Prognostics
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Prognostics

Prognostics[1] is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function.[2] This lack of performance is most often a failure beyond which the system can no longer be used to meet desired performance. The predicted time then becomes the remaining useful life (RUL), which is an important concept in decision making for contingency mitigation. Prognostics predicts the future performance of a component by assessing the extent of deviation or degradation of a system from its expected normal operating conditions.[3] The science of prognostics is based on the analysis of failure modes, detection of early signs of wear and aging, and fault conditions. An effective prognostics solution is implemented when there is sound knowledge of the failure mechanisms that are likely to cause the degradations leading to eventual failures in the system. It is therefore necessary to have initial information on the possible failures (including the site, mode, cause and mechanism) in a product. Such knowledge is important to identify the system parameters that are to be monitored. Potential uses for prognostics is in condition-based maintenance. The discipline that links studies of failure mechanisms to system lifecycle management is often referred to as prognostics and health management (PHM), sometimes also system health management (SHM) or--in transportation applications--vehicle health management (VHM) or engine health management (EHM). Technical approaches to building models in prognostics can be categorized broadly into data-driven approaches, model-based approaches, and hybrid approaches.

## Data-driven prognostics

Data-driven prognostics usually use pattern recognition and machine learning techniques to detect changes in system states.[4] The classical data-driven methods for nonlinear system prediction include the use of stochastic models such as the autoregressive (AR) model, the threshold AR model, the bilinear model, the projection pursuit, the multivariate adaptive regression splines, and the Volterra series expansion. Since the last decade, more interests in data-driven system state forecasting have been focused on the use of flexible models such as various types of neural networks (NNs) and neural fuzzy (NF) systems. Data-driven approaches are appropriate when the understanding of first principles of system operation is not comprehensive or when the system is sufficiently complex such that developing an accurate model is prohibitively expensive. Therefore, the principal advantages to data driven approaches is that they can often be deployed quicker and cheaper compared to other approaches, and that they can provide system-wide coverage (cf. physics-based models, which can be quite narrow in scope). The main disadvantage is that data driven approaches may have wider confidence intervals than other approaches and that they require a substantial amount of data for training. Data-driven approaches can be further subcategorized into fleet-based statistics and sensor-based conditioning. In addition, data-driven techniques also subsume cycle-counting techniques that may include domain knowledge.

The two basic data-driven strategies involve (1) modeling cumulative damage (or, equivalently, health) and then extrapolating out to a damage (or health) threshold, or (2) learning directly from data the remaining useful life.[5][6]

As mentioned, a principal bottleneck is the difficulty in obtaining run-to-failure data, in particular for new systems, since running systems to failure can be a lengthy and rather costly process. When future usage is not the same as in the past (as with most non-stationary systems), collecting data that includes all possible future usages (both load and environmental conditions) becomes often nearly impossible. Even where data exist, the efficacy of data-driven approaches is not only dependent on the quantity but also on the quality of system operational data. These data sources may include temperature, pressure, oil debris, currents, voltages, power, vibration and acoustic signal, spectrometric data as well as calibration and calorimetric data. Features must be extracted from (more often than not) noisy, high-dimensional data.[7]

Selecting proper prognostics algorithm for each application is a challenging factor in applying data driven prognostics methods. In a survey article, authors have prepared useful information to conclude pros and cons of various prognostics algorithms in fault diagnosis and failure prediction of rotating machineries.[8]

## Model-based prognostics

Model-based prognostics attempts to incorporate physical understanding (physical models) of the system into the estimation of remaining useful life (RUL). Modeling physics can be accomplished at different levels, for example, micro and macro levels. At the micro level (also called material level), physical models are embodied by series of dynamic equations that define relationships, at a given time or load cycle, between damage (or degradation) of a system/component and environmental and operational conditions under which the system/component are operated. The micro-level models are often referred as damage propagation model. For example, Yu and Harris's fatigue life model for ball bearings, which relates the fatigue life of a bearing to the induced stress,[9] Paris and Erdogan's crack growth model,[10] and stochastic defect-propagation model[] are other examples of micro-level models. Since measurements of critical damage properties (such as stress or strain of a mechanical component) are rarely available, sensed system parameters have to be used to infer the stress/strain values. Micro-level models need to account in the uncertainty management the assumptions and simplifications, which may pose significant limitations of that approach.

