Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach

Amar, Moty; Gondal, Iqbal; Wilson, Campbell

doi:10.1109/tie.2014.2327555

Cited by 190 publications

(92 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In which, certain data files are either non diagnosable or having electrical noises or the signal is clipped off as stated in the benchmark study made by [23]. Therefore, it can be observed from figure 4 that the accuracy of classification is less for dataset C-ii of load 2HP condition and the same can be seen in figure 5, for dataset D-i of load 2HP condition.…”

Section: Figure 3: (I)accuracy (Ii)sensitivity and (Iii)specificity mentioning

confidence: 86%

“…The data recorder had been equipped with a low -pass filter at the input stage for antialiasing. The benchmark study made by [23] indicates that the central load zone is at 6 O' clock position. In addition, the study revealed the fact that some of the vibration signals recorded on DE and FE positions were non useable due to lack of clarity, clipping of the data, and contamination of data due to the presence of significant electrical noise signals.…”

Section: Dataset Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

Nayana¹,

Tjprc²

2018

IJMPERD

View full text Add to dashboard Cite

Section: Figure 3: (I)accuracy (Ii)sensitivity and (Iii)specificity mentioning

confidence: 86%

Section: Dataset Descriptionmentioning

confidence: 99%

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

Nayana¹,

Tjprc²

2018

IJMPERD

View full text Add to dashboard Cite

“…Due to these facts, the real implantation of these technologies to prevent bearing currents is still scarce in many industrial sites. Vibration monitoring is widely employed to detect bearing damages (provoked by either bearing currents or other causes) [7][8][9][10]. The evaluation of characteristic frequency components in the vibration spectrum may help to determine the condition of the different parts of the bearing and, eventually, to detect the progressive degradation in any of these parts.…”

Section: Introductionmentioning

confidence: 99%

Time-frequency vibration analysis for the detection of motor damages caused by bearing currents

Prudhom

Antonino-Daviu

Razik

et al. 2017

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

“…Different possible signal sources, such as vibration signal and acoustic signal, are investigated to provide more accurate and efficient methods [1][2][3]. Although vibration signal is adopted in most fault diagnosis cases, acoustic signal is more desirable in some implementations due to its unique merits [2].…”

Section: Introductionmentioning

confidence: 99%

Correlated EEMD and Effective Feature Extraction for Both Periodic and Irregular Faults Diagnosis in Rotating Machinery

2017

View full text Add to dashboard Cite

Intelligent fault diagnosis of complex machinery is crucial for industries to reduce the maintenance cost and to improve fault prediction performance. Acoustic signal is an ideal source for diagnosis because of its inherent characteristics in terms of being non-directional and insensitive to structural resonances. However, there are also two main drawbacks of acoustic signal, one of which is the low signal to noise ratio (SNR) caused by its high sensitivity and the other one is the low computational efficiency caused by the huge data size. These would decrease the performance of the fault diagnosis system. Therefore, it is significant to develop a proper feature extraction method to improve computational efficiency and performance in both periodic and irregular fault diagnosis. To enhance SNR of the acquired acoustic signal, the correlation coefficient (CC) method is employed to eliminate the redundant intrinsic mode functions (IMF), which comes from the decomposition procedure of pre-processing known as ensemble empirical mode decomposition (EEMD), because the higher the correlated coefficient of an IMF is, the more significant fault signatures it would contain, and the redundant IMF would compromise both the SNR and the computational cost performance. Singular value decomposition (SVD) and sample Entropy (SampEn) are subsequently used to extract the fault feature, by exploiting their sensitivities to irregular and periodic fault signals, respectively. In addition, the proposed feature extraction method using sparse Bayesian based pairwise coupled extreme learning machine (PC-SBELM) outperforms the existing pairwise-coupling probabilistic neural network (PC-PNN) and pairwise-coupling relevance vector machine (PC-RVM) by 1.8% and 2%, respectively, to achieve an accuracy of 93.9%. The experiments conducted on the periodic and irregular faults in the gears and bearings have demonstrated that the proposed hybrid fault diagnosis system is effective.

show abstract

Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach

Cited by 190 publications

References 26 publications

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

Time-frequency vibration analysis for the detection of motor damages caused by bearing currents

Correlated EEMD and Effective Feature Extraction for Both Periodic and Irregular Faults Diagnosis in Rotating Machinery

Contact Info

Product

Resources

About