Induction Motor’s Bearing Fault Diagnosis Using an Improved Short Time Fourier Transform

2018 ◽  
pp. 411-426
Author(s):  
Ahmed Hamida Boudinar ◽  
Ameur Fethi Aimer ◽  
Mohamed El Amine Khodja ◽  
Noureddine Benouzza
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Short Time Fourier Transform ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Short Time

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 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.

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.

scholarly journals A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors

2021 ◽  
Vol 3 (1) ◽  
pp. 228-242
Author(s):  
Christos T. Alexakos ◽  
Yannis L. Karnavas ◽  
Maria Drakaki ◽  
Ioannis A. Tziafettas
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Image Classification ◽  
Rolling Element Bearings ◽  
Short Time Fourier Transform ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Rolling Element ◽  
Computational Resources ◽  
Short Time

The most frequent faults in rotating electrical machines occur in their rolling element bearings. Thus, an effective health diagnosis mechanism of rolling element bearings is necessary from operational and economical points of view. Recently, convolutional neural networks (CNNs) have been proposed for bearing fault detection and identification. However, two major drawbacks of these models are (a) their lack of ability to capture global information about the input vector and to derive knowledge about the statistical properties of the latter and (b) the high demand for computational resources. In this paper, short time Fourier transform (STFT) is proposed as a pre-processing step to acquire time-frequency representation vibration images from raw data in variable healthy or faulty conditions. To diagnose and classify the vibration images, the image classification transformer (ICT), inspired from the transformers used for natural language processing, has been suitably adapted to work as an image classifier trained in a supervised manner and is also proposed as an alternative method to CNNs. Simulation results on a famous and well-established rolling element bearing fault detection benchmark show the effectiveness of the proposed method, which achieved 98.3% accuracy (on the test dataset) while requiring substantially fewer computational resources to be trained compared to the CNN approach.

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