The discipline that links studies of failure mechanisms to system lifecycle management is referred to as Prognostics and Health Management or simply PHM. The PHM is a proactive equipment maintenance capability enabled by using Health Management indicators to predict a functional failure ahead of the event so that appropriate action can be taken. In fact, PHM involves the collection and processing of precursor-to-failure data from fielded platforms for the purpose of mitigating failures before mission loss. The most active research groups are under the dynamic leadership of Professor Michael Pecht, who is the Director both of the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland, USA, and the City University of Hong Kong Centre for Prognostics and System Health Management.

The CityU PHM Centre under Professor Pecht has been conducting research and development in prognostics and health management applications for electronic products and systems as well as for systems-of-systems for several years. Consumer electronics companies and organizations like Dell, NASA, Boeing, General Motors, etc., are investing heavily in this area to reduce warranty costs and product qualification time.

In collaboration with the CALCE PHM programme of University of Maryland under the leadership of Professor Pecht, City University of Hong Kong has set up the CityU PHM Centre with headquarters in Hong Kong and a laboratory in Shenzhen. This new centre has been established because of growing interest in PHM implementation on the part of a large number of companies in China in the area of avionics, aerospace, computer, telecommunications, LED lighting, automotives, and power. The CityU PHM Centre will be cooperating with Georgia Tech, University of Maryland, Beihang University’s Institute of Reliability Engineering, China Electronics Produce Reliability and Environmental Testing Research Institute: Reliability Research and Analysis Centre (CEPREI), and China AVIC Aero-polytechnology Establishment (CAPE) to co-develop state-of-the-art methods and technologies to promote high quality prognostics research and development.

I am pleased to know that the International Journal of Performability Engineering (IJPE) has taken the initiative, in association with CityU PHM centre and CALCE PHM, to bring out a special issue of IJPE on PHM. This will help spread the message of PHM effectively in the Asia–Pacific region and generate much-needed research in this important discipline.

I congratulate the Guest Editors, Dr. Daniel Lau and Mr. Sony Mathew, for taking up this timely endeavour.

Technological advances in the 21^st 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.

In this work, an extension of a data-driven approach for estimation of the available Recovery Time (RT) is presented. The improvement is in avoiding the need of resorting to a fault detection module for the identification of the anomalous system behavior: the algorithm proposed jointly detects the onset of the accidental transient and performs the estimation of the available RT. This is achieved by fuzzy similarity analysis of the currently developing scenario and reference multidimensional trajectory patterns of failure scenarios; the RT remaining before the developing trajectory pattern hits a failure threshold is predicted by combining the times of failure of the reference patterns, weighed by their similarity with the developing pattern.
For illustration purposes, failure scenarios of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS) are considered.
Received on September 03, 2009, revised on March 20, 2010
References: 32

Bearings are critical components employed virtually in all rotating machines and automobiles to alleviate friction between surfaces during relative motion. In traditional approaches, rolling element bearing failures are predicted based on either historical time-to-failure data (event data) or condition monitoring (CM) data. Prediction methods using event data are of little value to maintenance decision making since they render general forecasts for the total population of identical units instead of forecast for a particular unit presently operating in the machine. Prognosis based on CM data provides short term predictions which may not be useful in maintenance scheduling. Proportional hazards model (PHM) can be used to predict hazard rates and reliability of machines and its components using both event data and CM data.
This paper presents a method for defect prognosis of roller bearings using Weibull proportional hazards model (WPHM) based on parameters obtained from vibration analysis and historical event data. Morlet wavelet filter (MWF) is used for denoising of vibration signals. Time domain parameters extracted from the denoised vibration signals are used as covariates in the WPHM. Use of log-likelihood parameters as covariates in WPHM is explored and their performance is compared with that of other parameters. The proposed approach helps in early estimation of hazard and reliability with more accuracy, eventually increasing the effectiveness of condition based maintenance and reducing maintenance costs.
Received on August 31, 2009, revised on March 17, 2010
References: 20

Reliability prediction is becoming more and more important for electronics components and devices, such as avionics. In this paper, a grey prediction model based prognostics approach was developed to perform the failure prediction of electronics. The grey prediction model first makes the original data set into a new data set with less randomness in order to find the tendency. Then, history data is needed for training the algorithm and predicting the future condition. Last, the predicted result in the new data set is transferred back to the original data set. Compared with traditional data-driven method, this approach was especially useful for reliability prediction with small sample size. The whole prognostics approach was also verified by two case studies. One was performed on electronic boards with ball grid array (BGA) and quad flat package (QFP) components under thermal cycle loading. The other was performed on electronic boards with capacitors under temperature, humidity and bias tests.
Received on August 31, 2009, revised on March 21, 2010
References: 15

For electronic products, interconnect failures may occur due to mechanisms such as fatigue, creep, corrosion, and mechanical over-stress. Regardless of the failure mechanism, interconnect degradation often starts at a surface and propagates inward. DC resistance, which has been used by the electronics industry to monitor the reliability of board level interconnects, does not offer an adequate means to predict an impending failure. However, RF impedance does respond to the early stages of interconnect degradation due to the skin effect, and thus can provide a failure precursor for an interconnect.

