs feature-level fusion on the feature information to obtain pattern recognition results; and the gateway node performs decision-level fusion on the recognition results to evaluate the running status of mechanical equipment. The results show that the slight damage fault of the bearing inner ring can be accurately diagnosed by decision-level fusion based on four groups of probability distribution functions. According to the statistics of 30 test results, the fault recognition rate is 83.3%. The method can be applied to mechanical fault diagnosis effectively.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Application of Wireless Sensor Network Data Fusion Technology in Mechanical Fault Diagnosis

Zhao¹,

Shen²,

Jing³

2022

Mathematical Problems in Engineering

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“…Different from traditional machine learning methods that adopt supervised learning, unsupervised-learning-based deep learning can realize fault diagnosis when samples are scarce, providing an effective solution for fault feature diagnosis and analysis. This advantage makes unsupervised feature learning methods gradually enter the field of mechanical fault diagnosis [27,28]. Niu et al [29], for example, proposed a hybrid flexible diagnosis framework for rolling bearings based on a DBN model as a reliable and effective general method for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

Cheng,

Cao,

Liu

et al. 2024

Applied Sciences

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The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets.

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“…Unlike traditional machine learning methods that adopt supervised learning, unsupervised learning-based deep learning can achieve fault diagnosis with scarce samples, providing an effective solution for fault feature diagnosis and analysis. The advantage mentioned above makes the unsupervised feature learning method gradually enter the field of mechanical fault diagnosis [20,21].…”

Section: Introductionmentioning

confidence: 99%

An improved self-organizing mapping neural network and its application in fault diagnosis of CNC machine tool servo drive system

Cheng¹,

Cao²,

Zhang

et al. 2023

Preprint

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The computer numerically controlled (CNC) system is the main functional component of the CNC machine tool control system, whose essential part is the servo-drive system. The failure cause of the servo-drive system can be challenging to diagnose due to the complex working environment. Therefore, effectively detecting servo-drive system faults ensures the regular operation of CNC machine tools. In this paper, an improved self-organizing mapped neural network method is proposed and applied to fault diagnosis of the servo-drive system. The MDC technology can identify hidden fault features in data and diagnose fault types based on various data collected by the MDC system and working parameter range indicators. The method's core is the self-organizing mapped neural network that employs the competitive learning mechanism of unsupervised learning to perform cluster analysis on data with different characteristics, find the winning neurons, and diagnose the fault data. In addition, feature standardization and principal component analysis are introduced to preprocess the input data set, which can balance the influence of different feature scales, enhance the fault data features, and reduce data dimension. The rationality of this technique in practical application is validated via a series of fault data sets tests. Lastly, the advantages of the proposed technique are verified by comparison with other standard methods.

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Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

Cited by 4 publications

References 32 publications

Application of Wireless Sensor Network Data Fusion Technology in Mechanical Fault Diagnosis

Application of Wireless Sensor Network Data Fusion Technology in Mechanical Fault Diagnosis

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

An improved self-organizing mapping neural network and its application in fault diagnosis of CNC machine tool servo drive system

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