Macro-level models are the mathematical model at system level, which defines the relationship among system input variables, system state variables, and system measures variables/outputs where the model is often a somewhat simplified representation of the system, for example a lumped parameter model. The trade-off is increased coverage with possibly reducing accuracy of a particular degradation mode. Where this trade-off is permissible, faster prototyping may be the result. However, where systems are complex (e.g., a gas turbine engine), even a macro-level model may be a rather time-consuming and labor-intensive process. As a result, macro-level models may not be available in detail for all subsystems. The resulting simplifications need to be accounted for by the uncertainty management.

## Hybrid approaches

Hybrid approaches attempt to leverage the strength from both data-driven approaches as well as model-based approaches.[11][12] In reality, it is rare that the fielded approaches are completely either purely data-driven or purely model-based. More often than not, model-based approaches include some aspects of data-driven approaches and data-driven approaches glean available information from models. An example for the former would be where model parameters are tuned using field data. An example for the latter is when the set-point, bias, or normalization factor for a data-driven approach is given by models. Hybrid approaches can be categorized broadly into two categories, 1) Pre-estimate fusion and 2.) Post-estimate fusion.

### Pre-estimate fusion of models and data

The motivation for pre-estimate aggregation may be that no ground truth data are available. This may occur in situations where diagnostics does a good job in detecting faults that are resolved (through maintenance) before system failure occurs. Therefore, there are hardly any run-to-failure data. However, there is incentive to know better when a system would fail to better leverage the remaining useful life while at the same time avoiding unscheduled maintenance (unscheduled maintenance is typically more costly than scheduled maintenance and results in system downtime). Garga et al. describe conceptually a pre-estimate aggregation hybrid approach where domain knowledge is used to change the structure of a neural network, thus resulting in a more parsimonious representation of the network.[] Another way to accomplish the pre-estimate aggregation is by a combined off-line process and on-line process: In the off-line mode, one can use a physics-based simulation model to understand the relationships of sensor response to fault state; In the on-line mode, one can use data to identify current damage state, then track the data to characterize damage propagation, and finally apply an individualized data-driven propagation model for remaining life prediction. For example, Khorasgani et al [13] modeled the physics of failure in electrolytic capacitors. Then, they used a particle filter approach to derive the dynamic form of the degradation model and estimate the current state of capacitor health. This model is then used to get more accurate estimation of the Remaining Useful Life (RUL) of the capacitors as they are subjected to the thermal stress conditions.

### Post-estimate fusion of model-based approaches with data-driven approaches

Motivation for post-estimate fusion is often consideration of uncertainty management. That is, the post-estimate fusion helps to narrow the uncertainty intervals of data-driven or model-based approaches. At the same time, the accuracy improves. The underlying notion is that multiple information sources can help to improve performance of an estimator. This principle has been successfully applied within the context of classifier fusion where the output of multiple classifiers is used to arrive at a better result than any classifier alone. Within the context of prognostics, fusion can be accomplished by employing quality assessments that are assigned to the individual estimators based on a variety of inputs, for example heuristics, a priori known performance, prediction horizon, or robustness of the prediction.

## Prognostic performance evaluation

Prognostic performance evaluation is of key importance for a successful PHM system deployment. The early lack of standardized methods for performance evaluation and benchmark data-sets resulted in reliance on conventional performance metrics borrowed from statistics. Those metrics were primarily accuracy and precision based where performance is evaluated against actual End of Life (EoL) typically known a priori in an offline setting. More recently, efforts towards maturing prognostics technology has put a significant focus on standardizing prognostic methods, including those of performance assessment. A key aspect, missing from the conventional metrics, is the capability to track performance with time. This is important because prognostics is a dynamic process where predictions get updated with an appropriate frequency as more observation data become available from an operational system. Similarly, the performance of prediction changes with time that must be tracked and quantified. Another aspect that makes this process different in a PHM context is the time value of a RUL prediction. As a system approaches failure, the time window to take a corrective action gets shorter and consequently the accuracy of predictions becomes more critical for decision making. Finally, randomness and noise in the process, measurements, and prediction models are unavoidable and hence prognostics inevitably involves uncertainty in its estimates. A robust prognostics performance evaluation must incorporate the effects of this uncertainty.

Several prognostics performance metrics have evolved with consideration of these issues:

• Prognostic horizon (PH) quantifies how much in advance an algorithm can predict with a desired accuracy before a failure occurs. A longer PH is preferred as more time is then available for a corrective action.
• ?-? accuracy further tightens the desired accuracy levels using a shrinking cone of desired accuracy as EoL approaches. In order to comply with desired ?-? specifications at all times an algorithm must improve with time to stay within the cone.
• Relative accuracy (RA) quantifies the accuracy relative to the actual time remaining before failure.
• Convergence quantifies how fast the performance converges for an algorithm as EoL approaches.

