4.7 Article

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

Journal

JOURNAL OF INTELLIGENT MANUFACTURING
Volume 28, Issue 8, Pages 1893-1914

Publisher

SPRINGER
DOI: 10.1007/s10845-015-1077-x

Keywords

Degradation tendency; Remaining useful life; Adaptive time window; Increasing rate; Back-propagation neural network

Funding

  1. National Science Foundation of China [51035008]
  2. Fundamental Research Funds for the State Key Laboratory of Mechanical Transmission, Chongqing University [SKLMT-ZZKT-2012 MS 02]

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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.

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