A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories condition

Lei Xiao (), Xiaohui Chen (), Xinghui Zhang () and Min Liu ()
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Lei Xiao: Chongqing University
Xiaohui Chen: Chongqing University
Xinghui Zhang: Mechanical Engineering College
Min Liu: Chongqing University

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 8, No 9, 1893-1914

Abstract: Abstract Remaining useful life prediction methods are extensively researched based on failure or suspension histories. However, for some applications, failure or suspension histories are hard to obtain due to high reliability requirement or expensive experiment cost. In addition, some systems’ work condition cannot be simulated. According to current research, remaining useful life prediction without failure or suspension histories is challenging. To solve this problem, an individual-based inference method is developed using recorded condition monitoring data to date. Features extracted from condition data are divided by adaptive time windows. The time window size is adjusted according to increasing rate. Features in two adjacent selected windows are regarded as the inputs and outputs to train an artificial neural network. Multi-step ahead rolling prediction is employed, predicted features are post-processed and regarded as inputs in the next prediction iteration. Rolling prediction is stopped until a prediction value exceeds failure threshold. The proposed method is validated by simulation bearing data and PHM-2012 Competition data. Results demonstrate that the proposed method is a promising intelligent prognostics approach.

Keywords: Degradation tendency; Remaining useful life; Adaptive time window; Increasing rate; Back-propagation neural network (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-015-1077-x

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