NDLI: A probabilistic description scheme for rotating machinery health evaluation

Content Provider	Springer Nature Link
Author	Miao, Qiang Wang, Dong Pecht, Michael
Copyright Year	2010
Abstract	Condition-based maintenance has become more popular in recent years because of its advantages in terms of minimizing downtime, extending lifetime, and reducing cost. This kind of maintenance strategy is based on condition monitoring of machinery in operation. Ccondition monitoring is a key step in maintenance decision analysis. Numerous non-stationary signal processing methods have been developed to reveal fault characteristics in rotating machinery. In this study, an adaptive signal analysis method called empirical mode decomposition is employed for gearbox vibration signal preprocessing. Considering a modulation phenomenon that appeared in a faulty gear, the Hilbert Transform is applied to obtain an envelope signature, which usually contains abundant fault-related signatures. Being different from other failure classification problems, this paper is concerned with determining the probability of normal condition based on current observations describing the condition of a gearbox. Moreover, according to Bayes rule, this problem can be translated to estimate the conditional probability of current observations given normal gearbox condition using a Hidden Markov Model. From this point, a novel probabilistic health description index called Average Probability Index is proposed for gearbox health evaluation. For automatic detection, a semi-dynamic threshold is presented to detect an early fault in a gear. At last, validation and comparative studies are performed using two sets of gearbox lifetime accelerated testing vibration data. The results show the advantages of the proposed method for gearbox condition monitoring.
Starting Page	2421
Ending Page	2430
Page Count	10
File Format	PDF
ISSN	1738494X
Journal	Journal of Mechanical Science and Technology
Volume Number	24
Issue Number	12
e-ISSN	19763824
Language	English
Publisher	Korean Society of Mechanical Engineers
Publisher Date	2011-01-20
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Condition-based maintenance Health evaluation Empirical mode decomposition Average probability index Hidden Markov model Industrial and Production Engineering Vibration, Dynamical Systems, Control Mechanical Engineering
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Mechanical 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

Combined rotor fault diagnosis in rotating machinery using empirical mode decomposition

Fault diagnosis of partial rub and looseness in rotating machinery using Hilbert-Huang transform

Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition

A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

Singularity detection in machinery health monitoring using Lipschitz exponent function

Fault diagnosis of rotating machinery based on kernel density estimation and Kullback-Leibler divergence

Multiscale singular value manifold for rotating machinery fault diagnosis

A machine learning approach for the condition monitoring of rotating machinery

Surge detection methods using empirical mode decomposition and continuous wavelet transform for a centrifugal compressor

A probabilistic description scheme for rotating machinery health evaluation

Similar Documents

Combined rotor fault diagnosis in rotating machinery using empirical mode decomposition

Fault diagnosis of partial rub and looseness in rotating machinery using Hilbert-Huang transform

Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition

A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

Singularity detection in machinery health monitoring using Lipschitz exponent function

Fault diagnosis of rotating machinery based on kernel density estimation and Kullback-Leibler divergence

Multiscale singular value manifold for rotating machinery fault diagnosis

A machine learning approach for the condition monitoring of rotating machinery

Surge detection methods using empirical mode decomposition and continuous wavelet transform for a centrifugal compressor

A probabilistic description scheme for rotating machinery health evaluation