Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards
Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards
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DOI：
10.1016/j.neucom.2021.12.035
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发表时间：
2022-04-28
期刊：
影响因子：
6
通讯作者：
Zimmerman, Andrew T.
中科院分区：
文献类型：
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作者：
Nemani, Venkat P.;Lu, Hao;Zimmerman, Andrew T.
轴承剩余使用寿命(RUL)的概率预测至关重要，特别是在计划外维护需求、计划外设备停机或灾难性故障可能会给公司造成数百万美元损失并威胁工人安全的工业环境中。目前轴承预测领域的研究清楚地表明了基于深度学习的解决方案的优势，但在运行条件变化的恶劣工业环境中，纯粹由数据驱动的预测的可靠性是值得怀疑的。为了使这项工作具有工业意义，我们采用国际标准化组织的指导方针来确定在速度域中定义的轴承故障阈值(特别是ISO 10816)，同时考虑由每个轴承的几何形状定义的特征轴承故障频率。我们提出了一个两阶段长短期记忆(LSTM)模型集成，它包括：(1)预测步骤和(2)校正步骤，以抵消RUL预测。集合中的每个LSTM模型被定制为包括高斯层，该高斯层捕捉预测参数中的任意不确定性，并且所有单独的LSTM模型的集合提供RUL预测中的认知不确定性。我们在西安交通大学和长兴苏姆扬科技有限公司(XJTU-SY)公开提供的轴承数据集上演示了所提出的模型的实施，并与轴承预测领域中的其他常用技术进行了比较，确定了该模型在精度和不确定性量化方面的优势。与贝叶斯模型相比，集成模型倾向于探索多种功能/预测模式，提供更好的不确定性估计。(C)2021年爱思唯尔B.V.保留所有权利。
Probabilistic prediction of the remaining useful life (RUL) of bearings is critically important, especially in an industrial setting where unplanned maintenance needs, unscheduled equipment downtime, or catastrophic failures can cost a company millions of dollars and threaten worker safety. Current research in the field of bearing prognostics clearly shows the advantage of a deep learning-based solution, but the reliability of purely data-driven predictions is questionable in harsh industrial environments with varying operational conditions. To make this work industrially relevant, we adopt ISO guidelines to determine bearing failure thresholds (specifically ISO 10816), which are defined in the velocity domain, while considering characteristic bearing fault frequencies defined by the geometry of each bearing. We propose a two-stage Long Short-Term Memory (LSTM) model ensemble which includes: (1) a predictor step to forecast and (2) a corrector step to offset the RUL prediction. Each LSTM model within the ensemble is customized to include a Gaussian layer that captures the aleatoric uncertainty in the forecasted parameter, and the ensemble of all the individual LSTM models provides the epistemic uncertainty in the RUL prediction. We demonstrate the implementation of the proposed model on the publicly available Xi'an Jiaotong University and Changxing Sumyoung Technology Co., Ltd. (XJTU-SY) bearing dataset and establish the superiority of the model, both in terms of accuracy as well as uncertainty quantification, when compared against other commonly used techniques in the field of bearing prognostics. The ensemble model tends to explore multiple functional/forecast modes providing better uncertainty estimates when compared to Bayesian counterparts. (C) 2021 Elsevier B.V. All rights reserved.