Journal ArticleDOI
Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models
Qiang Miao,Viliam Makis +1 more
TLDR
In this article, the condition classification is based on hidden Markov models (HMMs) processing information obtained from vibration signals, and the machinery condition is identified by selecting the HMM which maximises the probability of a given observation sequence.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2007-02-01. It has received 130 citations till now. The article focuses on the topics: Condition monitoring & Hidden Markov model.read more
Citations
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Journal ArticleDOI
Condition monitoring of wind turbines: Techniques and methods
TL;DR: A review of the state-of-the-art in the condition monitoring of wind turbines can be found in this article, which describes the different maintenance strategies, condition monitoring techniques and methods, and highlights in a table the various combinations of these that have been reported in the literature.
Journal ArticleDOI
Prognostic modelling options for remaining useful life estimation by industry
TL;DR: Business issues that need to be considered when selecting an appropriate modelling approach for trial are discussed and classification tables and process flow diagrams are presented to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment.
Prognostic modelling options for remaining useful life estimation by industry
TL;DR: In this paper, the authors discuss business issues that need to be considered when selecting an appropriate modelling approach for trial, and present classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment.
Journal ArticleDOI
Stochastic modelling and analysis of degradation for highly reliable products
Zhi-Sheng Ye,Min Xie +1 more
TL;DR: In this paper, degradation models are classified into three classes, that is, stochastic process models, general path models, and other models beyond these two classes.
Journal ArticleDOI
Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K -Nearest Neighbor Distance Analysis
TL;DR: The method is able to detect incipient faults and diagnose the locations of faults under masking noise, and provides a health index that tracks the degradation of faults without missing intermittent faults.
References
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Journal ArticleDOI
A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
Journal ArticleDOI
Singularity detection and processing with wavelets
Stéphane Mallat,Wen-Liang Hwang +1 more
TL;DR: It is proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents.
Journal ArticleDOI
Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains
Jean-Luc Gauvain,Chin-Hui Lee +1 more
TL;DR: A framework for maximum a posteriori (MAP) estimation of hidden Markov models (HMM) is presented, and Bayesian learning is shown to serve as a unified approach for a wide range of speech recognition applications.
Journal ArticleDOI
Statistical Process Control of Multivariate Processes
TL;DR: An overview of multivariate statistical methods use for the statistical process control of both continuous and batch multivariate processes and examples are provided of their use for analysing the operations of a mineral processing plant, for on-line monitoring and fault diagnosis of a continuous polymerization process and for the on- line monitoring of an industrial batch polymerization reactor.
Proceedings ArticleDOI
Video-based face recognition using adaptive hidden Markov models
Xiaoming Liu,Tsuhan Cheng +1 more
TL;DR: This paper proposes to use adaptive hidden Markov models (HMM) to perform video-based face recognition and shows that the proposed algorithm results in better performance than using majority voting of image-based recognition results.