Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network
In today’s interconnected industrial landscape, the ability to predict and monitor the operational status of equipment is crucial for maintaining efficiency and safety. Diesel engines, which are integral to numerous industrial applications, require reliable fault detection mechanisms to reduce opera...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Vibration |
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| Online Access: | https://www.mdpi.com/2571-631X/7/4/46 |
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| author | Gabriel Hasmann Freire Moraes Ronny Francis Ribeiro Junior Guilherme Ferreira Gomes |
| author_facet | Gabriel Hasmann Freire Moraes Ronny Francis Ribeiro Junior Guilherme Ferreira Gomes |
| author_sort | Gabriel Hasmann Freire Moraes |
| collection | DOAJ |
| description | In today’s interconnected industrial landscape, the ability to predict and monitor the operational status of equipment is crucial for maintaining efficiency and safety. Diesel engines, which are integral to numerous industrial applications, require reliable fault detection mechanisms to reduce operational costs, prevent unplanned downtime, and extend equipment lifespan. Traditional anomaly detection methods, such as thermometry, wear indicators, and radiography, often necessitate significant expertise, involve costly equipment shutdowns, and are limited by high usage costs and accessibility. Addressing these challenges, this study introduces a novel approach for fault detection in diesel engines by analyzing torsional vibration data in the time domain. The proposed method leverages short-term Fourier transform (STFT) and continuous wavelet transform (CWT) techniques, integrated with a convolutional neural network (CNN) to identify hidden patterns and diagnose engine conditions accurately. The method achieved a detection accuracy of 96.5% with STFT and 92.2% with CWT. To ensure robustness, the model was tested under various noise conditions, maintaining accuracies above 70% for noise levels up to 40%. This research provides a practical and efficient solution for real-time fault detection in diesel engines, offering a significant improvement over traditional methods in terms of cost, accessibility, and ease of implementation. |
| format | Article |
| id | doaj-art-3dc49f7eedaf4cf68b4c580e0617a199 |
| institution | OA Journals |
| issn | 2571-631X |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vibration |
| spelling | doaj-art-3dc49f7eedaf4cf68b4c580e0617a1992025-08-20T02:01:10ZengMDPI AGVibration2571-631X2024-09-017486389310.3390/vibration7040046Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural NetworkGabriel Hasmann Freire Moraes0Ronny Francis Ribeiro Junior1Guilherme Ferreira Gomes2Mechanical Engineering Institute, Universidade Federal de Itajuba (UNIFEI), Itajuba 37500-903, BrazilMechanical Engineering Institute, Universidade Federal de Itajuba (UNIFEI), Itajuba 37500-903, BrazilMechanical Engineering Institute, Universidade Federal de Itajuba (UNIFEI), Itajuba 37500-903, BrazilIn today’s interconnected industrial landscape, the ability to predict and monitor the operational status of equipment is crucial for maintaining efficiency and safety. Diesel engines, which are integral to numerous industrial applications, require reliable fault detection mechanisms to reduce operational costs, prevent unplanned downtime, and extend equipment lifespan. Traditional anomaly detection methods, such as thermometry, wear indicators, and radiography, often necessitate significant expertise, involve costly equipment shutdowns, and are limited by high usage costs and accessibility. Addressing these challenges, this study introduces a novel approach for fault detection in diesel engines by analyzing torsional vibration data in the time domain. The proposed method leverages short-term Fourier transform (STFT) and continuous wavelet transform (CWT) techniques, integrated with a convolutional neural network (CNN) to identify hidden patterns and diagnose engine conditions accurately. The method achieved a detection accuracy of 96.5% with STFT and 92.2% with CWT. To ensure robustness, the model was tested under various noise conditions, maintaining accuracies above 70% for noise levels up to 40%. This research provides a practical and efficient solution for real-time fault detection in diesel engines, offering a significant improvement over traditional methods in terms of cost, accessibility, and ease of implementation.https://www.mdpi.com/2571-631X/7/4/46fault detectionSTFTwaveletconvolutional neural networkdiesel engines |
| spellingShingle | Gabriel Hasmann Freire Moraes Ronny Francis Ribeiro Junior Guilherme Ferreira Gomes Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network Vibration fault detection STFT wavelet convolutional neural network diesel engines |
| title | Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network |
| title_full | Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network |
| title_fullStr | Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network |
| title_full_unstemmed | Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network |
| title_short | Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network |
| title_sort | fault classification in diesel engines based on time domain responses through signal processing and convolutional neural network |
| topic | fault detection STFT wavelet convolutional neural network diesel engines |
| url | https://www.mdpi.com/2571-631X/7/4/46 |
| work_keys_str_mv | AT gabrielhasmannfreiremoraes faultclassificationindieselenginesbasedontimedomainresponsesthroughsignalprocessingandconvolutionalneuralnetwork AT ronnyfrancisribeirojunior faultclassificationindieselenginesbasedontimedomainresponsesthroughsignalprocessingandconvolutionalneuralnetwork AT guilhermeferreiragomes faultclassificationindieselenginesbasedontimedomainresponsesthroughsignalprocessingandconvolutionalneuralnetwork |