Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance

Lu, Siliang; Zheng, Ping; Liu, Yongbin; Cao, Zheng; Yang, Hui; Wang, Qunjing

doi:10.1016/j.jsv.2019.02.028

Cited by 76 publications

(26 citation statements)

References 35 publications

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“…In particular, to facilitate the effective operation of a manufacturing process, automated predictive maintenance operations are preferentially performed via fault detection, diagnosis, and predictions based on sensor signals collected during process execution [9,10,[13][14][15]. For example, Lu et al [16] detected occurrences of bearing faults during operation under harsh conditions (i.e., low signal-to-noise ratio) by applying adaptive stochastic resonance. They installed vibration and acoustic sensors on main shaft bearings and collected corresponding signals.…”

Section: Introductionmentioning

confidence: 99%

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Baek

2021

Int J Adv Manuf Technol

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As automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.

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Section: Introductionmentioning

confidence: 99%

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Baek

2021

Int J Adv Manuf Technol

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“…Studies on bearing-fault classification differ in two ways. The first type of studies covers different signal-processing techniques for the classification of bearing faults [7] and [13] or for feature extraction and selection from vibrational data, which are then used to enhance the results of an applied classification method [14]. Other studies mostly utilize the different classification methods to obtain better classification results [6] and [15].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Highlights• Gaussian-mixture-model-based classification was applied to the bearing-fault classification. • To discriminate the faulty from non-faulty bearings only simple statistics from vibrational data was used. • Two different datasets, the Case Western Rice University dataset and Bearing vibration data collected under time-varying rotational speed conditions dataset are used. • The Gaussian-mixture-model-based classification method showed to be a competitive and efficient method.

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“…The other key point in the performance degradation trend prediction is the construction of an effective and accurate forecasting model, and scholars have proposed several relevant theoretical methods and models through long-term exploration and research. Among them, the Bayesian network, 15,16 hidden Markov model (HMM), artificial neural network (ANN) 17 and support vector machine (SVM) are widely used. In 1988, Pearl 18 first proposed the Bayesian network model, which attracted wide attention and could be applied to various types of problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of performance deterioration of rolling bearing based on JADE and PSO-SVM

Zan

Liu

Wang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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In order to improve the prediction accuracy of performance degradation trends of rolling bearings, a method based on the joint approximative diagonalization of eigen-matrices (JADE) and particle swarm optimization support vector machine (PSO-SVM) was proposed. Firstly, the features of the time-domain, frequency-domain, and time-frequency-domain eigenvalues of the vibration signal corresponding to the entire life cycle of the rolling bearing are extracted, and the performance degradation parameters are initially selected by using the monotonicity parameter. Then, a fusion feature that can effectively represent the performance degradation is obtained by using the JADE method. Finally, the prediction model based on PSO-SVM is constructed to predict the performance degradation trend. By comparing with the prediction results obtained by other classical methods, it can be proved that this method can accurately predict the performance degradation trend and the remaining useful life (RUL) of rolling bearings under small sample sizes, and has considerable application potentials.

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Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance

Cited by 76 publications

References 35 publications

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Prediction of performance deterioration of rolling bearing based on JADE and PSO-SVM

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