scholarly journals Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hongmei Liu ◽  
Lianfeng Li ◽  
Jian Ma
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Short Time Fourier Transform ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Short Time

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.

scholarly journals Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6437
Author(s):  
Sihan Wang ◽  
Dazhi Wang ◽  
Deshan Kong ◽  
Jiaxing Wang ◽  
Wenhui Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Learning Method ◽  
Safety Issues ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Damage Data ◽  
Meta Learning

Fault diagnosis methods based on deep learning and big data have achieved good results on rotating machinery. However, the conventional deep learning method of bearing fault diagnosis is mostly based on laboratory artificial simulation data, and there is an error with actual fault data, which will reduce the generalization performance of the deep learning method. In addition, labeled data are very precious in real industrial environment. Due to expensive equipment and personnel safety issues, it is difficult to obtain a large amount of high-quality fault labeling data. Therefore, in this paper, we propose a metric-based meta-learning method named Reinforce Relation Network (RRN) for diagnosing bearing faults with few-shot samples. In the proposed method, a 1D convolution neural network is used to extract fault features, and a metric learner is used to predict the similarity between samples under different transfer conditions. Label smoothing and the Adabound algorithm are utilized to further improve the performance of network classification. The performance of the proposed method is verified on a dataset which contains artificial damage and natural damage data. The comparison studies with other methods demonstrate the superiority of the proposed method in the few-shot scenario.

Bearing Fault Diagnosis of a PWM Inverter Fed-Induction Motor Using an Improved Short Time Fourier Transform

2019 ◽  
Vol 14 (3) ◽  
pp. 1201-1210 ◽  
Author(s):  
Mohammed-El-Amine Khodja ◽  
Ameur Fethi Aimer ◽  
Ahmed Hamida Boudinar ◽  
Noureddine Benouzza ◽  
Azeddine Bendiabdellah
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Short Time Fourier Transform ◽  
Pwm Inverter ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Short Time

scholarly journals Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 13
Author(s):  
Jianpeng Ma ◽  
Chengwei Li ◽  
Guangzhu Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Rolling Bearing ◽  
Fusion Method ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Evolution Analysis ◽  
Comparison Results

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

Using ASTFT Spectrum to Suppress Cross Terms in WVD and its Application in Fault Diagnosis

2009 ◽  
Vol 626-627 ◽  
pp. 535-540
Author(s):  
B.P. Tang ◽  
F. Li ◽  
W.Y. Liu
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Effective Suppression ◽  
Cross Terms ◽  
Diagnosis Method ◽  
Simulation Results ◽  
Short Time

A new fault diagnosis method to suppress cross terms of Wigner-Ville distribution (WVD) using Adaptive Short-time Fourier Transform (ASTFT) spectrum is put forward. The relationships of correlation between auto terms and cross terms of WVD are obtained theoretically by analyzing the WVD. Firstly, the signal ASTFT spectrum which can determine the signal component positions in the time-frequency plane is obtained. Then, the ASTFT spectrum as a window function is selected to process the signal WVD. Thus the cross terms can be effectively restrained. The simulation results show that a better resolution and more effective suppression of cross terms can be obtained. At last, the proposed method is applied to the fault diagnosis of bearing. The simulation and the experiment results indicate that the proposed method is effective in feature extraction.

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