Rolling element bearing diagnostics—A tutorial
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.
About: This article is published in Mechanical Systems and Signal Processing.The article was published on 2011-02-01. It has received 1858 citations till now. The article focuses on the topics: Rolling-element bearing & Cyclostationary process.
Citations
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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
Cites methods from "Rolling element bearing diagnostics..."
...Fault diagnosis using the proposed method Rolling element bearings and gears are the key components in rotating machinery [31,32]....
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TL;DR: Though intended primarily as a benchmark to aid in testing new diagnostic algorithms, it is also hoped that much of the discussion will have broader applicability to other bearing diagnostics cases.
1,167 citations
Cites background or methods from "Rolling element bearing diagnostics..."
...Typical signals and envelope signals from local faults in rolling element bearings [1]...
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...This method is a streamlined version of the benchmark method proposed in [1], which was based on the semi-automated bearing diagnostics procedure suggested in [8], and consisted of the following steps:...
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...This has prompted a great deal of research on vibration-based diagnostics of REBs over the last few decades, and the area is now quite well understood, with a number of powerful diagnostic techniques available [1]....
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TL;DR: In this article, a mathematical analysis to select the most significant intrinsic mode functions (IMFs) is presented, and the chosen features are used to train an artificial neural network (ANN) to classify bearing defects.
594 citations
TL;DR: In this article, a review of fault severity assessment of rolling bearing components is presented, focusing on data-driven approaches such as signal processing for extracting proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions.
453 citations
TL;DR: In this article, the authors have presented the various signal processing methods applied to the fault diagnosis of rolling element bearings with the objective of giving an opportunity to the examiners to decide and select the best possible signal analysis method as well as the excellent defect representative features for future application in the prognostic approaches.
453 citations
References
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TL;DR: In this article, the authors developed a spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coefficients, and achieved a performance within a factor log 2 n of the ideal performance of piecewise polynomial and variable-knot spline methods.
Abstract: SUMMARY With ideal spatial adaptation, an oracle furnishes information about how best to adapt a spatially variable estimator, whether piecewise constant, piecewise polynomial, variable knot spline, or variable bandwidth kernel, to the unknown function. Estimation with the aid of an oracle offers dramatic advantages over traditional linear estimation by nonadaptive kernels; however, it is a priori unclear whether such performance can be obtained by a procedure relying on the data alone. We describe a new principle for spatially-adaptive estimation: selective wavelet reconstruction. We show that variable-knot spline fits and piecewise-polynomial fits, when equipped with an oracle to select the knots, are not dramatically more powerful than selective wavelet reconstruction with an oracle. We develop a practical spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coefficients. RiskShrink mimics the performance of an oracle for selective wavelet reconstruction as well as it is possible to do so. A new inequality in multivariate normal decision theory which we call the oracle inequality shows that attained performance differs from ideal performance by at most a factor of approximately 2 log n, where n is the sample size. Moreover no estimator can give a better guarantee than this. Within the class of spatially adaptive procedures, RiskShrink is essentially optimal. Relying only on the data, it comes within a factor log 2 n of the performance of piecewise polynomial and variableknot spline methods equipped with an oracle. In contrast, it is unknown how or if piecewise polynomial methods could be made to function this well when denied access to an oracle and forced to rely on data alone.
8,153 citations
TL;DR: Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied and the notion of time-frequency localization is made precise, within this framework, by two localization theorems.
Abstract: Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied. The first procedure is the short-time or windowed Fourier transform; the second is the wavelet transform, in which high-frequency components are studied with sharper time resolution than low-frequency components. The similarities and the differences between these two methods are discussed. For both schemes a detailed study is made of the reconstruction method and its stability as a function of the chosen time-frequency density. Finally, the notion of time-frequency localization is made precise, within this framework, by two localization theorems. >
6,180 citations
01 Nov 1981
TL;DR: In this paper, a summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented, including classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods.
Abstract: A summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented in this tutorial. An examination of the underlying time series model assumed by each technique serves as the common basis for understanding the differences among the various spectrum analysis approaches. Techniques discussed include the classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods. A summary table in the text provides a concise overview for all methods, including key references and appropriate equations for computation of each spectral estimate.
2,941 citations
TL;DR: This communication describes a fast algorithm for computing the kurtogram over a grid that finely samples the ( f, Δ f ) plane and the efficiency of the algorithm is illustrated on several industrial cases concerned with the detection of incipient transient faults.
1,130 citations
TL;DR: In this article, the spectral kurtosis (SK) was used to detect and characterize nonstationary signals in the presence of strong masking noise and to detect incipient faults in rotating machines.
1,067 citations