Deep learning for automobile predictive maintenance under Industry 4.0

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

Semi-supervised machine learning techniques for classification of evolving data in pattern recognition

The amount of data recorded and processed over recent years has increased exponentially. To create intelligent systems that can learn from this data, we need to be able to identify patterns hidden in the data itself, learn these pattern and predict future results based on our current observations. If we think about this system in the context of time, the data itself evolves and so does the nature of the classification problem. As more data become available, different classification algorithms are suitable for a particular setting. At the beginning of the learning cycle when we have a limited amount of data, online learning algorithms are more suitable. When truly large amounts of data become available, we need algorithms that can handle large amounts of data that might be only partially labeled as a result of the bottleneck in the learning pipeline from human labeling of the data. An excellent example of evolving data is gesture recognition, and it is present throughout our work. We need a gesture recognition system to work fast and with very few examples at the beginning. Over time, we are able to collect more data and the system can improve. As the system evolves, the user expects it to work better and not to have to become involved when the classifier is unsure about decisions. This latter situation produces additional unlabeled data. Another example of an application is medical classification, where experts’ time is a rare resource and the amount of received and labeled data disproportionately increases over time. Although the process of data evolution is continuous, we identify three main discrete areas of contribution in different scenarios. When the system is very new and not enough data are available, online learning is used to learn after every single example and to capture the knowledge very fast. With increasing amounts of data, offline learning techniques are applicable. Once the amount of data is overwhelming and the teacher cannot provide labels for all the data, we have another setup that combines labeled and unlabeled data. These three setups define our areas of contribution; and our techniques contribute in each of them with applications to pattern recognition scenarios, such as gesture recognition and sketch recognition. An online learning setup significantly restricts the range of techniques that can be used. In our case, the selected baseline technique is the Evolving TS-Fuzzy Model. The semi-supervised aspect we use is a relation between rules created by this model. Specifically, we propose a transductive similarity model that utilizes the relationship between generated rules based on their decisions about a query sample during the inference time. The activation of each of these rules is adjusted according to the transductive similarity, and the new decision is obtained using the adjusted activation. We also propose several new variations to the transductive similarity itself. Once the amount of data increases, we are not limited to the online learning setup, and we can take advantage of the offline learning scenario, which normally performs better than the online one because of the independence of sample ordering and global optimization with respect to all samples. We use generative methods to obtain data outside of the training set. Specifically, we aim to improve the previously mentioned TS Fuzzy Model by incorporating semi-supervised learning in the offline learning setup without unlabeled data. We use the Universum learning approach and have developed a method called UFuzzy. This method relies on artificially generated examples with high uncertainty (Universum set), and it adjusts the cost function of the algorithm to force the decision boundary to be close to the Universum data. We were able to prove the hypothesis behind the design of the UFuzzy classifier that Universum learning can improve the TS Fuzzy Model and have achieved improved performance on more than two dozen datasets and applications. With increasing amounts of data, we use the last scenario, in which the data comprises both labeled data and additional non-labeled data. This setting is one of the most common ones for semi-supervised learning problems. In this part of our work, we aim to improve the widely popular tecjniques of self-training (and its successor help-training) that are both meta-frameworks over regular classifier methods but require probabilistic representation of output, which can be hard to obtain in the case of discriminative classifiers. Therefore, we develop a new algorithm that uses the modified active learning technique Query-by-Committee (QbC) to sample data with high certainty from the unlabeled set and subsequently embed them into the original training set. Our new method allows us to achieve increased performance over both a range of datasets and a range of classifiers. These three works are connected by gradually relaxing the constraints on the learning setting in which we operate. Although our main motivation behind the development was to increase performance in various real-world tasks (gesture recognition, sketch recognition), we formulated our work as general methods in such a way that they can be used outside a specific application setup, the only restriction being that the underlying data evolve over time. Each of these methods can successfully exist on its own. The best setting in which they can be used is a learning problem where the data evolve over time and it is possible to discretize the evolutionary process. Overall, this work represents a significant contribution to the area of both semi-supervised learning and pattern recognition. It presents new state-of-the-art techniques that overperform baseline solutions, and it opens up new possibilities for future research