Prognostic and health management for engineering systems: a review of the data‐driven approach and algorithms
Authors:
Open Access
- 9 July 2015
- journal article
- review article
- Published by Institution of Engineering and Technology (IET) in The Journal of Engineering
- Vol. 2015 (7) , 215-222
Abstract
Prognostics and health management (PHM) has become an important component of many engineering systems and products, where algorithms are used to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL). PHM can provide many advantages to users and maintainers. Although primary goals are to ensure the safety, provide state of the health and estimate RUL of the components and systems, there are also financial benefits such as operational and maintenance cost reductions and extended lifetime. This study aims at reviewing the current status of algorithms and methods used to underpin different existing PHM approaches. The focus is on providing a structured and comprehensive classification of the existing state‐of‐the‐art PHM approaches, data‐driven approaches and algorithms.Keywords
This publication has 33 references indexed in Scilit:
- Size and power estimation for the Wilcoxon–Mann–Whitney test for ordered categorical dataStatistics in Medicine, 2011
- Prognostics of Failures in Embedded Planar Capacitors using Model-Based and Data-Driven ApproachesJournal of Intelligent Material Systems and Structures, 2011
- A prognostics and health management roadmap for information and electronics-rich systemsMicroelectronics Reliability, 2010
- Anomaly detectionACM Computing Surveys, 2009
- A particle-filtering approach for on-line fault diagnosis and failure prognosisTransactions of the Institute of Measurement and Control, 2009
- Novelty detection: a review—part 1: statistical approachesSignal Processing, 2003
- A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingIEEE Transactions on Signal Processing, 2002
- Novelty detection using extreme value statisticsIEE Proceedings - Vision, Image, and Signal Processing, 1999
- Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond1998
- A New Approach to Linear Filtering and Prediction ProblemsJournal of Basic Engineering, 1960