A visual representation of these metrics can be used to depict prognostic performance over a long time horizon.

## Industrial applications and case studies

### Manufacturing applications

The industrial applications of PHM are quite diverse in terms of industry, with examples found in manufacturing, automotive, heavy industry, aerospace, power generation, and transportation. With respect to manufacturing, there has been considerable work for rotating machinery, including PHM development and solutions for the machine tool industry. Examples include methods and software for monitoring spindle bearing health based on vibration and motor current,[14] a cloud based monitoring architecture for relating tool wear health to part quality,[15] and numerous works on monitoring the health condition of a machine tool feed axis.[16][17] A low cost and practical method for monitoring the machine tool feed axis was demonstrated in a production environment, in which only controller signals were used to detect the early symptoms of pulley degradation prior two weeks before the pulley axis failed.[18] For automotive manufacturing, there has recent developments on monitoring the health condition of industrial robots, using available controller signals such as motor current; this approach represents a practical approach for monitoring a fleet of industrial robots.[19] Methods based on frequency analysis and classification algorithms for detecting the early symptoms of surge for air compressors have also been successfully demonstrated and implemented for an automotive manufacturing plant.[20] Data mining and advanced analytical approaches have also been developed for continuous manufacturing production lines and semiconductor manufacturing applications.[21][22] In terms of success in the manufacturing sector for PHM solutions, there is some economic numbers that can be reported. For example, the National Science Foundation funded in independent economic impacts study on Industry/University Cooperative Research Centers (I/UCRC) and surveyed 5 industrial members of the Center for Intelligent Maintenance Systems; the 5 companies (predominantly manufacturing applications) reported a savings of over \$855 Million U.S dollars based on the successful implementation of the predictive monitoring and PHM solutions.[23] Please see intelligent maintenance system for more reference.

### Heavy vehicle and mining application

Heavy vehicles used in construction, agriculture, and mining, are also seeing greater interest in predictive monitoring and PHM technology. Original equipment manufacturers of these heavy duty vehicles, such as Komatsu and Caterpillar already have the infrastructure in place for remote monitoring, and are now developing the advanced data analysis algorithms to detect the vehicle problems at an early stage.[24] Original equipment manufacturers for underground mining are also developing the necessary infrastructure and analysis algorithms with the idea of providing similar PHM service solutions.[25]

### Power generation application

Commercial implementations of PHM solutions in the power generation industry are also increasing, with applications that focus on rotating machinery and turbines, to early problem detection algorithms based on data from the supervisory control data acquisition (SCADA) system. In addition, to some of the larger assets, vibration monitoring and intelligent analytics are also being considered for the balance of plant (fans, pumps) equipment used in power generation.[26]

Renewable energy applications, such as wind turbines, are also an industry sector that has received considerable attention regarding PHM technology and commercial solutions. Approaches based on using statistical methods for modeling the normal relationship between input parameters such as wind speed and output parameters such as generator power have been used to successfully monitor the wind turbines performance degradation.[27] Wind turbine drivetrain condition monitoring solutions based on vibration data have also seen considerable research work[28] and some commercial monitoring products are also available.[29] The wind turbine drive train monitoring represents a more challenging PHM application, due to the rotational speed fluctuations and low rotational speeds for the input shaft, time-varying load conditions, and the more complicated vibration algorithms needed for monitoring planetary gearbox health.

### Aerospace and defense applications

The aerospace and defense sector has several research studies in the PHM area and some fielded systems that have some level of PHM functionality. The health and usage management systems (HUMS) is an example of a fielded PHM solution for rotorcraft, that can detect several different types of problems using vibration and other signals, from shaft unbalance, to gear and bearing deterioration. In addition, it is reported that the HUMS system provided significant maintenance cost reductions and improved fleet availability when compared with rotorcraft units that did not have the HUMS systems.[30] Aircraft engines are another application in which there is considerable PHM technology that is being used and developed. Original equipment manufacturers, such as General Electric Aviation have monitored aircraft engines for over 15 years and are providing diagnostic services to detect the early symptoms of engine problems before they lead to operational downtime.[31] In terms of research and development efforts, the Joint Strike Fighter program allocated significant resources for PHM development and implementation.[32] PHM research and case studies for military ground vehicles are also being conducted for engines,[33] alternator,[34] and structural components;[35] however it seems that there are more fielded systems for the aerospace platforms at this time.

