Towards practical data-driven predictive maintenance: a robust and generalizable deep learning approach - Mohamed Ragab Mohamad Adam (Nanyang Technological University, 2022)
Predictive maintenance (PdM) is a prevailing maintenance strategy that aims to minimize downtime, reduce maintenance costs, and increase machine reliability. With the huge data availability in the era of Industry 4.0, data-driven predictive maintenance leverages Artificial Intelligence (AI) and historical data to forecast potential machine failure, enabling accurate in-advance maintenance schedules based on the machine condition. Recently, deep learning has shown widely acclaimed potential for data-driven predictive maintenance tasks. Nevertheless, deep learning only performs well under two main assumptions: (1) the availability of vast amounts of labeled data; (2) the training phase and deployment phase share the same data distribution or working condition. These constraints can significantly hinder the deep learning applicability to real-world environments. On the one hand, data labeling and annotation can be very laborious tasks, especially for sensor data that encompass complex underlying patterns. Furthermore, running to failure for vast and sophisticated machines can be costly and cause catastrophic consequences. This imposes an additional challenge on obtaining labeled data that identify the health status of the machine. On the other hand, under the premise of the real-world manufacturing environment, variation in working environments is inevitable. This can yield different sensor readings across the working environments even for the same industrial asset. As a result, the data generated from each working condition may have different statistics (i.e., distributions). Under those circumstances, a model that performs well under one working condition performs poorly when tested on data from different working conditions. Therefore, there is a need for more practical deep learning approaches that can handle the aforementioned limitations.