A review on machinery diagnostics and prognostics implementing condition-based maintenance

doi:10.1016/J.YMSSP.2005.09.012

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A review on machinery diagnostics and prognostics implementing condition-based maintenance

Journal Article•DOI•

A review on machinery diagnostics and prognostics implementing condition-based maintenance

Andrew K.S. Jardine¹, Daming Lin¹, Dragan Banjevic¹•Institutions (1)

University of Toronto¹

01 Oct 2006-Mechanical Systems and Signal Processing (Academic Press)-Vol. 20, Iss: 7, pp 1483-1510

TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.

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About: This article is published in Mechanical Systems and Signal Processing.The article was published on 2006-10-01. It has received 3848 citations till now. The article focuses on the topics: Condition-based maintenance & Prognostics.

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Journal Article•DOI•

Remaining useful life estimation - A review on the statistical data driven approaches

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Xiaosheng Si¹, Wenbin Wang², Wenbin Wang³, Wenbin Wang⁴, Chang Hua Hu, Donghua Zhou¹ - Show less +2 more•Institutions (4)

Tsinghua University¹, University of Science and Technology Beijing², City University of Hong Kong³, University of Salford⁴

16 Aug 2011-European Journal of Operational Research

TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.

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1,667 citations

Cites background or methods from "A review on machinery diagnostics a..."

...Peng et al. (2010) recently provided an extended review on machine prognostics in the context of CBM based on the review of Jardine et al. (2006)....
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...Both Jardine et al. (2006) and Heng et al. (2009) are comprehensive to some extent and well structured....
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...Since Jardine et al. (2006) have given a good review on this subject, we will not touch these techniques again....
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...…health management, recently, several review papers specifically on CBM and associated RUL issues have appeared, see Kothamasu et al. (2006), Jardine et al. (2006), Heng et al. (2009), Pecht (2008), Gorjian et al. (2009a,b), van Noortwijk (2009), Dragomir et al. (2009) and Peng et al.…...
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...As analyzed by Jardine et al. (2006), to achieve a well estimated RUL using statistical datadriven methods, collecting and storing useful data (information) from targeted assets is necessary....
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Journal Article•DOI•

Deep learning and its applications to machine health monitoring

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Rui Zhao¹, Ruqiang Yan¹, Zhenghua Chen², Kezhi Mao², Peng Wang³, Robert X. Gao³ - Show less +2 more•Institutions (3)

Xi'an Jiaotong University¹, Nanyang Technological University², Case Western Reserve University³

15 Jan 2019-Mechanical Systems and Signal Processing

TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.

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1,569 citations

Journal Article•DOI•

Artificial intelligence for fault diagnosis of rotating machinery: A review

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Ruonan Liu¹, Boyuan Yang¹, Enrico Zio², Enrico Zio³, Xuefeng Chen¹ - Show less +1 more•Institutions (3)

Xi'an Jiaotong University¹, CentraleSupélec², Supélec³

01 Aug 2018-Mechanical Systems and Signal Processing

TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.

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1,287 citations

Journal Article•DOI•

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

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Yaguo Lei¹, Naipeng Li¹, Liang Guo¹, Ningbo Li¹, Tao Yan¹, Jing Lin¹ - Show less +2 more•Institutions (1)

Xi'an Jiaotong University¹

01 May 2018-Mechanical Systems and Signal Processing

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.

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1,116 citations

Cites background or methods from "A review on machinery diagnostics a..."

...For example, the stochastic model-based approaches are divided into statistical approaches in [1], data-driven models/approaches in [7,9,11] and conditional probability models in [8]....
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...Condition based maintenance (CBM) is a maintenance strategy which monitors the health condition of machinery in real time and makes an optimal maintenance decision based on condition monitoring information [1,2]....
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...[1] 2005 Statistical approaches, model-based approaches and AI approaches...
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Journal Article•DOI•

Rotating machinery prognostics: State of the art, challenges and opportunities

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Aiwina Soong Yin Heng¹, Sheng Zhang¹, Andy C. C. Tan¹, Joseph Mathew¹•Institutions (1)

Queensland University of Technology¹

01 Apr 2009-Mechanical Systems and Signal Processing

TL;DR: In this article, the authors synthesize and place these individual pieces of information in context, while identifying their merits and weaknesses, and discuss the identified challenges, and in doing so, alerts researchers to opportunities for conducting advanced research in the field.

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953 citations

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References

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Journal Article•DOI•

A tutorial on hidden Markov models and selected applications in speech recognition

[...]

Lawrence R. Rabiner¹•Institutions (1)

Bell Labs¹

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.

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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. >

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21,819 citations

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Time-frequency distributions-a review

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Leon Cohen¹•Institutions (1)

Hunter College¹

01 Jul 1989

TL;DR: A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented with emphasis on the diversity of concepts and motivations that have gone into the formation of the field.

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Abstract: A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented. The objective of the field is to describe how the spectral content of a signal changes in time and to develop the physical and mathematical ideas needed to understand what a time-varying spectrum is. The basic gal is to devise a distribution that represents the energy or intensity of a signal simultaneously in time and frequency. Although the basic notions have been developing steadily over the last 40 years, there have recently been significant advances. This review is intended to be understandable to the nonspecialist with emphasis on the diversity of concepts and motivations that have gone into the formation of the field. >

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3,568 citations

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The image processing handbook

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John C. Russ

01 Jan 2011

TL;DR: In this paper, the acquisition and use of digital images in a wide variety of scientific fields is discussed. But the focus is on high dynamic range imaging in more than two dimensions.

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Abstract: "This guide clearly explains the acquisition and use of digital images in a wide variety of scientific fields. This sixth edition features new sections on selecting a camera with resolution appropriate for use on light microscopes, on the ability of current cameras to capture raw images with high dynamic range, and on imaging in more than two dimensions. It discusses Dmax for X-ray images and combining images with different exposure settings to further extend the dynamic range. This edition also includes a new chapter on shape measurements, a review of new developments in image file searching, and a wide range of new examples and diagrams"

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3,017 citations

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Statistical Digital Signal Processing and Modeling

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Monson H. Hayes

19 Apr 1996

TL;DR: The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering.

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Abstract: From the Publisher: The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.

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2,549 citations

"A review on machinery diagnostics a..." refers methods in this paper

...For an introduction to signal analysis of random signals, see the book by Hayes [56]....
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...For details, see [56]....
[...]

Book•

Fault detection and diagnosis in engineering systems

[...]

Janos Gertler

01 Jan 1998

TL;DR: In this article, a fault detection and diagnosis framework for discrete linear systems with residual generators and residual generator parameters is presented for additive and multiplicative faults by parameter estimation using a parity equation.

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Abstract: Introduction to fault detection and diagnosis discrete linear systems random variables parameter estimation fundamentals analytical redundancy concepts parity equation implementation of residual generators design for structured residuals design for directional residuals residual generation for parametric faults robustness in residual generation statistical testing of residuals model identification for the diagnosis of additive faults diagnosing multiplicative faults by parameter estimation

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2,188 citations

"A review on machinery diagnostics a..." refers background or methods in this paper

...For an introduction to various methods for fault detection and diagnostics, see books [114,172,173]....
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...Other approaches Another class of machine fault diagnostic approaches is the model-based approaches [172,173]....
[...]