### Automotive and electric vehicle application

There are numerous research studies in the automotive sector that aim at providing a more advanced monitoring functionality for monitoring key vehicle components, such as vehicle batteries, vehicle alternators, and internal combustion engines. Methods based on looking at unusual patterns for a particular vehicle when compared to the fleet have also showed promise in a research setting.[36][37] Electric vehicle and battery health monitoring and prognostic work has also seen an increasing level of research and development. Algorithms for estimating the electric vehicles battery state of charge and state of health have been successfully demonstrated in several research studies.[38] A more challenging problem is predicting the remaining driving range for an electric vehicle, since this depends on not just the battery state of charge, but also environmental factors, the road and traffic conditions, driving behavior, among other factors.[39]

### Railway applications

Monitoring the condition of the rolling stock and railway infrastructure has also been an area that has received considerable attention. Monitoring the condition of the track infrastructure using vibration, displacement, and other measurements has been conducted in several research studies, with some initial systems being implemented. For the track infrastructure, vibration data analysis based on the magnitude, wavelength and time-frequency characteristics along with statistical or pattern recognition tools can be used to assess the track condition with respect to corrugation, rolling contact fatigue defects, and geometry and alignment issues.[40] Point Machines (devices used to operate railway turnouts), are also a target for PHM technology, in which the electrical signals and statistical or pattern recognition analysis methods can be used to catch the early symptoms of point machine degradation prior to failure.[41] Commercial solutions for rolling stock condition monitoring are also being provided by original equipment manufacturers. An example commercial offering is the TrainTracer product, which provides real-time data collection and remote monitoring of rolling stock systems and components.[42]

## Commercial hardware and software platforms

For most PHM industrial applications, commercial off the shelf data acquisition hardware and sensors are normally the most practical and common. Example commercial vendors for data acquisition hardware include National Instruments[43] and Advantech Webaccess;[44] however, for certain applications, the hardware can be customized or ruggedized as needed. Common sensor types for PHM applications include accelerometers, temperature, pressure, measurements of rotational speed using encoders or tachometers, electrical measurements of voltage and current, acoustic emission, load cells for force measurements, and displacement or position measurements. There are numerous sensor vendors for those measurement types, with some having a specific product line that is more suited for condition monitoring and PHM applications.

The data analysis algorithms and pattern recognition technology are now being offered in some commercial software platforms or as part of a packaged software solution. National Instruments currently has a trial version (with a commercial release in the upcoming year) of the Watchdog Agent® prognostic toolkit, which is a collection of data-driven PHM algorithms that were developed by the Center for Intelligent Maintenance Systems.[45] This collection of over 20 tools allows one to configure and customize the algorithms for signature extraction, anomaly detection, health assessment, failure diagnosis, and failure prediction for a given application as needed. Customized predictive monitoring commercial solutions using the Watchdog Agent toolkit are now being offered by a recent start-up company called Predictronics Corporation[46] in which the founders were instrumental in the development and application of this PHM technology at the Center for Intelligent Maintenance Systems. Other commercial software offerings focus on a few tools for anomaly detection and fault diagnosis, and are typically offered as a package solution instead of a toolkit offering. Example includes Smart Signals anomaly detection analytical method, based on auto-associative type models (similarity based modeling) that look for changes in the nominal correlation relationship in the signals, calculates residuals between expected and actual performance, and then performs hypothesis testing on the residual signals (sequential probability ratio test).[47] Similar types of analysis methods are also offered by Expert Microsystems, which uses a similar auto-associative kernel method for calculating residuals, and has other modules for diagnosis and prediction.[48]

## System-level Prognostics

[49]While most prognostics approaches focus on accurate computation of the degradation rate and the remaining useful life (RUL) of individual components, it is the rate at which the performance of subsystems and systems degrade that is of greater interest to the operators and maintenance personnel of these systems.

