A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing
27 Sep 2017-Vol. 5, Iss: 4, pp 21
TL;DR: In this paper, an empirical study of feature extraction methods for the application of low-speed bearing condition monitoring is presented, where the selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure.
Abstract: This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed (≈ 1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. The selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure.
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Cites background from "A Review of Feature Extraction Meth..."
...Moreover, different input features and the influence of their hyperparameters will be studied and compared to end-to-end approaches [53], [54]....
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References
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TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Abstract: A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...
18,956 citations
"A Review of Feature Extraction Meth..." refers methods in this paper
...Empirical Mode Decomposition-Based Hilbert Huang Transform Empirical mode decomposition (EMD) [42] has been used in some applications that involve non-stationary signals (see, e....
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...Empirical Mode Decomposition-Based Hilbert Huang Transform Empirical mode decomposition (EMD) [42] has been used in some applications that involve nonstationary signals (see, e....
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TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
Abstract: Image Processing and Mathematical Morphology-Frank Y. Shih 2009-03-23 In the development of digital multimedia, the importance and impact of image processing and mathematical morphology are well documented in areas ranging from automated vision detection and inspection to object recognition, image analysis and pattern recognition. Those working in these ever-evolving fields require a solid grasp of basic fundamentals, theory, and related applications—and few books can provide the unique tools for learning contained in this text. Image Processing and Mathematical Morphology: Fundamentals and Applications is a comprehensive, wide-ranging overview of morphological mechanisms and techniques and their relation to image processing. More than merely a tutorial on vital technical information, the book places this knowledge into a theoretical framework. This helps readers analyze key principles and architectures and then use the author’s novel ideas on implementation of advanced algorithms to formulate a practical and detailed plan to develop and foster their own ideas. The book: Presents the history and state-of-the-art techniques related to image morphological processing, with numerous practical examples Gives readers a clear tutorial on complex technology and other tools that rely on their intuition for a clear understanding of the subject Includes an updated bibliography and useful graphs and illustrations Examines several new algorithms in great detail so that readers can adapt them to derive their own solution approaches This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
9,566 citations
"A Review of Feature Extraction Meth..." refers methods in this paper
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TL;DR: A new and related complexity measure is developed, sample entropy (SampEn), and a comparison of ApEn and SampEn is compared by using them to analyze sets of random numbers with known probabilistic character, finding SampEn agreed with theory much more closely than ApEn over a broad range of conditions.
Abstract: Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely related to entropy, which is easily applied to clinical cardiovascular and other time series. ApEn statistics, however, lead to inconsistent results. We have developed a new and related complexity measure, sample entropy (SampEn), and have compared ApEn and SampEn by using them to analyze sets of random numbers with known probabilistic character. We have also evaluated cross-ApEn and cross-SampEn, which use cardiovascular data sets to measure the similarity of two distinct time series. SampEn agreed with theory much more closely than ApEn over a broad range of conditions. The improved accuracy of SampEn statistics should make them useful in the study of experimental clinical cardiovascular and other biological time series.
6,088 citations
"A Review of Feature Extraction Meth..." refers background or methods in this paper
...The sample entropy has been proposed by Richman and Moorman [66] to improve the approximate entropy....
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...Sample Entropy The sample entropy has been proposed by Richman and Moorman [66] to improve the approximate entropy....
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...Sample Entropy The sample entropy has been proposed by Richman and Moorman [66] to improve the approximate entropy....
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...Sa le ntro y he sa le entro y has been ro ose by ich an an oor an [66] to i rove the a roxi ate entro y....
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TL;DR: This work introduces a new dimensionality reduction technique which it is called Piecewise Aggregate Approximation (PAA), and theoretically and empirically compare it to the other techniques and demonstrate its superiority.
Abstract: The problem of similarity search in large time series databases has attracted much attention recently. It is a non-trivial problem because of the inherent high dimensionality of the data. The most promising solutions involve first performing dimensionality reduction on the data, and then indexing the reduced data with a spatial access method. Three major dimensionality reduction techniques have been proposed: Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and more recently the Discrete Wavelet Transform (DWT). In this work we introduce a new dimensionality reduction technique which we call Piecewise Aggregate Approximation (PAA). We theoretically and empirically compare it to the other techniques and demonstrate its superiority. In addition to being competitive with or faster than the other methods, our approach has numerous other advantages. It is simple to understand and to implement, it allows more flexible distance measures, including weighted Euclidean queries, and the index can be built in linear time.
1,550 citations
"A Review of Feature Extraction Meth..." refers methods in this paper
...Piecewise Aggregate Approximation (PAA) and Adaptive Piecewise Constant Approximation (APCA) In online bearing condition monitoring cases, the changes of bearing condition can be effectively identified by measuring the similarity between two vibration signals (i.e., normal or reference and monitored signal) using Euclidean distance....
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...The similarity search methods are techniques that perform dimensionality data reduction methods such as piecewise aggregate approximation (PAA) [72,73] and adaptive piecewise constant approximation (APCA) [72]....
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...The detailed discussion and the application of PAA in slew bearing vibration data is presented in [39]....
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...The APCA [74], is another approach of PAA that allows arbitrary length segments....
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...Either PAA or APCA have particular advantages; for example, PAA has twice as many approximating segments, and APCA is able to separate a single segment in an area of low activity and many segments in areas of high activity [74]....
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01 Jun 2003
TL;DR: Empirical Mode Decomposition is presented, and issues related to its effective implementation are discussed, and an interpretation of the method in terms of adaptive constant-Q filter banks is supported.
Abstract: Huang’s data-driven technique of Empirical Mode Decomposition (EMD) is presented, and issues related to its effective implementation are discussed. A number of algorithmic variations, including new stopping criteria and an on-line version of the algorithm, are proposed. Numerical simulations are used for empirically assessing performance elements related to tone identification and separation. The obtained results support an interpretation of the method in terms of adaptive constant-Q filter banks.
1,448 citations
Additional excerpts
...In some preliminary studies on condition monitoring [39,45], the original EMD [46] is used for non-stationary slew bearing data....
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