4.7 Article

Decision-level machinery fault prognosis using N-BEATS-based degradation feature prediction and reconstruction

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ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2023.110435

关键词

Box-Cox transformation; N-BEATS; Transfer learning; Signal reconstruction; Fault prognosis; Rotating machinery

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This paper proposes a new prognosis framework for machinery fault diagnosis based on original condition monitoring signals. The framework uses Box-Cox transformation to extract a series of degradation features and utilizes N-BEATS algorithm for time-series prediction. A parameter-based transfer learning method is also introduced to reduce computation complexity. The future original signals of machinery are reconstructed through inverse Box-Cox transformation, and a new failure criterion suitable for decision-level fault prognosis is defined.
Condition monitoring signals provide sufficient information about the health of machines and, therefore, are widely used for fault diagnosis, prognosis, and health management. Existing ap-proaches generally extract one or more degradation features from original signals collected in a time interval and predict the remaining useful life of machinery based on a selected or fused feature under a pre-specified threshold. However, using a single feature is often inadequate in terms of the accuracy of fault prognosis due to the interval-based extraction procedure. To overcome the shortcoming, a new prognosis framework is proposed for machinery based on original condition monitoring signals in this paper. Technically, the Box-Cox transformation is first performed on the original signals point by point to construct a series of degradation features without losing information. Then, the neural basis expansion analysis for time series (N-BEATS) that has robust performance in time-series prediction is utilized to predict the future evolution for each feature with high volatility. By leveraging the similarity of multiple Box-Cox features, a parameter-based transfer learning method is proposed to reduce the computation complexity. Finally, we reconstruct the future original signals of machinery through the inverse Box-Cox transformation. Since the reconstructed original signals are noisy, a new failure criterion suit-able for decision-level fault prognosis is defined from an industrial application perspective. An application on high speed train wheels and two follow-up simulations are used to illustrate the performance of our proposed framework in machinery fault prognosis.

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