Reliability is an important consideration issue during the development of a variety of systems or products, e.g., automobiles, airplanes, semiconductors, and power plants, which ensures that their performances are maintained over a specified period of time under specific use environments. As technology evolves, system complexity increases and reliability evaluation for the systems remains an important area of research and has attracted the attention of system engineers. Once, the system is launched and used in the field, failure data or maintenance data are collected so improvements can be performed to maximize system’s availability or minimize operation cost. Recently, system monitoring and diagnostic methods using smart sensors and internet of thing (IoT) garner more attentions from variety of industrial areas.

Nowadays, innovative tools for reliability analysis and decision making in design, operation and maintenance of engineering systems are developed for safe, reliable and effective operation of these systems. This special issue on “Reliability Analysis and Prognostics for Complex Systems” presents a platform where researchers from academy and industry can present methodologies of coping with the uncertainties in reliability modeling & prognostics for complex systems through the use of concepts and various techniques; life tests and lifetime prediction from repairable or non-repairable systems, maintenance scheduling and modeling, Residual useful life (RUL) prediction using parametric or nonparametric methods, etc.

Keywords

• System reliability
• Maintainability & Availability for repairable or non-repairable systems
• Diagnostics & Prognostics
• Condition-based maintenance
• Maintenance modelling, planning, scheduling and optimization
• Remaining useful life estimation
• Machine learning & deep learning in maintenance modelling