Wavelet-based features for prognosis of degradation in rolling element bearing with non-linear autoregressive neural network

Nistane, V. M.

doi:10.1080/14484846.2019.1630949

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…The single-point defect such as ball defect, inner race defect, outer race defect, and generalized roughness of bearing was considered for the evaluation of bearing degradation by Nistane and Harsha. 5,6 The failure evolution and fault diagnosis of bearing was expressed by Nistane 7 and Kankar et al 8 The literature suggested that the complexity of components would be incorporated into the model by the data-driven approach. The bearing health can be assessed by the vibration sensor signal data.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative performance of prognostics for remaining useful life of bearing

Nistane

2020

Noise & Vibration Worldwide

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The frequent failure components in rotary machinery are the rolling element bearings as it is important for prognostics of rolling element bearings. In this study, the data-driven technique was used to develop the prognostic models based on particle swarm optimization techniques. These models apply to estimate the rolling element bearing degradation and predict remaining useful life. Initially, the fault features were extracted by processing the vibration signals through wavelet packet decomposition based on the intensifying impulsive characteristics and superiority of features. Health indicators evaluate from fault features. The prognostic models are developed with the following approaches: Gated recurrent neural network, classification and regression tree, and Autoregressive-Moving Average models. Performances of models are verified on the basis of error in prediction. To verify the suggested methodology, an experiment on normal and faulty bearings was conducted using a bearing test rig. Experimental results clarify that the prognostic algorithms predict bearing remaining useful life with significant robustness. Outperformance of the proposed method and conformation of degree of accuracy from results indicate that remaining useful life is conjectured as inference compared with the published literature.

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Section: Literature Reviewmentioning

confidence: 99%

Comparative performance of prognostics for remaining useful life of bearing

Nistane

2020

Noise & Vibration Worldwide

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“…This datadriven method combines PSW for fast-time scale behavior and a physics-based crack growth model for slow-time scale characterization. Nistane et al [33] proposes a health assessment model for rotary machine rolling element bearings based on a non-linear autoregressive neural network and the exponential value of health indicator (HI). Using vibration signals and continuous wavelet transform for feature extraction, the model achieves accurate degradation prediction through optimal NAR and NARX networks, demonstrating effectiveness in various scenarios.…”

Section: Introductionmentioning

confidence: 99%

Temporal Convolutional Network with Attention Mechanism for Bearing Remaining Useful Life Prediction

Wang

Zhang

et al. 2023

Mechanisms and Machine Science

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Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is crucial in industrial production, yet existing models often struggle with limited generalization capabilities due to their inability to fully process all vibration signal patterns. We introduce a novel multi-input autoregressive model to address this challenge in RUL prediction for bearings. Our approach uniquely integrates vibration signals with previously predicted Health Indicator (HI) values, employing feature fusion to output current window HI values. Through autoregressive iterations, the model attains a global receptive field, effectively overcoming the limitations in generalization. Furthermore, we innovatively incorporate a segmentation method and multiple training iterations to mitigate error accumulation in autoregressive models. Empirical evaluation on the PMH2012 dataset demonstrates that our model, compared to other backbone networks using similar autoregressive approaches, achieves significantly lower Root Mean Square Error (RMSE) and Score. Notably, it outperforms traditional autoregressive models that use label values as inputs and non-autoregressive networks, showing superior generalization abilities with a marked lead in RMSE and Score metrics.

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