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.

为了降低维护成本并实现可持续的运营管理，预测性维护（PdM）在行业中已变得十分普遍。PdM的核心是预测下一次故障，以便可以在发生故障之前安排相应的维护。这项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。对于PdM，数据稀疏性被认为是一个关键问题，它可能会危害基于维护数据的建模算法的性能。同时，数据审查对处理维护数据提出了另一个挑战，因为被审查的数据仅被部分标记。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。在这个研究中，提出了一种称为Cox比例风险深度学习（CoxPHDL）的新方法，以解决上述在运营维护数据分析中常见的数据稀疏性和数据审查问题。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可靠的表示形式。其次，研究了Cox比例风险模型（Cox PHM）来估计审查数据的TBF。然后，建立了长期短期记忆（LSTM）网络，以基于预处理的维护数据来训练TBF预测模型。