Sensitivity analysis of higher order coherent spectra in machine faults diagnosis

Yunusa‐Kaltungo, Akilu; Sinha, Jyoti

doi:10.1177/1475921716651394

Cited by 31 publications

(29 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stage 1 of the process is concerned with the fusion of measured vibration data (i.e., primary data) from multiple VCM sensors (say "q" number of sensors) installed on the monitored rotating machine, so as to develop single pCCB and pCCT fault diagnostic features (i.e., secondary data) that adequately represent the dynamic behaviour of the entire machine. Equations (1)-(4) provide further elaborations on the mathematical computations which have been extensively discussed in an earlier study by Yunusa-Kaltungo et al [29,51,52]: 1respectively represent the Fourier transformation (FT) of the rth segment at frequency f k of the vibration responses at bearings 1, 2, 3, 4, . .…”

Section: Stage 1: Sensor Level Data Fusion Using Pccb and Pcctmentioning

confidence: 99%

“…S pCCS (f k ) represents the poly-Coherent Composite Spectrum (pCCS) at frequency, f k . Equations (2) and 3respectively show the computations of pCCB and pCCT fault diagnosis features, while X r pCCS is the poly coherent composite FT for a certain segment 'r' of the measured vibration data from 'q' bearing locations at a certain frequency, f k , which was computed as [29,52]:…”

Section: Stage 1: Sensor Level Data Fusion Using Pccb and Pcctmentioning

confidence: 99%

See 1 more Smart Citation

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

2018

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Stage 1: Sensor Level Data Fusion Using Pccb and Pcctmentioning

confidence: 99%

Section: Stage 1: Sensor Level Data Fusion Using Pccb and Pcctmentioning

confidence: 99%

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

2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…These methods realize fault detection from the time domain, frequency domain, or time-frequency domain. In addition to the above methods, there is another kind of method that has been universally used for bearing fault detection to certain success, that is, higher order spectral (HOS) analysis [25][26][27][28][29][30][31][32][33]. HOS methods such as bispectrum, third order spectrum, etc, were put forward to compensate for the deficiencies of the FFT(Fast Fourier Transform Algorithm)-based techniques when they process the non-stationary and non-Gaussian signals.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition

Luo

Zhang

2019

Applied Sciences

View full text Add to dashboard Cite

The periodic impulse characteristics caused by rolling bearing damage are weak in the incipient failure stage. Thus, these characteristics are always drowned out by background noise and other harmonic interference. A novel approach based on multi-resolution singular value decomposition (MRSVD) is proposed in order to extract the periodic impulse characteristics for incipient fault detection. With the MRSVD method, the vibration signal is first decomposed to obtain a group of approximate signals and detailed signals with different resolutions. The first detail signal is mainly composed of noise and the last approximate signal is mainly composed of harmonic interference. Combined with the kurtosis index, the hidden periodic impulse signal will be extracted from the detail signals (in addition to the first detail signal). Thus, the incipient fault detection of a rolling bearing can be fulfilled according to the envelope demodulation spectrum of the extracted periodic impulse signal. The effectiveness of the proposed method has been demonstrated with both simulation and experimental analyses.

show abstract

“…A large number of sensors (typically more than 10,000) are required to collect the seismic signals, which are generated by an active excitation source. Similar to other distributed vibration data collection methods [1,2], using cable for data transmission in seismic signal acquisitions is a typical approach. To improve the quality of depth images and simplify acquisition logistics, replacing cabling with wireless technology should be a new trend in seismic exploration.…”

Section: Introductionmentioning

confidence: 99%

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

Tian

2017

Algorithms

View full text Add to dashboard Cite

Abstract:We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster's sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

show abstract

Sensitivity analysis of higher order coherent spectra in machine faults diagnosis

Cited by 31 publications

References 22 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

Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

Contact Info

Product

Resources

About