Diagnosis of broken-bars fault in induction machines using higher order spectral analysis

Saïdi, Lotfi; Fnaiech, Farhat; Henao, H.; Cirrincione, Giansalvo

doi:10.1016/j.isatra.2012.08.003

Cited by 86 publications

(68 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To verify the proposed method and analyze the classify accuracy on different fault pattern, the behavior of the simulated fault patterns number, PCA, and parameters optimization method were examined through a full factorial experiment. It mainly had the following three factors: (1) parameters optimization method with two levels (CV MSVM and PPA MSVM), (2) fault pattern number with three levels of four patterns (see Table 3, fault numbers 1-4), seven patterns (see Table 3, fault numbers 1-7), and ten patterns (see Table 3, fault numbers 1-10). Table 6 shows the results of the full factorial experiment.…”

Section: Results Analysismentioning

confidence: 99%

“…EMD method obtained the first few high-frequency IMF components, which can effectively represent the signal characteristics; remaining IMFs belong to the residual component, which is mainly the low-frequency noise. Selected highfrequency IMF components are transformed by the Hilbert method (see (2)), and (3) construct their analytic signals.…”

Section: Features Extractionmentioning

confidence: 99%

“…Many fault diagnosis analysis methods have been developed to accurately and automatically identify faults in the past two decades. They usually use some basic measurements, like vibration, acoustic, temperature, and wear debris analysis [1,2]. Although these methods have been beneficial, tests are still quite expensive and timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Zhang

Cai

Cheng

2018

Shock and Vibration

View full text Add to dashboard Cite

Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. In this paper, a novel method called multiscale feature extraction (MFE) and multiclass support vector machine (MSVM) with particle parameter adaptive (PPA) is proposed. MFE is used to preprocess the process signals, which decomposes the data into intrinsic mode function by empirical mode decomposition method, and instantaneous frequency of decomposed components was obtained by Hilbert transformation. Then, statistical features and principal component analysis are utilized to extract significant information from the features, to get effective data from multiple faults. MSVM method with PPA parameters optimization will classify the fault patterns. The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1) the proposed intelligent method (MFE PPA MSVM) improves the classification recognition rate; (2) the accuracy will decline when the number of fault patterns increases; (3) prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. It verifies the method is feasible and efficient for fault diagnosis.

show abstract

Section: Results Analysismentioning

confidence: 99%

Section: Features Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Zhang

Cai

Cheng

2018

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…The time-domain analysis methods are to identify the quantities of a signal related to its time behaviour such as the maximum amplitude, root mean square (rms) value, kurtosis and crest factor of a signal, while the frequency-domain analysis methods are to analyse the contents of a signal related to its frequency behaviour, like power spectrum, cepstrum and higher-order spectrum of a signal. [7][8][9][10][11]. Since machinery operating in nonstationary mode generates a signature which at each instant of time has a distinct frequency, it is desirable to use time-frequency analysis technique to see how frequency changes with time.…”

Section: Introductionmentioning

confidence: 99%

Adaptive noise cancelling and time–frequency techniques for rail surface defect detection

Liang

Iwnicki

Ball

et al. 2015

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

Adaptive noise cancelling (ANC) is a technique which is very effective to remove additive noises from the contaminated signals. It has been widely used in the fields of telecommunication, radar and sonar signal processing. However it was seldom used for the surveillance and diagnosis of mechanical systems before late of 1990s. As a promising technique it has gradually been exploited for the purpose of condition monitoring and fault diagnosis. Time-frequency analysis is another useful tool for condition monitoring and fault diagnosis purpose as time-frequency analysis can keep both time and frequency information simultaneously. This paper presents an ANC and time-frequency application for railway wheel flat and rail surface defect detection. The experimental results from a scaled roller test rig show that this approach can significantly reduce unwanted interferences and extract the weak signals from strong background noises. The combination of ANC and time-frequency analysis may provide us one of useful tools for condition monitoring and fault diagnosis of railway vehicles.

show abstract

“…The MCSA technique can easily detect broken rotor bars by using only current sensors; thus noninvasive detection is achieved. Compared with other detecting signals for the fault of BRB, it has many advantages in the aspects of accuracy, reliability, and conciseness [7][8][9][10]. Up to now, MCSA is applied in squirrel-cage induction motor to monitor BRB.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Broken Rotor Bars in Squirrel-Cage Induction Motor of Hoister Based on Duffing Oscillator and Multifractal Dimension

Zhao

Wang

Zhang

2014

Advances in Mechanical Engineering

View full text Add to dashboard Cite

This paper is to propose a novel fault diagnosis method for broken rotor bars in squirrel-cage induction motor of hoister, which is based on duffing oscillator and multifractal dimension. Firstly, based on the analysis of the structure and performance of modified duffing oscillator, the end of transitional slope from chaotic area to large-scale cycle area is selected as the optimal critical threshold of duffing oscillator by bifurcation diagrams and Lyapunov exponent. Secondly, the phase transformation duffing oscillator from chaos to intermittent chaos is sensitive to the signals, whose frequency difference is quite weak from the reference signal. The spectrums of the largest Lyapunov exponents and bifurcation diagrams of the duffing oscillator are utilized to analyze the variance in different parameters of frequency. Finally, this paper is to analyze the characteristics of both single fractal (box-counting dimension) and multifractal and make a comparison between them. Multifractal detrended fluctuation analysis is applied to detect extra frequency component of current signal. Experimental results reveal that the method is effective for early detection of broken rotor bars in squirrel-cage induction motor of hoister.

show abstract

Diagnosis of broken-bars fault in induction machines using higher order spectral analysis

Cited by 86 publications

References 25 publications

Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Multiple‐Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Adaptive noise cancelling and time–frequency techniques for rail surface defect detection

Fault Diagnosis of Broken Rotor Bars in Squirrel-Cage Induction Motor of Hoister Based on Duffing Oscillator and Multifractal Dimension

Contact Info

Product

Resources

About