## Notes

1. ^ Prognostics: The Science of Making Predictions, K. Goebel, M. Daigle, S. Sankararaman, A. Saxena, I. Roychoudhury, J. Celaya, 2017, CreateSpace Independent Publishing Platform.
2. ^ Vachtsevanos; Lewis, Roemer; Hess, and Wu (2006). Intelligent fault Diagnosis and Prognosis for Engineering Systems. Wiley. ISBN 0-471-72999-X.
3. ^ Pecht, Michael G. (2008). Prognostics and Health Management of Electronics. Wiley. ISBN 978-0-470-27802-4.
4. ^ Liu, Jie; Wang, Golnaraghi (2009). "A multi-step predictor with a variable input pattern for system state forecasting". Mechanical Systems and Signal Processing. 23 (5): 1586-1599. doi:10.1016/j.ymssp.2008.09.006.
5. ^ Mosallam, A.; Medjaher, K; Zerhouni, N. (2014). "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction". Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0933-4.
6. ^ Mosallam, A.; Medjaher, K.; Zerhouni, N. (2015). "Component based data-driven prognostics for complex systems: Methodology and applications". International Conference on Reliability Systems Engineering: 1-7. doi:10.1109/ICRSE.2015.7366504.
7. ^ Mosallam, A.; Medjaher, K; Zerhouni, N. (2013). "Nonparametric time series modelling for industrial prognostics and health management". The International Journal of Advanced Manufacturing Technology. 69 (5): 1685-1699. doi:10.1007/s00170-013-5065-z.
8. ^ Lee, Jay (2013). "Prognostics and health management design for rotary machinery systems--Reviews, methodology and applications". Mechanical Systems and Signal Processing. 42 (1-2): 314-334. doi:10.1016/j.ymssp.2013.06.004.
9. ^ Yu, Wei Kufi; Harris (2001). "A new stress-based fatigue life model for ball bearings". Tribology Transactions. 44 (1): 11-18. doi:10.1080/10402000108982420.
10. ^ Paris, P.C.; F. Erdogan (1963). "A Critical Analysis of Crack Propagation Laws". ASME Journal of Basic Engineering. 85: 528-534. doi:10.1115/1.3656903.
11. ^ Pecht, Michael; Jaai (2010). "A prognostics and health management roadmap for information and electronics-rich systems". Microelectronics Reliability. 50 (3): 317-323. doi:10.1016/j.microrel.2010.01.006.
12. ^ Liu, Jie; Wang, Ma; Yang, Yang (2012). "A data-model-fusion prognostic framework for dynamic system state forecasting". Engineering Applications of Artificial Intelligence. 25 (4): 814-823. doi:10.1016/j.engappai.2012.02.015.
13. ^ researchgate.net
14. ^ Liao; Lee (2009). "Design of a Reconfigurable Prognostics platform for Machine Tools". Expert Systems with Applications. 37: 240-252. doi:10.1016/j.eswa.2009.05.004.
15. ^ Kao, Ann; Lee,J.; Edzel,E.; Yang,S.; Huang,Y.; Yen,N. (2011). "iFactory Cloud Service Platform based on IMS Tools and Servo-lution". World Congress on Engineering Asset Management.
16. ^ Skirtich, T; Siegel,D.; Lee,Jay. (2011). "A Systematic Health Monitoring and Fault Identification Methodology for Machine Tool Feed Axis". MFPT Applied Systems Health Management Conference.
17. ^ Jin, W; Chen, Y.; Lee,J. (2013). "Methodology for Ball Screw Health Assessment and Failure Analysis". International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference.
18. ^ Skirtich (2012). "A Comparative Study of Prognostic and Health Assessment Methods in Sensor Rich and Sensorless Environments". Master Thesis, University of Cincinnati.
19. ^ Siegel, D; Edzel,L.; AbuAli,M.; Lee,J. (2009). "A Systematic Health Monitoring Methodology for Sparsely Sampled Machine Data". Machine Failure and Prevention Conference MFPP.
20. ^ Sodemann; Y.Li.,Lee,J. (2006). "Data-Driven Surge Map Modeling for Centrifugal Air Compressors". Proceedings: International Mechanical Engineering Congress and Exposition.
21. ^ Lee, J.,; Siegel,D.; Lapira,E. (2013). "Development of a Predictive and Preventive Maintenance Demonstration System for a Semiconductor Etching Tool". ECS Transactions. 52 (1): 913-927. doi:10.1149/05201.0913ecst.
22. ^ Bleakie,A; Djurdjanovic,D (2012). "Feature extraction, condition monitoring, and fault modeling in semiconductor manufacturing systems". Computers in Industry.
