Technological advances in the 21st century have brought about ever-increasing sophistication and miniaturization of products, and systems. Consumer electronics manufacturers are releasing new versions of their products with increasing frequency in the hungry world market. These companies not only have to address the ever-increasing expectations of customers, but also have to create a niche in the market so as to stay ahead of the competition. Therefore, there is added pressure on these companies to produce high quality products in a much shorter time. This poses a severe challenge to these companies as they have to achieve cost-effective reliability, availability, maintainability, and safety in the design of their products while at the same time reducing testing and qualification time. The inability to do so could result in loss of market share.
Companies must also take into consideration the usage conditions that that their products experience, as the environmental and operational conditions may vary considerably even for the same product made by the same manufacturer. For high value and critical products and systems, the major concerns are the safety and reliability. For mission critical systems, availability and maintainability are very important. The two types of general maintenance activities are corrective maintenance and preventive maintenance. Both aim to bring the product back to its operational state. A continuous approach to determine the health of the product is needed so that maintenance, repair, and replacement activities are only conducted when necessary. This is known as condition-based maintenance.
High operational availability of systems is difficult to achieve because of the lack of understanding of the interactions of performance parameters, application environments, and their effect on degradation and failure. Traditional approaches to failure mitigation have typically failed because of their reliance on averaged accumulated historical field data (e.g., MIL-HDBK-217, Telcordia SR-332, and CNET/RDF (FIDES). These approaches typically assume a constant failure rate- rather than relying on in-situ data from a particular system. Prognostics and health management (PHM) is an effective way of estimating reliability in-situ.? Prognostics is the process of monitoring the health of a product and predicting its remaining useful life by assessing the extent of deviation or degradation of the product from its expected state of health. A PHM approach will provide advance warning of failures, detect faults, and isolate the causes of the faults. PHM technology can assist in effective product qualification, improved next-generation design, reduced number of no-fault-found problems, increased reliability, and enhanced return on investment.
PHM has emerged as the new paradigm for the reliability, maintenance, and logistics community. Prognostics capability has become a requirement for the reliability of mission-critical systems used in both military and commercial applications (e.g., it is a requirement for many systems sold to the U.S. Department of Defense). Military, aerospace, industrial electronics, commercial electronics, computer and telecom, power and energy, and oil-drilling companies have also started including prognostics requirements for their products. New professional societies in the U.S. (the PHM Society) and China (the China PHM Society) include professionals from industry, government, and academia have been established to further the research and dissemination of information on PHM.
Keeping in view the importance of this field of engineering, the International Journal of Performability Engineering made the decision to bring out a special issue on PHM. Researchers active in the field of prognostics from the U.S., Europe, and Asia- were invited to submit their abstracts and papers for this special edition. After a thorough review process, a set of 10 papers were selected.
The papers in this special section include: ?
- Paper 1: A Fuzzy Similarity-based Method for Failure Detection and Recovery Time Estimation, by E. Zio and F. D. Maio, Italy.
This paper extends a fuzzy similarity-analysis method for estimating the available recovery Time (RT) during the evolution of the failure trajectory of a system. This extension to the method is aimed at freeing it from the need of resorting to a fault detection module for the identification of anomalous system behavior.
- Paper 2: Rolling Bearing Defect Prognosis using Likelihood Parameters and Proportional Hazards Model, by A.K. Verma, B. Sreejith and A. Srividya, India.
This paper presents a method for defect prognosis of roller bearings using a Weibull proportional hazards model (WPHM) based on parameters obtained from vibration analysis and historical event data.
- Paper 3: Application of Grey Prediction Model for Failure Prognostics of Electronics, by J. Gu, N. Vichare, B. Ayyub, and M. Pecht, U.S.A.
In this paper, the application of the grey prediction model was investigated for failure prognostics of electronics. The grey prediction demonstrated a higher level of accuracy when dealing with small sample size data.
- Paper 4: Prognostics of Interconnect Degradation using RF Impedance Monitoring and Sequential Probability Ratio Test, by D. Kwon, M. H. Azarian and M. Pecht, Hong Kong
This paper presents a prognostic technique to detect interconnect failure precursors using RF impedance monitoring and the sequential probability ratio test (SPRT) under thermo-mechanical loading conditions.
