Predictive maintenance using cox proportional hazard deep learning
Predictive maintenance using cox proportional hazard deep learning
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DOI：
10.1016/j.aei.2020.101054
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发表时间：
2020-04-01
影响因子：
8.8
通讯作者：
Syntetos, Aris A.
中科院分区：
文献类型：
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作者：
Chen, Chong;Liu, Ying;Syntetos, Aris A.
为了降低维护成本，实现可持续的运营管理，预测性维护在行业中得到了广泛的应用。产品数据管理的核心是对下一次故障进行预测，以便在故障发生之前安排相应的维护。本研究的目的是通过数据驱动的方法建立故障间隔时间(TBF)预测模型。在产品数据管理中，数据稀疏性被认为是影响基于维修数据建模的算法性能的关键问题。与此同时，数据审查给处理维护数据带来了另一个挑战，因为被审查的数据只有部分标签。此外，在解决数据审查问题时，数据稀疏性可能会影响现有方法的算法性能。针对运维数据分析中普遍存在的数据稀疏和数据审查问题，提出了一种新的COX比例风险深度学习方法。这个想法是通过利用深度学习和可靠性分析来提供一个集成的解决方案。首先，采用自动编码器将标称数据转换为稳健的表示。其次，研究了COX比例风险模型(COX PHM)来估计删失数据的TBF。然后建立一个长-短期记忆(LSTM)网络来训练基于预处理的维修数据的TBF预测模型。使用英国一家机队公司提供的大规模真实机队维护数据集进行的实验研究证明了该方法的优点，其中基于所提出的LSTM网络的算法性能分别在MCC和RMSE方面得到了改善。
Predictive maintenance (PdM) has become prevalent in the industry in order to reduce maintenance cost and to achieve sustainable operational management. The core of PdM is to predict the next failure so corresponding maintenance can be scheduled before it happens. The purpose of this study is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. For PdM, data sparsity is regarded as a critical issue which can jeopardize algorithm performance for the modelling based on maintenance data. Meanwhile, data censoring has imposed another challenge for handling maintenance data because the censored data is only partially labelled. Furthermore, data sparsity may affect algorithm performance of existing approaches when addressing the data censoring issue. In this study, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed to tackle the aforementioned issues of data sparsity and data censoring that are common in the analysis of operational maintenance data. The idea is to offer an integrated solution by taking advantage of deep learning and reliability analysis. To start with, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox proportional hazard model (Cox PHM) is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental studies using a sizable real-world fleet maintenance data set provided by a UK fleet company have demonstrated the merits of the proposed approach where the algorithm performance based on the proposed LSTM network has been improved respectively in terms of MCC and RMSE.