Journal ArticleDOI
A review on machinery diagnostics and prognostics implementing condition-based maintenance
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TLDR
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.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.read more
Citations
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Remaining useful life estimation - A review on the statistical data driven approaches
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.
Journal ArticleDOI
Deep learning and its applications to machine health monitoring
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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Artificial intelligence for fault diagnosis of rotating machinery: A review
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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Machinery health prognostics: A systematic review from data acquisition to RUL prediction
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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Rotating machinery prognostics: State of the art, challenges and opportunities
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.
References
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A tutorial on hidden Markov models and selected applications in speech recognition
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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Time-frequency distributions-a review
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.
Book
The image processing handbook
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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Statistical Digital Signal Processing and Modeling
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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Fault detection and diagnosis in engineering systems
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.