NDLI: Remaining Useful Life Estimation of Critical Components With Application to Bearings

Content Provider	IEEE Xplore Digital Library
Author	Medjaher, K. Tobon-Mejia, D.A. Zerhouni, N.
Copyright Year	1963
Abstract	Prognostics activity deals with the estimation of the Remaining Useful Life (RUL) of physical systems based on their current health state and their future operating conditions. RUL estimation can be done by using two main approaches, namely model-based and data-driven approaches. The first approach is based on the utilization of physics of failure models of the degradation, while the second approach is based on the transformation of the data provided by the sensors into models that represent the behavior of the degradation. This paper deals with a data-driven prognostics method, where the RUL of the physical system is assessed depending on its critical component. Once the critical component is identified, and the appropriate sensors installed, the data provided by these sensors are exploited to model the degradation's behavior. For this purpose, Mixture of Gaussians Hidden Markov Models (MoG-HMMs), represented by Dynamic Bayesian Networks (DBNs), are used as a modeling tool. MoG-HMMs allow us to represent the evolution of the component's health condition by hidden states by using temporal or frequency features extracted from the raw signals provided by the sensors. The prognostics process is then done in two phases: a learning phase to generate the behavior model, and an exploitation phase to estimate the current health state and calculate the RUL. Furthermore, the performance of the proposed method is verified by implementing prognostics performance metrics, such as accuracy, precision, and prediction horizon. Finally, the proposed method is applied to real data corresponding to the accelerated life of bearings, and experimental results are discussed.
Sponsorship	IEEE Reliability Society
Starting Page	292
Ending Page	302
Page Count	11
File Size	1393619
File Format	PDF
ISSN	00189529
Volume Number	61
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Degradation Sensors Estimation Feature extraction Data models Monitoring remaining useful life Condition monitoring condition-based maintenance dynamic Bayesian networks mixture of Gaussians hidden Markov models performance metrics prognostics
Content Type	Text
Resource Type	Article
Subject	Safety, Risk, Reliability and Quality Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

Hierarchical HMMs for Autonomous Diagnostics and Prognostics

Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression

Degradation Modeling and Maintenance Decisions Based on Bayesian Belief Networks

Health-State Estimation and Prognostics in Machining Processes

Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction

Identifying new prognostic features for remaining useful life prediction

Review of remaining useful life prediction using support vector machine for engineering assets

A Profust Reliability Based Approach to Prognostics and Health Management

Remaining Useful Life Estimation of Critical Components With Application to Bearings

Similar Documents

A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

Hierarchical HMMs for Autonomous Diagnostics and Prognostics

Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression

Degradation Modeling and Maintenance Decisions Based on Bayesian Belief Networks

Health-State Estimation and Prognostics in Machining Processes

Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction

Identifying new prognostic features for remaining useful life prediction

Review of remaining useful life prediction using support vector machine for engineering assets

A Profust Reliability Based Approach to Prognostics and Health Management

Remaining Useful Life Estimation of Critical Components With Application to Bearings