Monitoring gear vibrations through motor current signature analysis and wavelet transform
TL;DR: In this article, a multi-stage transmission gearbox (with and without defects) has been studied in order to replace the conventional vibration monitoring by MCSA, and it has been observed through FFT analysis that low frequencies of the vibration signatures have sidebands across line frequency of the motor current whereas high frequencies of vibration signature are difficult to be detected.
About: This article is published in Mechanical Systems and Signal Processing.The article was published on 2006-01-01. It has received 262 citations till now. The article focuses on the topics: Vibration.
Citations
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TL;DR: Current applications of wavelets in rotary machine fault diagnosis are summarized and some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, newWavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosed are discussed.
1,087 citations
Cites methods from "Monitoring gear vibrations through ..."
...They further integrated motor current signal analysis with DWT to monitor gear vibrations [87]....
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TL;DR: An adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis that can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task.
Abstract: A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.
275 citations
TL;DR: This paper deals with the demodulation of the current signal of an induction motor driving a multistage gearbox for its fault detection.
Abstract: Demodulation of vibration signal to detect faults in machinery has been a prominent prevalent technique that is discussed by a number of authors. This paper deals with the demodulation of the current signal of an induction motor driving a multistage gearbox for its fault detection. This multistage gearbox has three gear ratios, and thus, three rotating shafts and their corresponding gear mesh frequencies (GMFs). The gearbox is loaded electrically by a generator feeding an electrical resistance bank. Amplitude demodulation and frequency demodulation are applied to the current drawn by the induction motor for detecting the rotating shaft frequencies and GMFs, respectively. Discrete wavelet transform is applied to the demodulated current signal for denoising and removing the intervening neighboring features. Spectrum of a particular level, which comprises the GMFs, is used for gear fault detection
231 citations
Cites background or methods or result from "Monitoring gear vibrations through ..."
...In [13], these authors have established that current signature analysis can be effective in diagnosing faults at different load conditions in a multistage gearbox, which is validated through experiments....
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...This has already been verified by Kar and Mohanty [13], [22], [23]....
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...Its application to motor current has been discussed in some recent studies [12], [13]....
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...It has already been verified in [13] that the input shaft frequency can be an effective parameter in monitoring defects or loads while investigating both vibration and current signatures....
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...The objective of this paper is to extend the case discussed in [13] where vibration signature is compared with current signature....
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TL;DR: An optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy.
Abstract: This paper presents an optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy. For this purpose, slight-worn, medium-worn, and broken-tooth of a spur gear of the gearbox system were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed algorithm. Moreover, the processing of vibration signals has become much more difficult because a full-of-oil complex gearbox system has been considered to record raw vibration signals. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using piecewise cubic hermite interpolation to construct the sample signals with the same length. Next, standard deviation of wavelet packet coefficients of the vibration signals considered as the feature vector for training purposes of the ANN. To ameliorate the algorithm, GA was exploited to optimize the algorithm so as to determine the best values for ''mother wavelet function'', ''decomposition level of the signals by means of wavelet analysis'', and ''number of neurons in hidden layer'' resulted in a high-speed, meticulous two-layer ANN with a small-sized structure. This technique has been eliminated the drawbacks of the type of mother function for fault classification purpose not only in machine condition monitoring, but also in other related areas. The small-sized proposed network has improved the stability and reliability of the system for practical purposes.
186 citations
Cites methods from "Monitoring gear vibrations through ..."
...Sung et al. and also Gaborson used DB20 and DB4, as the mother wavelet, in their works, respectively (Kar & Mohanty, 2006)....
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...…monitoring, Daubechies family functions are often selected for signal analysis and synthesis arbitrarily by trial and error (Wang and McFadden, 1995; Samanta and Al-Balushi, 2003; Tse et al., 2004; Liu, 2005; Peng et al., 2005; Kar and Mohanty, 2006; Saravanan et al., 2007; Rafiee et al., 2007....
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...…which was previously determined by trial-and-error methods based on intrinsic characteristics of the data in several papers (Wang & McFadden, 1995; Samanta & AlBalushi, 2003; Tse et al., 2004; Liu, 2005; Kar & Mohanty, 2006; Saravanan et al., 2007; Rafiee, Arvani, Harifi, & Sadeghi, 2007)....
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TL;DR: In this article, the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes, are reviewed.
Abstract: Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicable to wind turbine farm-level health management and compares these methods on criteria such as reliability, accuracy and implementation aspects. It concludes with a brief discussion of the challenges and future trends in health assessment for wind farms.
163 citations
References
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TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >
20,028 citations
TL;DR: The perfect reconstruction condition is posed as a Bezout identity, and it is shown how it is possible to find all higher-degree complementary filters based on an analogy with the theory of Diophantine equations.
Abstract: The wavelet transform is compared with the more classical short-time Fourier transform approach to signal analysis. Then the relations between wavelets, filter banks, and multiresolution signal processing are explored. A brief review is given of perfect reconstruction filter banks, which can be used both for computing the discrete wavelet transform, and for deriving continuous wavelet bases, provided that the filters meet a constraint known as regularity. Given a low-pass filter, necessary and sufficient conditions for the existence of a complementary high-pass filter that will permit perfect reconstruction are derived. The perfect reconstruction condition is posed as a Bezout identity, and it is shown how it is possible to find all higher-degree complementary filters based on an analogy with the theory of Diophantine equations. An alternative approach based on the theory of continued fractions is also given. These results are used to design highly regular filter banks, which generate biorthogonal continuous wavelet bases with symmetries. >
1,804 citations
"Monitoring gear vibrations through ..." refers background in this paper
...Application of WVD gives rise to cross terms whereas in STFT, the uncertainty principle [14] limits the resolution of the time and frequency....
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...The demerit of this technique lies in the fact that good time resolution will give rise to poor frequency resolution and vice versa, as per the uncertainty principle [14]....
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...A number of articles have already been published citing advantages of WT over other time–frequency techniques such as WVD and STFT [12–14]....
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...4 is an example of such decomposition and the equation that governs this is as follows [14,24]:...
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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.
1,023 citations
"Monitoring gear vibrations through ..." refers background or methods in this paper
...The most recent and powerful technique that has wide applicability is wavelet transform [6,8–12]....
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...A number of articles have already been published citing advantages of WT over other time–frequency techniques such as WVD and STFT [12–14]....
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...A review of the wavelet application in fault diagnosis has been carried out by Peng and Chu [12]....
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02 Oct 1994
TL;DR: In this article, the authors used motor current spectral analysis to detect rolling-element bearing damage in induction machines, where the bearing failure modes were reviewed and bearing frequencies associated with the physical construction of the bearings were defined.
Abstract: This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. Vibration monitoring of mechanical bearing frequencies is currently used to detect the presence of a fault condition. Since these mechanical vibrations are associated with variations in the physical air gap of the machine, the air gap flux density is modulated and stator currents are generated at predictable frequencies related to the electrical supply and vibrational frequencies. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. The bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. Experimental results which show the vibration and current spectra of an induction machine with different bearing faults are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault. >
703 citations