23. ^ Gray,Do., Rivers,D., Vermont,G. "Measuring the Economic Impacts of NSF I/UCRC Program: A Feasibility Study" (PDF).
24. ^ Wang, H.; Lee,J.; Ueda,T.; Adjallah,H.H.; Ghaffari, M. (2007). "Engine Health Assessment and Prediction Using the Group Method of Data Handling and the Method of Match Matrix - Autoregressive Moving Average". Proceeding of 2007 ASME Turbo Expo.
25. ^ Joy Mining Company. "Smart Services" (PDF).
26. ^ Johnson, P (2012). "Fleet Wide Asset Monitoring: Sensory Data to Signal Processing to Prognostics". Proceedings of the Annual Conference of the Prognostics and Health Management Society.
27. ^ Edzel, L.; Brissert, D.; Davari, H.; Siegel,D.; Lee, J. (2011). "Wind Turbine Performance Assessment using Multi-Regime Modeling Approach". the International Journal of Renewable Energy. 45: 88-95. doi:10.1016/j.renene.2012.02.018.
28. ^ Siegel, D.; Zhao,W.; Lapira, E.; AbuAli, M.; Lee,J. (2013). "A Comparative Study on Vibration - Based Condition Monitoring Algorithms for Wind Turbine Drive Trains". Wind Energy Journal (Special Issue).
29. ^ Bechhoefer, E.; Mortom, B. (2012). "Condition monitoring architecture: To reduce total cost of ownership". In IEEE Prognostics and Health Management (PHM) Conference.
30. ^ UTC Aerospace. "Health and Usage Management System" (PDF).
31. ^ GE Aviation. "Aircraft Diagnostics".
32. ^ Hess,A.; Fila,L. (2002). "The Joint Strike Fighter (JSF) PHM Program Concept: Potential Impact on Aging Aircraft Problems". EEE Aerospace Conference Proceedings.
33. ^ Bayba,A.J.; Siegel, D.; Tom,K.; Ly,C. "Approaches to Health Monitoring of the CAT 7 Diesel Engine". ARL Technical Report.
34. ^ Banks, J; Brought, M.; Estep,J.; Hines,J.; Hobbs,N.; Rabeno,E.; Hillehass,M. (2011). "Health and Usage Monitoring for Military Ground Vehicle Power Generating Devices". IEEE Aerospace Conference.
35. ^ DiPetta,T.; Yoder, N.; Adams, D. E.; Gothamy, J.; Lamb, D.; Gorsich, D.; Decker, P (2008). "Health Monitoring for Condition-Based Maintenance of a HMMWV using an Instrumented Diagnostic Cleat". No. TARDEC-19335RC.
36. ^ Zhang, Y; Du,X.; Salman,M. (2012). "Peer to Peer Collaborative Vehicle Health Management - the Concept and Initial Study". Annual Conference of Prognostics and Health Management Society.
37. ^ Mosallam, A.; Byttner, S.; Svensson, M.; Rognvaldsson, T. (2011). "Nonlinear relation mining for maintenance prediction". IEEE Aerospace Conference: 1-9. doi:10.1109/AERO.2011.5747581.
38. ^ Zhang,J.; Lee,J. (2011). "A review on prognostics and health monitoring of Li-ion battery". Journal of Power Sources. 196: 6007-6014. doi:10.1016/j.jpowsour.2011.03.101.
39. ^ Rezvanizanian,S.M.; Huang,Y.; Chuan,J.; Lee,J. (2012). "A Mobility Performance Assessment on Plug-in EV Battery,". International Journal of Prognostics and Health Management.
40. ^ Hory,C.; Bouillaut, L.; Aknin, P. (2012). "Time-frequency Characterization of Rail Corrugation under a Combined Auto-regressive and Matched Filter Scheme". Mechanical Systems and Signal Processing. 29: 174-186. doi:10.1016/j.ymssp.2011.12.015.
41. ^ Ardakani, H. D; Lucas, C.; Siegel, D.; Chang, S.; Dersin, P.; Bonnet, B.; Lee, J. (2012). "PHM for Railway System--A Case Study on the Health Assessment of the Point Machines". IEEE Prognostics and Health Management (PHM) Conference.
42. ^ ALSTOM. "Trainlife Service" (PDF).
43. ^ National Instruments. "Condition Monitoring".
45. ^ National Instruments. "Watchdog Agent® Toolkit".
46. ^ Predictronics. "Predictronics".
47. ^ Wegerich,S. (2005). "Similarity-based Modeling of Vibration Features for Fault Detection and Identification". Sensor Review. 25 (2): 114-122. doi:10.1108/02602280510585691.
48. ^ Clarkson, S.A.; Bickford,R.L. (2013). "Path Classification and Remaining Life Estimation for Systems Having Complex Modes of Failure". MFPT Conference.
49. ^ Rodrigues, L. R.; Gomes, J. P. P.; Ferri, F. A. S.; Medeiros, I. P.; Galvão, R. K. H.; Júnior, C. L. Nascimento (December 2015). "Use of PHM Information and System Architecture for Optimized Aircraft Maintenance Planning". IEEE Systems Journal. 9 (4): 1197-1207. doi:10.1109/jsyst.2014.2343752. ISSN 1932-8184.

