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Journal ArticleDOI

Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

01 Jan 2014-Mechanical Systems and Signal Processing (Academic Press)-Vol. 42, Iss: 1, pp 314-334
TL;DR: A comprehensive review of the PHM field is provided, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information, to enable rapid customization and integration of PHM systems for diverse applications.
About: This article is published in Mechanical Systems and Signal Processing.The article was published on 2014-01-01. It has received 1164 citations till now. The article focuses on the topics: Prognostics.
Citations
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01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

2,933 citations

Journal ArticleDOI
TL;DR: This paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing and describes worldwide movements in intelligent manufacturing.

1,602 citations

Journal ArticleDOI
Feng Jia1, Yaguo Lei1, Jing Lin1, Xin Zhou1, Na Lu1 
TL;DR: The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

1,289 citations

Journal ArticleDOI
Yaguo Lei1, Naipeng Li1, Liang Guo1, Ningbo Li1, Tao Yan1, Jing Lin1 
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.

1,116 citations

Journal ArticleDOI
TL;DR: A new data-driven approach for prognostics using deep convolution neural networks (DCNN) using time window approach is employed for sample preparation in order for better feature extraction by DCNN.

948 citations

References
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Journal ArticleDOI
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

37,861 citations


"Prognostics and health management d..." refers background in this paper

  • ...[183,184] " To project feature space into a higher...

    [...]

Book
01 Jan 1989
TL;DR: Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.
Abstract: From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models... Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."- Choice "Well written, clearly organized, and comprehensive... the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent." - Contemporary Sociology "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."-The Statistician In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.

35,847 citations

Journal ArticleDOI
Lawrence R. Rabiner1
01 Feb 1989
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Abstract: This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >

21,819 citations

Book
01 Jan 1973

20,541 citations


"Prognostics and health management d..." refers background in this paper

  • ...Fisher Linear Discriminant [160] " To reduce the dimensionality by seeking a projection that best separates the data in a least-squares sense " Reduces multi-dimensionality...

    [...]