Diagnostics and Prognostics are the process of assessing the state of health of a system and predicting its remaining useful life (RUL) by analyzing various condition indicators. They are used to improve system availability, reduce downtime, extend maintenance cycles, and execute time repair actions.

A system’s health is a function of its components, their interactions with one another, and the environmental conditions under which it operates. It is the ability to evaluate this health, in an objective manner, that makes it possible for systems to be designed and operated more effectively.

For most mechanical systems, the health of individual components can be determined by examining their current state through the use of traditional condition monitoring techniques. However, it is not always possible to detect all deteriorations that may occur in the life of a component or a whole system.

Consequently, there is a need to develop new methods for diagnosing and predicting the condition of these systems. The goal is to achieve a better understanding of the health and behavior of these systems, which would enable engineers to design better products and processes.

There are many tools and techniques available to accomplish these tasks. These include fault diagnostics, failure detection, and failure prediction techniques. Some of them have been adapted to address the characteristics of complex engineered systems, and have become highly reliable. In addition, some methods have been developed to allow for the dynamic behavior of a system under study.

Most of these diagnostics and prognostics algorithms are based on statistical techniques and physics-based models that attempt to incorporate the physical understanding of a system into its estimation of RUL. These model-based approaches are typically more accurate than data-driven algorithms that only utilize information from the current state of a system.

However, there are many issues associated with using physics-based methods to estimate RUL of complex engineered systems. For example, a physics-based method may have an inaccurate representation of the system’s dynamics due to the presence of unintended nonlinearities and other phenomena. Additionally, it may not accurately account for the influence of aging effects on a system’s performance.

Therefore, it is crucial to combine physics-based methods with data-driven techniques and machine learning for accurate RUL estimation. This is especially true for complex multi-agent systems that have a large number of sensors, multiple components, and a wide range of operating conditions.

Adaptive diagnostics and prognostics of complex systems is an emerging field of research that addresses the limitations of conventional methods. It consists of three main components: online system identification and parameter estimation; adaptive modeling/filtering/observation; and diagnostic and prognostics algorithms. These algorithms are based on computational intelligence principles and can be applied to an extensive range of dynamic, high-order systems.

Adaptive diagnostics and prognostics techniques are an important approach to addressing the challenges of complex systems. These techniques have been applied in a variety of engineering fields, and the results have been published in many journals. The most common areas of application for these techniques are electrical and electronic, mechanical, and multidisciplinary engineering fields.