Remaining Useful Life as a Cognitive Tool in the Domain of Manufacturing

Abstract

The idea of information processing as a measure of health in a system engineering domain follows the cutting-edge area of research, widely known as prognostics and health management (PHM). The role of decision-making in an industry is one such technological advancement that uses remaining useful life (RUL) as a state of the health indicator for cognitive action. Correlations built using generic experimental data for a system helps in asset management. However, each system has a unique operating history of working in a complex environment. To account for this, degradation modelling-based prognosis that estimates the RUL of the target system by providing an estimate of usefulness in the life of the system contributes towards a much safer industry standard. This chapter aims to contribute in the development of stochastic decision-making using evolutionary techniques. The use of particle filter (PF) as an approach towards state estimation and RUL as a condition indicator (CI) for a degraded system is formulated. Analogy towards the cognitive decision-making has been explained to meet the desired expectation of the current theme in one of the sections. The presented prognostics method is thus validated using degraded motor data from industry. Accuracy in the results of the proposed technique is found to be information specific of the target system before the motor is jeopardized. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020, Corrected Publication 2020.