A Comprehensive Machine Learning Methodology for Embedded Systems PHM

Techniques of Machine Learning (ML) have recently been widely used in several applications, but not much for embedded systems, particularly those of safety-critical functionality, and are expected to help solve complex PHM and RUL which are difficult to be overcome with traditional approaches. This paper proposes a novel approach to encompass data-driven system-level health management, which comprises three main processes: an exhaustive offline training using machine learning techniques to derive a model capable of predicting the system of interest within fixed performance criteria; followed by its deployment on the targeted embedded system for real-time health assessment and finally the ability to provide a multi-step ahead forecasting for the system prognostics.The ability to monitor the current state of the systems and predict their behaviour becomes essential. Therefore, techniques to establish fault tolerance and fault prediction are required. Condition monitoring (CM), prognostics and health management (PHM) and remaining useful life (RUL) are enablers for fault detection, fault progression and system degradation.This paper presents a ML-based methodology for the PHM of embedded systems, aiming at generalization and real-time performance.