Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

Yu, Jianbo

doi:10.1016/j.ymssp.2016.06.004

Cited by 65 publications

(27 citation statements)

References 37 publications

(59 reference statements)

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“…Lei et al [17] employed the Paris-Erdogan model to represent the degradation processes of rolling element bearings and employed maximum likelihood estimation for initialization of the model parameters. Yu [18] proposed a bearing health degradation monitoring method that was used to calculate the similarity between probability density functions described by two different hidden Markov models. The Wiener process has many properties, such as non-monotonic, infinite divisibility and a physical interpretations property, which can be employed to characterize the dynamic characteristic of a system.…”

Section: Related Workmentioning

confidence: 99%

Adaptive event-triggered anomaly detection in compressed vibration data

Zhang

Hutchinson

Lieven

et al. 2019

Mechanical Systems and Signal Processing

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Anomaly detection is a crucial task in Prognostics and Condition Monitoring (PCM) of machinery. In modern remote PCM systems, data compression techniques are regularly used to reduce the need for bandwidth and storage. In these systems the challenge arises of how the compressed (distorted) vibration data affects the condition monitoring algorithms. This paper introduces a novel algorithm that can adaptively establish normal bounds of operation from continuous noisy vibration profiles working with compressed vibration data. The proposed technique is based on four modules, including feature extraction, feature fusion, extreme value vibration modeling and adaptive thresholding for anomaly detection. The proposed method has been validated with experiments using three time-series datasets. The experimental results indicate that the proposed algorithm is able to perform detection of malfunctions in rotating machines effectively without faulty reference data. Moreover, the proposed method is able to produce accurate early warning and alarm indications from both the raw and compressed (distorted) datasets with equal veracity.

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Section: Related Workmentioning

confidence: 99%

Adaptive event-triggered anomaly detection in compressed vibration data

Zhang

Hutchinson

Lieven

et al. 2019

Mechanical Systems and Signal Processing

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“…When the bearings in mechanical equipment fail, the amplitude and probability distribution of the time-domain signal change. Signal frequency components, energy of different frequency components, and the position of the main energy spectrum of the spectrum change, which can effectively characterize the state of bearing health, provide the information about the noise in the bearing vibration signal [6,42]. Some features are useless, so choosing the appropriate time-domain and frequency-domain features is the key to effectively predicting the bearing RUL.…”

Section: Data Preprocessing and Feature Extractionmentioning

confidence: 99%

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

Tian

Wang

2020

Applied Sciences

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The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.

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“…For example, hidden Markov model, Lagrange's interpolation, ARIMA (Autoregressive Integrated Moving Average). Yu (2017) proposed an adaptive hidden Markov model-based online learning framework for faulty bearing detection and performance degradation monitoring. Guiassa and Mayer (2011) proposed a predictive compliance-based model for compensation in multi-pass milling by on-machine probing, where Lagrange's interpolation-based approach is adopted so as to compensate the final cut more effectively.…”

Section: Data-driven Prediction Approachmentioning

confidence: 99%

On-line part deformation prediction based on deep learning

Zhao

Liu

et al. 2019

J Intell Manuf

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Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a Conventional Neural Network and a Recurrent Neural Network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data.

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Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

Cited by 65 publications

References 37 publications

Adaptive event-triggered anomaly detection in compressed vibration data

Adaptive event-triggered anomaly detection in compressed vibration data

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

On-line part deformation prediction based on deep learning

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