DIAGNOSTICS AND CLASSIFICATION OF FAULTS IN DIESEL ENGINE COMPONENTS USING TIME-FREQUENCY APPROACH AND MACHINE
Keywords:
Diesel engine, vibration, acoustics, fast fourier transform, short time fourier transform, artificial neural network.Abstract
Diagnosing engine component faults is a challenging task for every researcher due to the complexity of engine operation. Developed faults in engine components subsequently reduce their performance and lead to increased maintenance costs. Therefore, it is necessary to implement an effective condition monitoring method to diagnose faults in engine components. Therefore, this work presents potential fault diagnosis techniques for detecting and diagnosing scuffing defects occurring in diesel engine components. Condition monitoring techniques such as vibration and acoustic emission analysis were used to obtain signals associated with faults. These signals were analyzed in time domain, frequency domain and time-frequency domain using signal processing techniques viz. fast Fourier transform ( FFT ) and short time Fourier transform ( STFT ). Statistical feature parameters were also extracted from the received signals to diagnose fault severity. Additionally, artificial neural network (ANN) models have been developed to predict and classify scuffing defects occurring in engine components. The results showed that FFT and STFT methods provided better diagnostic information. The developed neural network models effectively classified scoring defects on engine components with 100% accuracy.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0 DEED).
You are free to:
- Share — copy and redistribute the material in any medium or format
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes .
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
Rights of Authors
Authors retain the following rights:
1. Copyright and other proprietary rights relating to the article, such as patent rights,
2. the right to use the substance of the article in future works, including lectures and books,
3. the right to reproduce the article for own purposes, provided the copies are not offered for sale,
4. the right to self-archive the article.