Higher Order Spectra for Crack and Misalignment Identification in the Shaft of a Rotating Machine

Sinha, Jyoti

doi:10.1177/1475921707082309

Cited by 46 publications

(30 citation statements)

References 26 publications

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“…For instance, Hameed et al [38] provided a comprehensive review of CM techniques for wind turbines, where it was shown that accurate information on the overall condition of the rotor (a very important component of the wind energy converter) can be obtained by trending the relation between wind speed and active power output of the wind energy converter (WEC). The study [38] also showed that the use of higher order signal processing tools (bispectrum and bicoherence) [18][19][20][21][22] for detecting the presence or absence of phase coupling between the frequency components of the electrical power signal when classifying the WEC as faulty or healthy is very possible.…”

Section: Overview Of Data Fusion For Faults Detection and Classificationmentioning

confidence: 99%

“…This integration of multiple VCM approaches sometimes complicates the entire fault finding process. Efforts aimed at overcoming these deficiencies have led to the application of higher order spectra (HOS) [18][19][20][21][22], where both amplitude and phase information are retained. Other researchers have also attempted to standardise rotating machines fault diagnosis by incorporating artificial intelligence (AI) techniques such as artificial neural networks (ANN) [23][24][25] and support vector machines (SVM) [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

2018

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The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.

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Section: Overview Of Data Fusion For Faults Detection and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

2018

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“…Sinha [9] showed experimentally that higher order spectra analyses (bispectrum and trispectrum) can be used to distinguish malfunctions usually found in rotating machines, namely, crack and misalignment. The identification of higher harmonics in the system vibration responses is a typical case of nonlinear behavior.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cracks in Rotating Shafts by Using the Combination Resonances Approach and the Approximated Entropy Algorithm

Nicoletti

Cavalini

Steffen

2018

Shock and Vibration

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Shaft crack detection is a very serious matter and machines suspected of having a crack must be treated carefully. The importance attributed to this problem is addressed due to the serious consequences when cracks are not early detected in rotating systems. Various crack detection techniques were proposed in the last years, in which the vibration based techniques have demonstrated being efficient. However, these techniques fail for the cases in which incipient cracks are concerned. Recently, a nonlinear approach to detect cracks in rotating shafts was presented. The idea is to excite the shaft by using a harmonic force to induce combination resonances in the system. If the combination resonances appear in the vibration responses of the rotating system, the presence of cracks is confirmed. However, this methodology demonstrated being effective in detecting only deep cracks. In this context, the uniqueness of this paper relies on the possibility of detecting incipient transverse cracks in rotating shafts by associating the combination resonances approach with the so-called Approximated Entropy algorithm (ApEn algorithm). ApEn is a statistical value used to quantify irregularities in data series. Patterns and correspondences between samples of the same series are searched to detect anomalies. Considering that the combination resonances change the pattern of the shaft vibration responses, the ApEn algorithm can be used to highlight the presence of such resonances and, consequently, the detection of incipient cracks. The proposed approach was numerically evaluated by considering a horizontal rotating machine. A preliminary experimental investigation is also presented. The results demonstrated the efficiency of the conveyed methodology.

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“…The bispectrum (and the bicoherence) was applied by Boltežar et al [28] to the dynamics of washing machines, demonstrating that certain excited modes of the oscillation could be associated with quadratic phase coupling. It has been demonstrated experimentally that the bispectrum can be used to monitor the increase in second harmonic generation by a rotating axle which is cracked or misaligned [29].…”

Section: Introductionmentioning

confidence: 99%

“…From Eqs (28). and(29) odd powers of the noise have a zero expected value and utilizing the integral[35] +∞ −∞…”

mentioning

confidence: 99%

Application of the bispectrum for detection of small nonlinearities excited sinusoidally

Courtney

Neild

Wilcox

et al. 2010

Journal of Sound and Vibration

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The non-linear behaviour of damaged systems excited by vibration or ultrasound offers potential as a technique for damage detection in machine condition monitoring and nondestructive testing applications. The bispectrum, a third order spectrum, has properties that lend themselves to the measurement of non-linearities in systems. The properties of interest are insensitivity to Gaussian noise and ability to detect quadratic phase coupling. However, thus far analysis of the statistics of bispectrum estimation have been mainly aimed at stochastic systems. Many applications to vibration and ultrasound involve primarily deterministic, periodic excitations in the presence of stochastic noise. This paper considers the properties of a bispectrum estimate when applied to a system with weak quadratic non-linearity excited by the superposition of two sinusoids in the presence of additive Gaussian noise. This is compared, using signal-to-noise ratios, to the powerspectrum, with the results validated using numerical data. Also addressed is the effect of quadratic phase coupling on such a system (in the absence of noise).

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Higher Order Spectra for Crack and Misalignment Identification in the Shaft of a Rotating Machine

Cited by 46 publications

References 26 publications

Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

Detection of Cracks in Rotating Shafts by Using the Combination Resonances Approach and the Approximated Entropy Algorithm

Application of the bispectrum for detection of small nonlinearities excited sinusoidally

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