Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram

Pham, Minh Tuan; Kim, Jong‐Myon; Kim, Cheol Hong

doi:10.3390/app10186385

Cited by 64 publications

(38 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SNR dB = 10 log 10 P signal P noise (5) The experimental results in noisy conditions are shown in Table 4, which indicate that in lower SNR values, the accuracy of the proposed method becomes relatively worse, but still provides high accuracy. When the SNR values are positive, the average accuracy is more than 89.85% and 92.11% for Dataset 1 and Dataset 2, respectively.…”

Section: Compound Fault Diagnosis In Noisy Conditionsmentioning

confidence: 99%

“…Therefore, many studies have focused on bearing FD using various types of signals acquired from sensors mounted on electric motors, especially vibration signals. In the last decade, with the advent of machine learning techniques, bearing FD methods using vibration signals have achieved high accuracy under specific conditions [5][6][7]. However, in realistic scenarios such as diagnosis of very early developed damage [3], diagnosis of very slow rotating machinery, and monitoring under high-vibration environments, acoustic…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Pham

Kim

2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

show abstract

Section: Compound Fault Diagnosis In Noisy Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Pham

Kim

2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, their work does not use other information available from signal data like skewness, kurtosis, impulse factor, RMS value [16]. Another work done by Pham et al proposes a method that converts the signal data into its spectrogram which is then fed to VGG16 for classification [17], [18]. In their paper, they used only four classes and achieved 98.8% accuracy.…”

Section: Introductionmentioning

confidence: 99%

FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

Magar

Ghule

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can provide us a greater understanding of different faults occurring in mechanical systems. In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults. We also highlight the importance of using different signal processing methods and their effect on accuracy for bearing fault detection. Apart from the traditional machine learning algorithms we also propose a convolutional neural network FaultNet which can effectively determine the type of bearing fault with a high degree of accuracy. The distinguishing factor of this work is the idea of channels proposed to extract more information from the signal, we have stacked the 'Mean' and 'Median' channels to raw signal to extract more useful features to classify the signals with greater accuracy.

show abstract

“…With respect to the frequency domain, the decomposition of the signal by the Fourier transform [ 27 ], the Hilbert–Huang transform (based on EMD) [ 28 ], and the continuous wavelet transform [ 29 ] can be found. In addition, the analysis of the signal by the kurtogram application [ 30 ] or spectrogram [ 31 ] are used to detect changes in the frequency characteristics. In general, the decomposition of the vibration signals for detecting and diagnosing low-speed bearing damage are not comparable to the results obtained by their application to regular bearings [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Sandoval

Leturiondo

Vidal

et al. 2021

Sensors

View full text Add to dashboard Cite

To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

show abstract

Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram

Cited by 64 publications

References 14 publications

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Contact Info

Product

Resources

About