BROWSE
Detail
Journal Article
A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions
- Authors
- Rafia Nishat Toma, Farzin Piltan, Kichang Im, Dongkoo Shon, Tae Hyun Yoon, Dae-Seung Yoo, Jong-Myon Kim
- Issue Date
- 2022-07
- Citation
- Sensors, v.22, no.13, pp.1-23
- ISSN
- 1424-8220
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal?릘o?릋mage encoding technique and a convolution neural network (CNN) with the motor?릀urrent signal is proposed to classify bearing faults. In the beginning, we split the dataset into four parts, considering the operating conditions. Then, the original signal is segmented into multiple samples, and we apply the Gramian angular field (GAF) algorithm on each sample to generate two?릁imensional (2?륞) images, which also converts the time?릗eries signals into polar coordinates. The image conversion technique eliminates the requirement of manual feature extraction and creates a distinct pattern for individual fault signatures. Finally, the resultant image dataset is used to design and train a 2?릐ayer deep CNN model that can extract high?릐evel features from multiple images to classify fault conditions. For all the experiments that were conducted on different operating conditions, the proposed method shows a high classification accuracy of more than 99% and proves that the GAF can efficiently preserve the fault characteristics from the current signal. Three built?릋n CNN structures were also applied to classify the images, but the simple structure of a 2?? layer CNN proved to be sufficient in terms of classification results and computational time. Finally, we compare the experimental results from the proposed diagnostic framework with some state?릓f-the?륾rt diagnostic techniques and previously published works to validate its superiority under inconsistent working conditions. The results verify that the proposed method based on motor?릀urrent signal analysis is a good approach for bearing fault classification in terms of classification accuracy and other evaluation parameters.
- KSP Keywords
- Bearing Fault Classification, CNN model, Classification framework, Classification models, Computational time, Convolution neural network(CNN), Deep CNN, Diagnostic technique, Encoding Technique, Evaluation parameters, Fault Characteristics
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
