Data-driven prognostics and health management (PHM) for predictive maintenance of industrial components and systems

As the digital, physical, and human worlds continue to integrate, the 4th industrial revolution, the internet of things, and big data, the industrial internet, are changing the way we design, manufacture, and deliver products and services. In this fast-paced, changing environment, the attributes related to the reliability of components and systems continue to play a fundamental role in the industry. On the other hand, the advancements in knowledge, methods, and techniques and the increase in information sharing and data availability offer new ways for the reliable design and safe operation of engineering systems and new opportunities for business in several application areas. Based on this increased knowledge, information, and the data available, we can improve our prediction capabilities. Particularly, the increased availability of data coming from monitoring the relevant parameters of components, systems, and assets performance, and the grown ability to treat these data by intelligent machine learning algorithms, capable of mining out information relevant to the assessment and prediction of their state, have open wide the doors for disruptive advancements in many industrial sectors, for improved design, operation, management, and maintenance. In this chapter, we look into this from the perspectives of data-driven Prognostics and Health Management (PHM) for the predictive maintenance of industrial components and systems in various industrial sectors.