- Paper 5: Computer Manufacturing Management Integrating Lean Six Sigma and Prognostic Health Management, by G. Niu, D. Lau, and M. Pecht, Hong Kong.
This paper introduces an integrated management strategy for improving computer manufacturing efficiency, effectiveness, and product reliability by combining the advantages of PHM and Lean Six Sigma approaches to raise management performance and increase enterprise profits.
- Paper 6: Modeling Approaches for Prognostics and Health Management of Electronics, by S. Kumar and M. Pecht, U.S.A.
This paper presents a methodology for selecting the correct model to perform diagnostics and prognostics in electronic systems based on a user’s application environment.
- Paper 7: A Novel Method for Monitoring Single Variable Systems for Fault Detection, Diagnostics, and Prognostics, by J. W. Hines, J. Coble and B. K. Bailey, U.S.A.
This paper introduces an empirical modeling technique for process and equipment monitoring, fault detection and diagnostics, and prognostics. A case study is also presented to demonstrate the new technique.
- Paper 8: Prognostics of Structural Health: Non-Destructive Methods, by Achintya Haldar, and A. K. Das, U.S.A.
This paper presents the concepts behind two nondestructive structural health assessment techniques now under development at the University of Arizona. Several implementation issues are discussed. It is concluded that the methods are capable of identifying small and large defects.
- Paper 9: Bayesian Networks for Predicting Remaining Life, by Y. Rosunally, S. Stoyanov, C. Bailey, P. Mason, S. Campbell, G. Monger, and Ian Bell, U.K.
This paper discusses a prognostics framework that is being developed to monitor the “health” of a ship’s (the Cutty Sark, which is undergoing major conservation) iron structures- to help ensure a 50-year life once conservation is completed with only minor deterioration taking place over time.
- Paper 10: Prognostic Reliability Analysis of Power Electronics Modules, by C. Yin, H. Lu, M. Musallam, C. Bailey, and C. M. Johnson, U.K. and U.S.A.
This paper describes a physics-of-failure (PoF) based prognostic method for power electronics modules (PEMs). Four techniques have been combined to develop this method, which allows the reliability performance of PEMs to be assessed in real time.
We would like to take this opportunity to express our sincere thanks to all of the authors and our gratitude to the reviewers for extending their cooperation in revising and preparing the final versions of these papers.
Special thanks go to Professor Krishna B. Misra, Editor-in-Chief, International Journal of Performability Engineering, for guiding us throughout this project to make this special issue possible.
Daniel Lau is currently Operations Director of Centre for Prognostics and System Health Management in the Department of Electronic Engineering, City University of Hong Kong, which he joined in 2005. Dr. Lau has over 18 years of extensive business and technology transfer experience in the advanced optoelectronics and semiconductor industry in the Asia- Pacific region. He has worked for companies like IBM, and Bio-Rad and has co-founded start-ups. Dr. Lau was Adjunct Professor at the University of Electronic Science and Technology of China (2004-7), Visiting Professor at Changchun University of Science and Technology (2007-8), and is currently a Guest Professor at Huazhong University of Science and Technology in Wuhan. He is an active member of various industrial associations and professional bodies and is well connected with industrial organizations in China and around the world. Dr. Daniel Lau is a Fellow of the Institution of Engineering and Technology (former IEE) in the U.K. and is a Chartered Engineer.
Sony Mathew is a Research Faculty at the Center for Advanced Life Cycle Engineering (CALCE) in the Mechanical Engineering Department of the University of Maryland, College Park, MD, U.S.A. He has bachelor in mechanical engineering (1997) and an MBA (1999) from Pune University, India. He completed his masters in mechanical engineering from the University of Maryland in May 2005 and is currently working towards his Ph.D. He manages the activities of the Prognostics and Health Management (PHM) group within CALCE. He develops, executes, and supervises research projects on prognostics of electronics and serves as a liaison with CALCE’s industry and government partners. His research areas include reliability of electronic products, the tin whisker phenomenon, and prognostics and health management of electronics.