Deep learning for automobile predictive maintenance under Industry 4.0

TLDR: In this article, the authors proposed a framework for automobile predictive maintenance (PdM) based on multi-source data for Industry 4.0 by using deep learning to predict the time-to-failure (TBF) of an automobile.

Abstract: Industry 4.0 refers to the fourth industrial revolution, which has boosted the development of the world. An important target of Industry 4.0 is to maximize the asset uptime so to improve productivity and reduce the production and maintenance cost. The emerging techniques such as artificial intelligence (AI), industrial Internet of things (IIoT) and cyber-physical system (CPS) have accelerated the development of data-orientated application such as predictive maintenance (PdM). Maintenance is a big concern for an automobile fleet management company. An accurate maintenance prediction can be helpful to avoid critical failure and avoid further loss. Deep learning is a type of prevailing machine learning algorithm which has been widely used in big data analytics. However, how to establish a maintenance prediction model based on historical maintenance data using deep learning has not been investigated. Moreover, it is worthwhile to study how to build a prediction model when the labelled data is insufficient. Furthermore, surrounding factors which may impact automobile lifecycle have not been concerned in the state-of-the-art. Hence, this thesis will focus on how to pave the way for automobile PdM under Industry 4.0. This research is structured according to four themes. Firstly, different from the conventional PdM research that only focuses on modelling based on sensor data or historical maintenance data, a framework for automobile PdM based on multi-source data is proposed. The proposed framework aims at automobile TBF modelling, prediction, and decision support based on the multi-source data. There are five layers designed in this framework, which are data collection, cloud data transmission and storage, data mapping, pre-processing and integration, deep learning for automobile TBF modelling, and decision support for PdM. This framework covers the entire knowledge discovery process from data collection to decision support. Secondly, one of the purposes of this thesis is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. An accurate automobile TBF iv Abstract prediction can bring tangible benefits to a fleet management company. Different from the existing studies that adopted sensor data for failure time prediction, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed based on the historical maintenance data for TBF modelling and prediction. CoxPHDL is able to tackle the data sparsity and data censoring issues that are common in the analysis of historical maintenance data. Firstly, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a 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 results have demonstrated the merits of the proposed approach. Thirdly, a large amount of labelled data is one of the critical factors to the satisfactory algorithm performance of deep learning. However, labelled data is expensive to collect in the real world. In order to build a TBF prediction model using deep learning when the labelled data is limited, a new semi-supervised learning algorithm called deep learning embedded semi-supervised learning (DLeSSL) is proposed. Based on DLeSSL, unlabelled data can be estimated using a semi-supervised learning approach that has a deep learning technique embedded so to expand the labelled dataset. Results derived using the proposed method reveal that deep learning (DLeSSL based) outperforms the benchmarking algorithms when the labelled data is limited. In addition, different from existing studies, the effect on algorithm performance due to the size of labelled data and unlabelled data is reported to offer insights for the deployment of DLeSSL. Finally, automobile lifecycle can be impacted by surrounding factors such as weather, traffic, and terrain. The data contains these factors can be collected and processed via geographical information system (GIS). To introduce these GIS data into automobile TBF modelling, an integrated approach is proposed. This is the first time that the surrounding factors are considered in the study of automobile TBF modelling. Meanwhile, in order to build a TBF prediction model based on multi-source data, a new deep learning architecture called merged-LSTM (M-LSTM) network is designed. Abstract v Experimental results derived using the proposed approach and M-LSTM network reveal the impacts of the GIS factors. This thesis aims to research automobile PdM using deep learning, which provides a feasibility study for achieving Industry 4.0. As such, it offers great potential as a route to achieving a more profitable, efficient, and sustainable fleet management.