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. Creep tests were conducted with a test vehicle including an impedance-controlled circuit board, a surface-mount low-pass filter, and two solder joints providing both mechanical and electrical connections between them. Constant mechanical loading was directly applied to the filter at an elevated temperature in order to generate creep failures of the solder joints. During solder joint degradation, the RF impedance provided detectable failure precursors by the sequential probability ratio test, while the DC resistance remained constant with no precursors prior to the generation of an open circuit. Failure analysis of a partially degraded solder joint revealed that the change in RF impedance resulted from a partial crack that initiated at the surface of the solder joint and propagated only part of the way across the solder joint. These test results indicate that the combination of RF impedance and SPRT can provide a non-destructive and real-time means to detect solder joint degradation.
Received on September 30, 2009, revised on March 23, 2010
References: 07

Computer manufacturers have been applying Six Sigma for continuous quality improvement and Lean Manufacturing for reducing process waste in order to maximally meet customer requirements. However, top computer manufacturers are now realizing the design and production with advanced capability for early failure detection, fault diagnostic and prediction will significantly improve product life cycle performance and increase competitive advantages. In this paper, prognostic health management is proposed as a predictive management strategy centered by technological approach. Through integration with Lean Six Sigma, a raised computer manufacturing management performance can be achieved.
Received on September 05, 009, revised on March 13, 2010
References: 29

Prognostics and Health Management is an enabling technology with the potential to solve complex reliability problems that are due to complexity in design, manufacturing, and maintenance. There are several different mathematical techniques that can assist in performing prognostics and health management of electronic systems. These techniques can be categorized into statistical reliability, life cycle loads, state estimation, and feature-extraction based models. The selection of the appropriate model depends on the application environment. 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. The model selection method is based on five properties, including usability, accuracy, performance, applicability at the system level, and flexibility of the model. Based on all this information, a comparison is made between the five prognostic models to show the advantages and disadvantages of each. Finally, recommendations are given for selecting the most appropriate model for system fault diagnostics and prognostics of electronics. While this methodology used in this study for analysis of electronic systems, it can be extended to other applications as well.
Received on October 5, 2009, revised March 16, 2010
References: 22

This paper introduces empirical modeling techniques for process and equipment monitoring, fault detection and diagnostics, and prognostics. The paper first provides a brief background and an overview of the theoretical foundations and presents a new method for applying these methods to systems which only have one useful measured variable. Instead of using a traditional auto-associative model to estimate the fault free parameter values, nominal operating features are inferred from the operating conditions of the system. This newly proposed system is called Stressor-based Univariate Monitoring Method (SUMM). A case study is presented for the application of this method to an aircraft generator that includes normal feature prediction over different operating conditions, actual feature measurement and residual generation, and fault detection and identification. Application of the proposed SUMM system to the simulated aircraft generator data includes fault detection and identification. The results presented here highlight application of the method to data including dynamically changing loads. A methodology for developing a corresponding prognostic model is given.
Received on August 29, 2009, revised on March 26, 2010
References: 21

For the prognosis of structural health, non-destructive defect assessment procedures are under active development by the profession. Two such procedures now under development by the research team at the University of Arizona, are MILS-UI and GILS-EKF-UI. They indicate a considerable application potential. The unique feature of the algorithms is that they can identify members' properties and in the process access the health of a structural system using only dynamic responses completely ignoring the excitation information. Although mathematically elegant, their practical implemental potential to identify defect-free and defective (single or multiple defects) states need critical evaluation and is discussed in the paper. With the help of an illustrative example, it was shown that both the MILS-UI and GILS-EKF-UI methods can identify defect-free and defective states of a structure very well. Both methods successfully indentified the presence of multiple defects. Ignoring responses at vertical dynamic degrees of freedoms did not alter the outcomes of the nondestructive evaluation for the problem under consideration. Both methods also correctly identified less severe defect in terms of loss of area over a finite length. It can be concluded that the methods are capable of identifying small and large defects.
Received on August 30, 2009, revised March 30, 2010
References: 08

The Cutty Sark is undergoing major conservation to slow down the deterioration of the original Victorian fabric of the ship. While the conservation work being carried out is "state of the art", there is no evidence at present of the effectiveness of the conservation work 50 plus years ahead. A Prognostics Framework is being developed to monitor the "health" of the ship's iron structures to help ensure a 50 year life once conservation is completed with only minor deterioration taking place over time. The framework encompasses four approaches: Canary and Parrot devices, Physics-of-Failure (PoF) models, Precursor Monitoring and Data Trend Analysis and Bayesian Networks. Bayesian network models are used to update remaining life predictions from PoF models with information from precursor monitoring. This paper presents the prognostics framework with focus on the Bayesian network approach used to improve remaining life predictions of Cutty Sark iron structures.
Received on September 30, 2009, revised March 27, 2010
References: 08

This paper describes a physics-of-failure (PoF) based prognostic method for power electronics modules (PEMs). Differing from the traditional reliability prediction methods, this approach allows the reliability performance of PEMs to be assessed in real time. Four techniques have been used to develop this method, they are: (1) Compact electro-thermal model (2) Rainflow counting algorithm (3) Compact thermo-mechanical model and (4) Lifetime consumption model. As a demonstration, this method has been applied to a typical IGBT half bridge module and solder joint fatigue was assumed as the major failure mechanism. In this application, a random electric current load profile was generated in laboratory environment and used to derive the thermal loading condition for the module. Due to the randomness of the load profile, rainflow counting method was used to reduce the continuous load profile into discrete sets of thermal cycles. The damage induced in each temperature cycle was calculated via a compact thermo-mechanical model, and used in the lifetime model to calculate the PEMs lifetimes under simple cyclic loading conditions. Based on these predicted lifetimes and the linear damage accumulation rule, the total consumed life of the PEMs over the whole period of usage was predicted.
Received on October 02, 2009, revised on March 27, 2010
References: 14

In this article, the probability of undetectable burst errors for network systems applying the tandem Cyclic Redundancy Check (CRC) coding scheme is analyzed under a performability framework.
Received on August 17, 2009, revised on January 1, 2010
References: 05