89.1 Introduction 

As the primary goal of remanufacturing is part reuse, understanding of the quality/condition of the returned cores is very important for decision-making in remanufacturing processes. Hence, condition assessment and fault isolation of the returned cores becomes one of the most critical activities that determine the success of a remanufacturing process. Existing practices in remanufacturing typically carry out defect inspection and fault diagnosis only for isolated parts/components after the returned cores are disassembled. This may impose additional challenges and cost on the remanufacturing process such as fluctuation of schedule for remedial processes or treatments in the shop floor due to unexpected defects/faults identified after disassembly. In addition, it depletes the opportunity to assess the condition and performance of the products systematically based on their field operational data in each lifecycle before they are returned as cores, which is very important to establish reliability models of the products (Mazhar et al. 2010).
On the other hand, for valuable machineries, such as mining trucks, a large amount of operational data is already being collected, typically on log sheets or by a control system. This process is usually not regarded as part of a condition monitoring or diagnostic program. However, there is a lot of valuable machinery performance and condition information buried in such operational data. In practice, the only challenge is how to extract useful information from such data. It is believed that the operational and inspection data collected on a machine, when properly interpreted, can produce an accurate picture of the machines health. In reality, the cores returned for remanufacturing may have experienced very different working conditions, and their components/parts may have diverse ages and different stress and strains arisen by the users, but remanufacturing companies usually do not involve the historical and field operational data of the cores when they make decisions for remanufacturing processes in the shop floor. Taking the engine of a mining truck as an example, operational data often is adequate to allow for calculations of engine efficiency and detection of its deviations, which can then be extended to evaluate the condition of the engines. Analysis such as the emerging operational trends of operational data not only can tell us that there is a performance problem with this engine, but it also can help to isolate fault sources well before the engine is sent for inspection in the remanufacturing workshop. Measures of this sort can greatly assist with prioritizing remanufacturing decisions and balance the time between reman cycles, and more importantly, it may simplify the reman workshop inspection procedure and minimize the inspection cost.
It should be noted that most of the current practices in remanufacturing rely on rules of thumb or expert knowledge and lack rigorous reliability-based evaluation models to support remanufacturing shop-floor decision-making. Although recent years have seen some applications using visual and/or statistical analysis tools to assist the lifecycle assessment of remanufactured products, such tools remain inadequate in coping with large amount of field operational data with inherent variability, uncertainty, and nonlinearity. The proposed approach attempts to address critical issues for improving remanufacturing processes through effective analysis of field operational data. This research will fill up the gap in the current state of the art where existing remanufacturing practices lack rigorous and reliable analysis of operational data for support of remanufacturing shop-floor decision-making, despite the fact that an effective analysis of field operational data in various aspects of the products will provide invaluable information to facilitate sound decision-making and continuous improvement of remanufacturing processes. More specifically, the novelty of this research includes.
A comprehensive approach to condition assessment and fault isolation through rigorous reliability-based evaluation models with progressive model learning capabilities; Fusion of statistical and machine learning techniques to form a fast real-time RUL prognosis tool that can scale well against large operational data with inherent variety and uncertainty in remanufacturing processes.