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• Lee, J.; Ghaffari, M.; Elmellgy, S. (2011). "Self-maintenance and engineering immune systems: Towards smarter machines and manufacturing systems". Annual Reviews in Control. 35 (1): 111-122. doi:10.1016/j.arcontrol.2011.03.007.
• Siegel, D.; Lee, J. (2011). "An Auto-Associative Residual Processing and K-means Clustering Approach for Anemometer Health Assessment". International Journal of Prognostics and Health Management Society. 2 (2): 12.
• Al-Atat, H.; Siegel, D.; Lee, J. (2011). "A Systematic Methodology for Gearbox Health Assessment and Fault Classification". International Journal of Prognostics and Health Management Society. 2 (1): 16.
• Wu, F.; Wang, T.; Lee, J. (2010). "An online adaptive condition-based maintenance method for mechanical mystems". Mechanical Systems and Signal Processing. 24 (8): 2985-2995. doi:10.1016/j.ymssp.2010.04.003.
• Liao, L.; Lee, J. (2009). "Design of a reconfigurable prognostics platform for machine tools". Expert Systems with Applications. 37 (1): 240-252. doi:10.1016/j.eswa.2009.05.004.
• Liao, L.; Lee, J. (2009). "A novel method for machine performance degradation assessment based on fixed cycle features test". Journal of Sound and Vibration. 326 (3-5): 894-908. doi:10.1016/j.jsv.2009.05.005.
• Lee, J.; Chen, Y.; Al-Atat, H.; Abuali, M.; Lapira, E. (2009). "A systematic approach for predictive maintenance service design: methodology and applications". International Journal of Internet manufacturing and Services. 2 (1): 76-94. doi:10.1504/ijims.2009.031341.
• Lee, J., Liao, L., Lapira E., Ni, J., and Li, L., "Informatics platform for designing and deploying e-Manufacturing systems," Collaborative Design and Planning for Digital Manufacturing, Springer, London, 2009, pp. 1-35
• Yan, J.; Isobe, N.; Lee, J. (2008). "Fuzzy Logic Combined Logistic Regression Methodology for Gas Turbine First Stage Nozzle Life,Prediction". Applied Mechanics and Materials. 10: 583-587.
• Liao, L.; Wang, H.; Lee, J. (2008). "Reconfigurable Watchdog Agent® for machine health prognostics". International Journal of COMADEM. 11 (3): 2-15.
• Lee, J.; Ni, J.; Djurdjanovic, D.; Qiu, H.; Liao, H. (2006). "Intelligent Prognostics Tools and E-Maintenance". Computers in Industry. 57: 476-489. doi:10.1016/j.compind.2006.02.014.
• Qiu, H.; Lee, J.; Lin, J. (2006). "Wavelet Filter-based Weak Signature Detection Method and its Application on Roller Bearing Prognostics". Journal of Sound and Vibration. 289 (4-5): 1066-1090. doi:10.1016/j.jsv.2005.03.007.
• Yan, J.; Lee, J. (2005). "Machine Degradation Assessment and Root Cause Classification Using Logistic Regression Method," IEEE Mechatronics". ASME Journal of Manufacturing Science and Engineering. 127: 912-914.
• Yan, J.; Koc, M.; Lee (2004). "A Prognostic Algorithm for Machine Performance Assessment and its Application". Production Planning & Control. 15 (8): 796-801. doi:10.1080/09537280412331309208.
• Qu, L.S.; Li, L.; Lee, J. (2004). "Enhanced diagnostic certainty using information entropy theory". International Journal of Advanced Engineering Informatics. 17 (3-4): 141-150. doi:10.1016/j.aei.2004.08.002.
• Djurdjanovic, D.; Lee, J.; Ni, J. (2003). "Watchdog Agent - An Infotronics Based Prognostics Approach for Product Performance Assessment and Prediction". International Journal of Advanced Engineering Informatics. 17 (3-4): 109-125. doi:10.1016/j.aei.2004.07.005.
• Lee, J., 2003 "Smart Products and Service Systems for e-Business Transformation," Special Issues on "Managing Innovative Manufacturing," International Journal of Technology Management pp. 45-52, Vol. 26, No. 1.
• Lee, J (2003). "e-Manufacturing Systems: fundamental and tools". Int. Journal of Robotics and Computer-integrated manufacturing. 9 (6): 501-507.
• Koc, M., Ni, J., Lee, J., Bandyopadhyay, P., "Introduction to e-Manufacturing," International Journal of Agile Manufacturing Vol. 6, Dec. 2003
• Qiu, H.; Lee, J.; Lin, J.; Yu, G. (2003). "Robust Performance Degradation Assessment Methods for Enhanced Rolling Element Bearings Prognostics". Journal of Advanced Engineering Informatics. 17 (3-4): 127-140. doi:10.1016/j.aei.2004.08.001.

