Abstract:

The month of July is special to us as we started International Journal of Performability Engineering in July 2005 and we have come a long way to present position. An update on IJPE has been provided by us on how IJPE has progressed during past ten years on page 510 of this issue. Also for the benefit of readers, we also provide the record for citations of the papers published in IJPE on page 538 of this issue. We believe IJPE has performed quite well.

Prognostics and Health Management (PHM) conferences have become regular annual conference series both in North America and China, since 2009. Europe has been promoting, participating and contributing to these events since the beginning. In 2013, a very successful conference, with more than 200 participants from all over the World, was held at Politecnico di Milano, Milano (Italy). Therefore, when Drs. Piero Baraldi and Francesco Di Maio, together with Professor Enrico Zio, stimulated me with the idea of bringing out a special issue on PHM based on the papers presented at the conference held in Milano, I could not but agree readily to the initiative. The present issue, therefore, is the outcome of the suggestion of colleagues at Politecnico di Milano.

Prognostics and Health Management (PHM), is an area that links failure mechanisms studies to system lifecycle management. Actually, prognostics is a discipline which relies on the phenomenon of failure modes, helps detect early signs of wear and aging, that eventually lead to failures. The signs of degradation are correlated with a damage propagation model.

The methodologies used in prognostics usually fall into categories of data-driven approaches, model-based approaches, and hybrid approaches. Data-driven techniques utilize monitored operational data related to system health. Data-driven approaches can be deployed quickly and are not so expensive.

A model-based prognostic incorporates physical models of the system into the estimation of the remaining useful life (RUL).

Hybrid approaches attempt to utilize the strength of both- data-driven approaches and model-based approaches. In reality, it is rare that the used approach is purely data-driven or purely model-based. Quite often the model-based approaches include some aspects of data-driven approaches and data-driven approaches use available information from models.

As is known, prognostics is a dynamic process where predictions are updated as often as more operational data become available during the operational life of the system being studied. The quality of prediction also changes with time and must be tracked and quantified. Prognostic performance evaluation is very important for a successful implementation of PHM system. Therefore, it is implied that research in this progressive area needs to be vigorously followed up to develop better and effective techniques with time and applied to such areas which defy the use of standard techniques.

The papers included in this issue have been carefully chosen to bring out the salient features of the methodologies of PHM and the areas of their realistic applications.

I would be failing in my duty in not recording the appreciation of Dr. Piero Baraldi, Dr. Francesco Di Maio and Professor Zio, who have worked hard to guarantee quality, respect deadlines and the standard of publication included herein. Last but not least, the team of reviewers, who helped maintain the standard of papers high, are worthy of our appreciation. I would also like to thank the authors whose contributions are included who helped maintain the time line. It is hoped that this issue of IJPE will generate further interest in PHM and provide impetus to research in this important area, and we hope to continue bringing out such special issues in the future as well.