Machine learning based online fault prognostics for nonstationary industrial process via degradation feature extraction and temporal smoothness analysis

Abstract

Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general, slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis (FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor (KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.

摘要

故障退化预测是预估过程劣化和故障发生的时间, 已被认为是维护策略中的一个关键组成部分。故障退化过程通常是缓慢变化的, 可以用自回归模型来建模。然而, 工业过程往往表现出典型的非平稳特性, 这就给故障退化信息的获取和非平稳过程的建模带来了挑战。针对上述问题, 本文提出了一种新的故障退化建模和在线故障预测策略。首先, 提出一种面向故障退化的慢特征分析(FDSFA)算法提取故障退化方向, 并沿该方向提取候选故障退化特征。然后, 利用趋势评估算法来选择主要的故障退化特征。其次, 结合主要的故障退化特征及其稳定性加权因子, 计算关键故障退化因子来表征故障退化趋势。针对过程非平稳特性, 建立了带时序平滑正则项的时变回归模型。在更新策略的基础上, 通过对预测误差的分析和建模, 进一步建立了在线故障预测模型。最后, 通过一个实际的工业案例验证了所提方法的预测性能。