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

2020 ◽  
Vol 10 (18) ◽  
pp. 6385 ◽  
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Shaft Speed ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Maintenance Schedules

Predicting bearing faults is an essential task in machine health monitoring because bearings are vital components of rotary machines, especially heavy motor machines. Moreover, indicating the degradation level of bearings will help factories plan maintenance schedules. With advancements in the extraction of useful information from vibration signals, diagnosis of motor failures by maintenance engineers can be gradually replaced by an automatic detection process. Especially, state-of-the-art methods using deep learning have contributed significantly to automatic fault diagnosis. This paper proposes a novel method for diagnosing bearing faults and their degradation level under variable shaft speed. In the proposed method, vibration signals are represented by spectrograms to apply deep learning methods through preprocessing using Short-Time Fourier Transform (STFT). Then, feature extraction and health status classification are performed by a convolutional neural network (CNN), VGG16. According to our various experiments, our proposed method can achieve very high accuracy and robustness for bearing fault diagnosis even under noisy environments.

scholarly journals Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6886
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Embedded Systems ◽  
Image Size ◽  
Bearing Faults ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Machine Health Monitoring ◽  
System Resources

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.

Sensitivity Analysis and Experimental Research on Ball Bearing Early Fault Diagnosis Based on Testing Signal From Casing

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
G. Chen ◽  
T. F. Hao ◽  
H. F. Wang ◽  
B. Zhao ◽  
J. Wang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Fault Diagnosis ◽  
Ball Bearing ◽  
Ball Bearings ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Vibration Attenuation ◽  
Aero Engine

The ball bearings of an aero-engine are key parts that frequently fail, and it is very important to effectively carry out fault diagnosis of the ball bearings. However, in the present research work, the ball bearing faults characteristics are extracted mainly from the bearing house signals, it is well known that usually only the casing signals can be measured in practical aero-engine test, and the ball bearing faults characteristics will greatly weaken after transmitting to the casing from the bearing house, therefore, it is very important to extract the fault characteristics of ball bearings from casing vibration signals for the ball bearing fault diagnosis in the practical aero-engine. In this study, simulation experiments for ball bearing faults are conducted using two rotor experimental rigs with casings. In addition, by means of the impulse response method, the transfer characteristics from the ball bearings to casing measuring points are measured, and a sensitivity analysis is performed. Faults are created on the inner ring, outer ring, and ball of the ball bearings in the two experimental rigs. The ball bearing experiments are carried out, and the fault features are extracted by means of a wavelet envelope analysis. The experimental results indicate that, with high connection stiffness between the bearing house and the casing, there is little vibration attenuation. However, with low connection stiffness, the vibration attenuation is great. After the impulse vibrations caused by the ball bearing faults are transmitted to the casing, the casing vibration is very weak and is often submerged in other signals. However, the ball bearing fault characteristic frequencies can still be effectively extracted from the weak casing vibration signals by using a wavelet envelope analysis. The research results in this study provide an experimental basis for a ball bearing fault diagnosis based on a casing test signal from a practical aero-engine.

scholarly journals Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 540 ◽  
Author(s):  
Nibaldo Rodriguez ◽  
Lida Barba ◽  
Pablo Alvarez ◽  
Guillermo Cabrera-Guerrero
Keyword(s):  
Fault Diagnosis ◽  
Shannon Entropy ◽  
Health Monitoring ◽  
Wavelet Packet ◽  
Rolling Element Bearing ◽  
Permutation Entropy ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Machine Health Monitoring ◽  
Machine Health

Bearing fault diagnosis methods play an important role in rotating machine health monitoring. In recent years, various intelligent fault diagnosis methods have been proposed, which are mainly based on the features extraction method combined with either shallow or deep learning methods. During the last few years, Shannon entropy features have been widely used in machine health monitoring, improving the accuracy of the bearing fault diagnosis process. Therefore, in this paper, we consider the combination of multi-scale stationary wavelet packet analysis with the Fourier amplitude spectrum to obtain a new discriminative Shannon entropy feature that we call stationary wavelet packet Fourier entropy (SWPFE). Features extracted by our SWPFE method are then passed onto a shallow kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. The proposed method was applied on two experimental vibration signal databases of a rolling element bearing and compared to two recently proposed methods called stationary wavelet packet permutation entropy (SWPPE) and stationary wavelet packet dispersion entropy (SWPPE). Based on our results, we can say that the proposed method is able to achieve better accuracy levels than both the SWPPE and SWPDE methods using fewer failure features. Further, as our method does not require any hyperparameter calibration step, it is less dependent on user experience/expertise.

scholarly journals A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

2018 ◽  
Vol 8 (9) ◽  
pp. 1621 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Yong Ren ◽  
Gongbo Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Feature Space ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Acceleration Sensors ◽  
Reconstructed Signal ◽  
Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

A survey on Deep Learning based bearing fault diagnosis

2019 ◽  
Vol 335 ◽  
pp. 327-335 ◽  
Author(s):  
Duy-Tang Hoang ◽  
Hee-Jun Kang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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