Prognostics and health management (PHM) can make full use of condition monitoring (CM) data from a functioning system to assess the reliability of the system in its actual life cycle conditions, to determine the advent of failure, and to mitigate system risk through managerial activities. PHM is a systematic approach that is used to evaluate the reliability of a system in its actual life-cycle conditions, to predict failure progression, and to mitigate operating risks via management actions. There are two parts in PHM, namely, “prognostics” and “health management.” Prognostics is often characterized by estimating the remaining useful life (RUL) of a system using available CM information. Once such prognosis is available, appropriate health management actions such as repair, replacement, and logistic support can be performed to achieve the required system’s operational objectives. A requirement of a PHM enabled system is the ability to estimate the RUL, which can provide the decision-maker with enough lead-time to perform the necessary maintenance actions prior to failure. This prognostic ability is a fundamental prerequisite for health management. So far, estimating the RUL, conditional on the CM data, has been considered as one of the most central components in PHM and attached great importance in practice.

With advances in information and sensing technologies, degradation signals of the system can be obtained relatively easily through CM techniques. However, it is quite common in practice that the degradation occurs in a stochastic way for a number of engineering systems such as bearings, gyroscopes, and battery systems. As a result, the RUL is also a random variable, resulting in the difficulty to estimate the RUL with certainty. The past decade has witnessed an increasingly growing research interest on various aspects of stochastic degradation-modelling from the observed signals for prognostics. This is partly caused by its importance in a variety of fields like maintenance, inventory control, public health surveillance and management, and more.

The main focus of this special issue will be on the new theories and methodologies and their applications in degradation modeling and prognostics and health management for complex engineering systems, especially in industry applications. The special issue enables researchers worldwide to report their most recent developments and ideas in the field, with a special emphasis on technical advances and new trends within the last five years. The selection criterion is the quality of the paper and the process will follow a standard procedure of a peer review process.

Potential topics include, but are not limited to:

• Degradation modeling
• Data-driven
• Condition-based maintenance
• Fault diagnostics and prognostics
• Lifetime estimation
• Predictive maintenance
• Prognostics and health management
• Reliability theory and application
• Remaining useful life prediction
• Replacement
• Spare parts ordering with prognostic information