Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings

Bendjama, Hocine

doi:10.1007/s00170-023-12710-5

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…SOM is an unsupervised neural network, which effectively retains the original topology of input samples in an intuitive visual form, and is an important tool for fault diagnosis and monitoring [43]. SOM employs competitive learning, in which each neuron or node competes with other nodes or neurons to get closer to the input data point.…”

Section: Basic Theory and Techniques For Sommentioning

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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Section: Basic Theory and Techniques For Sommentioning

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

View full text Add to dashboard Cite

show abstract

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

Cheng,

Cao,

Zhang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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Computer numerical control (CNC)-based systems are key functional components of industrial manufacturing installations, and the servo drive system is the main functional component of CNC systems. The complex working environment of industrial facilities will lead to the frequent failure of servo drive systems, and effective fault diagnosis measures are important to ensure the normal operation of CNC machine tools. In this paper, the application of fault diagnosis methods in servo drive systems is considered, and a method suitable for high-dimensional data of CNC systems is presented. Using data collected by a physical system and related indicators, the technique can be used to identify hidden fault characteristics in the data and to diagnose the fault types. The core of this method is the self-organizing map neural network, which uses unsupervised competitive learning to cluster data with different characteristics, find the winning neurons, and diagnose the fault. The introduction of feature standardization in the map’s initialization stage can accelerate the model’s training convergence speed and reduce the feature weight deviation. At the same time, principal component analysis is introduced to balance the influence of different feature scales, enhance the features of fault data, reduce the data dimensionality, and improve the interpretability of the model. A comparison with the conventional algorithm and testing various fault datasets shows that the proposed method exhibits improved performance when processing high-dimensional data and its enhancement fault recognition effect is verified.

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Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation

Kumar,

Chauhan,

Vashishtha

et al. 2024

Applied Sciences

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The health of mechanical components can be assessed by analyzing the vibration and acoustic signals they produce. These signals contain valuable information about the component’s condition, often encoded within specific frequency bands. However, extracting this information is challenging due to noise contamination from various sources. Narrow-band amplitude demodulation presents a robust technique for isolating fault-related information within the signal. This work proposes a novel approach based on cluster-based segmentation for demodulating the signal and extracting the frequency band of interest. The segmentation process leverages the criteria of maximum L-kurtosis and minimum entropy. L-kurtosis maximizes impulsiveness in the signal, while minimum entropy signifies a low degree of randomness and high cyclo-stationarity, and both characteristics are crucial for identifying the desired frequency band. Simulations and experimental tests using vibration signals from different gears demonstrate the effectiveness of this technique. The processed envelope of the signal exhibits distinct improvements, highlighting the ability to accurately extract the fault-related information embedded within the complex noise-ridden signals. This approach offers a promising solution for accurate and efficient fault diagnosis in mechanical systems, contributing to enhanced reliability and reduced downtime.

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Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings

Cited by 5 publications

References 39 publications

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

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

Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation

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