Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography
TL;DR: The application of the wavelet transform for machine fault diagnostics has been developed for last 10 years at a very rapid rate as mentioned in this paper, and a review on all of the literature is certainly not possible.
About: This article is published in Mechanical Systems and Signal Processing.The article was published on 2004-03-01. It has received 1023 citations till now. The article focuses on the topics: Wavelet packet decomposition & Second-generation wavelet transform.
Citations
More filters
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.
3,848 citations
Cites background from "Application of the wavelet transfor..."
...A recent review on the application of wavelet transform in vibration signal processing for machine diagnostics is given in [92]....
[...]
...A recent review with more extensive discussions and more references on the applications of wavelet transform for signal processing in machine condition monitoring and fault diagnostics was given in [92]....
[...]
TL;DR: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears.
1,858 citations
TL;DR: This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics.
1,410 citations
Additional excerpts
...Therefore, STFT is suitable for the analysis of quasistationary signals instead of real non-stationary signals [20]....
[...]
Book•
[...]
11 Jan 2013
TL;DR: Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit.
Abstract: With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysisis a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.
1,278 citations
Cites background from "Application of the wavelet transfor..."
...A detailed discussion of the use of the wavelet transformation for machine health monitoring is provided in [361]....
[...]
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.
1,164 citations
Additional excerpts
...[44,45], EMD [46–48], HHT [48–50], NN [51–54], Fuzzy Logic [55], Neuro-Fuzzy...
[...]
References
More filters
TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.
Abstract: Donoho and Johnstone (1994) proposed a method for reconstructing an unknown function f on [0,1] from noisy data d/sub i/=f(t/sub i/)+/spl sigma/z/sub i/, i=0, ..., n-1,t/sub i/=i/n, where the z/sub i/ are independent and identically distributed standard Gaussian random variables. The reconstruction f/spl circ/*/sub n/ is defined in the wavelet domain by translating all the empirical wavelet coefficients of d toward 0 by an amount /spl sigma//spl middot//spl radic/(2log (n)/n). The authors prove two results about this type of estimator. [Smooth]: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures. [Adapt]: the estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. The present proof of these results develops new facts about abstract statistical inference and its connection with an optimal recovery model. >
9,359 citations
"Application of the wavelet transfor..." refers methods in this paper
...Now, a lot of wavelet-based methods for the denoising have been available, for example, the soft-thresholding method [90] by Donoho, and the wavelet shrinkage denoising by Zheng and Li [91]....
[...]
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.
Abstract: The mathematical characterization of singularities with Lipschitz exponents is reviewed. Theorems that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform are reviewed. It is then 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. The wavelet transform of singularities with fast oscillations has a particular behavior that is studied separately. The local frequency of such oscillations is measured from the wavelet transform modulus maxima. It has been shown numerically that one- and two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two dimensions, the wavelet transform maxima indicate the location of edges in images. >
4,064 citations
"Application of the wavelet transfor..." refers methods in this paper
...The wavelet modulus maxima method has been almost a standard method for the detection of singularity points [79], in which the wavelet modulus maxima lines play an important role....
[...]
TL;DR: The reassignment method, first applied by Kodera, Gendrin, and de Villedary (1976) to the spectrogram, is generalized to any bilinear time-frequency or time-scale distribution.
Abstract: In this paper, the use of the reassignment method, first applied by Kodera, Gendrin, and de Villedary (1976) to the spectrogram, is generalized to any bilinear time-frequency or time-scale distribution. This method creates a modified version of a representation by moving its values away from where they are computed, so as to produce a better localization of the signal components. We first propose a new formulation of this method, followed by a thorough theoretical study of its characteristics. Its practical use for a large variety of known time-frequency and time-scale distributions is then addressed. Finally, some experimental results are reported to demonstrate the performance of this method. >
1,268 citations
TL;DR: In this paper, a denoising method based on wavelet analysis is applied to feature extraction for mechanical vibration signals, which is an advanced version of the famous soft thresholding denoizing method proposed by Donoho and Johnstone.
823 citations
TL;DR: The wavelet packet transform (WPT) is introduced as an alternative means of extracting time-frequency information from vibration signatures and significantly reduces the long training time that is often associated with the neural network classifier and improves its generalization capability.
Abstract: Condition monitoring of dynamic systems based on vibration signatures has generally relied upon Fourier-based analysis as a means of translating vibration signals in the time domain into the frequency domain. However, Fourier analysis provided a poor representation of signals well localized in time. In this case, it is difficult to detect and identify the signal pattern from the expansion coefficients because the information is diluted across the whole basis. The wavelet packet transform (WPT) is introduced as an alternative means of extracting time-frequency information from vibration signatures. The resulting WPT coefficients provide one with arbitrary time-frequency resolution of a signal. With the aid of statistical-based feature selection criteria, many of the feature components containing little discriminant information could be discarded, resulting in a feature subset having a reduced number of parameters without compromising the classification performance. The extracted reduced dimensional feature vector is then used as input to a neural network classifier. This significantly reduces the long training time that is often associated with the neural network classifier and improves its generalization capability.
515 citations
"Application of the wavelet transfor..." refers methods in this paper
...Yen and Lin [70] used the wavelet packet node energy selected by Fisher criterion function as fault feature and the network as classifier....
[...]