PREDICTIVE MAINTENANCE AND RELIABILITY-CENTERED APPROACHES IN MECHANICAL ENGINEERING SYSTEMS

Abstract

Predictive maintenance and reliability-centred maintenance (RCM) are now two important strategies in modern mechanical engineering systems and provide a useful solution to minimize the number of unexpected failures, decrease the cost of maintenance, and increase operational reliability. Data used in predictive maintenance: Integrating data on real-time, condition monitoring, machine learning algorithms and predicting equipment failures prior to occurrence enables timely data-driven solutions. Conversely, RCM offers a formal methodology to determine what maintenance strategy is most suitable on the basis of the role and the importance of each piece. Combined, these methods are a move away from the traditional reactive and pre-emptive maintenance into intelligent and data-driven systems. Industry 4.0 has enhanced its use by incorporating technologies like the Internet of Things (IoT), artificial intelligence (AI) and deep learning to enable an accurate identification of faults, better decision making and maintenance routines being performed automatically. Such methodologies have proved to be effective in many different industries, such as manufacturing, transportation, energy, and aerospace, where the performance of the systems and uptime are crucial. The predictive and reliability-centred maintenance styles have also become very crucial in the growth of the mechanical engineering systems as industries focus on greater efficiency, safety, and sustainability.