### Electronics PHM

• Modeling aging effects of IGBTs in power drives by ringing characterization, A. Ginart, M. J. Roemer, P. W. Kalgren, and K. Goebel, in International Conference on Prognostics and Health Management, 2008, pp. 1-7.
• Prognostics of Interconnect Degradation using RF Impedance Monitoring and Sequential Probability Ratio Test, D. Kwon, M. H. Azarian, and M. Pecht, International Journal of Performability Engineering, vol. 6, no. 4, pp. 351-360, 2010.
• Latent Damage Assessment and Prognostication of Residual Life in Airborne Lead-Free Electronics Under Thermo-Mechanical Loads, P. Lall, C. Bhat, M. Hande, V. More, R. Vaidya, J. Suhling, R. Pandher, K. Goebel, in Proceedings of International Conference on Prognostics and Health Management, 2008.
• Failure Precursors for Polymer Resettable Fuses, S. Cheng, K. Tom, and M. Pecht, IEEE Transactions on Devices and Materials Reliability, Vol.10, Issue.3, pp. 374-380, 2010.
• Prognostic and Warning System for Power-Electronic Modules in Electric, Hybrid Electric, and Fuel-Cell Vehicles,Y. Xiong and X. Cheng, IEEE Transactions on Industrial Electronics, vol. 55, June 2008.
• Cheng, Shunfeng; Azarian, Michael H.; Pecht, Michael G. (2010). "Sensor Systems for Prognostics and Health Management". Sensors. 10 (6): 5774-5797. doi:10.3390/s100605774.
• Cheng, S.; Tom, K.; Thomas, L.; Pecht, M. (2010). "A Wireless Sensor System for Prognostics and Health Management". IEEE Sensors Journal. 10 (4): 856-862. doi:10.1109/jsen.2009.2035817.
• Jaai, Rubyca; Pecht, Michael (2010). "A prognostics and health management roadmap for information and electronics-rich systems". Microelectronics Reliability. 50 (3): 317-323. doi:10.1016/j.microrel.2010.01.006.
• Physics-of-failure based Prognostics for Electronic Products, Michael Pecht and Jie Gu, Transactions of the Institute of Measurement and Control 31, 3/4 (2009), pp. 309-322.
• Sachin Kumar, Vasilis Sotiris, and Michael Pecht, 2008 Health Assessment of Electronic Products using Mahalanobis Distance and Projection Pursuit Analysis, International Journal of Computer, Information, and Systems Science, and Engineering, vol.2 Issue.4, pp. 242-250.
• Guest Editorial: Introduction to Special Section on Electronic Systems Prognostics and Health Management, P. Sandborn and M. Pecht, Microelectronics Reliability, Vol. 47, No. 12, pp. 1847-1848, December 2007.
• Sandborn, P. A.; Wilkinson, C. (2007). "A Maintenance Planning and Business Case Development Model for the Application of Prognostics and Health Management (PHM) to Electronic Systems". Microelectronics Reliability. 47 (12): 1889-1901. doi:10.1016/j.microrel.2007.02.016.
• Gu, J.; Barker, D.; Pecht, M. (2007). "Prognostics Implementation of Electronics under Vibration Loading". Microelectronics Reliability. 47 (12): 1849-1856. doi:10.1016/j.microrel.2007.02.015.
• Prognostic Assessment of Aluminum Support Structure on a Printed Circuit Board, S. Mathew, D. Das, M. Osterman, M. Pecht, and R. Ferebee ASME Journal of Electronic Packaging, Vol. 128, Issue 4, pp. 339-345, December 2006.
• A Methodology for Assessing the Remaining Life of Electronic Products, S. Mathew, P. Rodgers, V. Eveloy, N. Vichare, and M. Pecht, International Journal of Performability Engineering, Vol. 2, No. 4, pp. 383-395, October, 2006.
• Prognostics and Health Management of Electronics, N. Vichare and M. Pecht, IEEE Transactions on Components and Packaging Technologies, Vol. 29, No. 1, March 2006.