It is an interdisciplinary field that combines engineering, data analysis, and predictive modelling to assess and manage the health condition, performance, and reliability of systems and assets. It involves continuously monitoring and analysing data from sensors, diagnostics, and other sources to detect early signs of anomalies, degradation, or potential failures. By using advanced algorithms and statistical techniques, PHM aims to predict the remaining useful life (RUL), diagnose faults, and provide recommendations for maintenance or operational adjustments. PHM enables proactive decision-making, reduces downtime, optimizes maintenance strategies, and enhances the overall performance and availability of complex systems across various industries.
Published in Chapter:
Prediction of Remaining Useful Life of Batteries Using Machine Learning Models
Copyright: © 2024
|Pages: 20
DOI: 10.4018/979-8-3693-0497-6.ch017
Abstract
Predictive maintenance is a maintenance strategy based on monitoring the state of components to predict the date of future failure. The objective is to take the appropriate measures to avoid the consequences of this failure. For this reason, the authors determine the remaining useful life (RUL) which is the remaining time before the appearance of the failure on the component. It is an important approach that allows the prediction of aging mechanisms likely to lead components to failure. In this chapter, a new methodology for predicting the remaining useful life of components is proposed using a data-driven prognosis approach with the integration of machine learning. This approach is illustrated in a battery case study to predict the remaining useful life.