AN EXPERIMENTAL EVALUATION OF INTELLIGENT FAULT DETECTION AND CLASSIFICATION FOR INDUCTION MOTORS UTILIZING MACHINE LEARNING APPROACHES
Öz
The concept of Industry 4.0 aims fully digital and autonomous production. For manufacturing systems to work properly, their maintenance must be done correctly. However, while unnecessary maintenance causes waste of money and time, skipping necessary maintenance can also cause unexpected down times in production. Predictive maintenance (PdM) aims to predict and diagnose faults at an early stage and also the time remaining for future failures of equipment which might provide significant cost savings compared to traditional maintenance approaches. Today's sensor and data collection technologies have become more accessible and reliable which paved the way for manufacturers to continuously monitor their equipment, collect and store large volume of data in their production systems. Using this data with machine learning (ML) algorithms and analyzing the fingerprints of equipment faults can help making more informed decision regarding maintenance in manufacturing which might help increasing production quality and capacity. In our study, induction motors (IM) which are widely used in factories for different purposes and their failure scenarios are targeted. Triaxial vibration data collected from two similar induction motors under different operating conditions are examined. Various features of vibration data are extracted, scaled and labeled with a status information of the operation state. The obtained dataset is analyzed with six different ML algorithms. Performances are examined and compared against each other. In this study, we present our promising experimental results and experimentally show that the abnormal operating conditions of IMs can be successfully detected utilizing ML algorithms for a PdM application.
Anahtar Kelimeler
Destekleyen Kurum
TÜBİTAK
Proje Numarası
Project No: 118C252 and also Project No: 1170452
Teşekkür
This research is supported in part by 2232 International Fellowship for Outstanding Researchers Program of TÜBİTAK (Project No: 118C252) and also TÜBİTAK 1511 IOTOPRO Project (Project No: 1170452)
Kaynakça
- Ágostonak, K. (2015). Fault detection of the electrical motors based on vibration analysis. Procedia technology, 19, 547-553.
- Benbouzid, M. E. H. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, 47(5), 984-993. doi:10.1109/41.873206
- Deng, W., Yao, R., Zhao, H., Yang, X., & Li, G. (2019). A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Comput., 23(7), 2445–2462. doi:10.1007/s00500-017-2940-9
- Feng-qi, W., & Meng, G. (2006). Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM. Mechanical Systems and Signal Processing, 20, 2007-2021.
- Glowacz, A. (2019). Fault diagnosis of single-phase induction motor based on acoustic signals. Mechanical Systems and Signal Processing, 117, 65-80. doi:https://doi.org/10.1016/j.ymssp.2018.07.044
- Han, T., Jiang, D., Zhao, Q., Wang, L., & Yin, K. (2018). Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control, 40(8), 2681-2693. doi:10.1177/0142331217708242
- Jimenez, J. J. M., Schwartz, S., Vingerhoeds, R., Grabot, B., & Salaün, M. (2020). Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics. Journal of Manufacturing Systems, 56, 539-557.
- Kumar, P., & Hati, A. S. (2020). Review on Machine Learning Algorithm Based Fault Detection in Induction Motors. Archives of Computational Methods in Engineering. doi:10.1007/s11831-020-09446-w
- Liu, Z., Guo, W., Hu, J., & Ma, W. (2017). A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM. ISA Transactions, 66, 249-261. doi:https://doi.org/10.1016/j.isatra.2016.11.001
- Moosavian, A., Ahmadi, H., Sakhaei, B., & Labbafi, R. (2014). Support vector machine and K-nearest neighbour for unbalanced fault detection. Journal of Quality in Maintenance Engineering, 20(1), 65-75. doi:10.1108/JQME-04-2012-0016
- Rodriguez, P. J., Belahcen, A., & Arkkio, A. (2006). Signatures of electrical faults in the force distribution and vibration pattern of induction motors. IEE Proceedings - Electric Power Applications, 153(4), 523-529. doi:10.1049/ip-epa:20050253
- Saravanan, N., & Ramachandran, K. I. (2009). Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification. Expert Systems with Applications, 36(5), 9564-9573. doi:https://doi.org/10.1016/j.eswa.2008.07.089
- Saunders, C., Gammerman, A., & Vovk, V. (1998). Ridge Regression Learning Algorithm in Dual Variables. Paper presented at the Proceedings of the Fifteenth International Conference on Machine Learning.
- Sullivan, G., Pugh, R., Melendez, A. P., & Hunt, W. D. (2010). Operations & Maintenance Best Practices - A Guide to Achieving Operational Efficiency (Release 3) (PNNL-19634; Other: EL1703010; TRN: US201204%%75 United States 10.2172/1034595 Other: EL1703010; TRN: US201204%%75 PNNL English). Retrieved from https://www.osti.gov/servlets/purl/1034595
- Sun, W., Chen, J., & Li, J. (2007). Decision tree and PCA-based fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 21(3), 1300-1317. doi:https://doi.org/10.1016/j.ymssp.2006.06.010
- Wang, D. (2016). K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited. Mechanical Systems and Signal Processing, 70-71, 201-208. doi:https://doi.org/10.1016/j.ymssp.2015.10.007
- You, L., Fan, W., Li, Z., Liang, Y., Fang, M., & Wang, J. (2019). A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis. Shock and Vibration, 2019, 1908485. doi:10.1155/2019/1908485
- Zhou, Z., Wen, C., & Yang, C. (2016). Fault Isolation Based on k-Nearest Neighbor Rule for Industrial Processes. IEEE Transactions on Industrial Electronics, 63(4), 2578-2586. doi:10.1109/TIE.2016.2520898
MAKİNE ÖĞRENMESİ YAKLAŞIMLARI İLE İNDÜKSİYON MOTORLARI İÇİN AKILLI HATA TESPİTİ VE SINIFLANDIRMADA DENEYSEL BİR DEĞERLENDİRME
Öz
Endüstri 4.0 kavramı tamamen dijital ve otonom üretimi hedefliyor. İmalat sistemlerinin düzgün çalışması için bakımlarının doğru yapılması gerekir. Ancak, gereksiz bakım, para ve zaman israfına neden olurken, gerekli bakımın atlanması da üretimde beklenmedik duruş sürelerine neden olabilir. Kestirimci bakım (PdM), arızaları erken aşamada tahmin ve teşhis etmenin yanında geleneksel bakım yaklaşımlarına kıyasla önemli maliyet tasarrufu sağlayabilecek gelecekteki ekipman arızaları için kalan faydalı ömrü belirlemeyi de amaçlamaktadır. Günümüz sensör ve veri toplama teknolojileri daha erişilebilir ve güvenilir hale geldi, bu, üreticilerin ekipmanlarını sürekli olarak izlemelerine, üretim sistemlerinde büyük hacimli veri toplamalarına ve depolamalarına yol açtı. Bu verileri makine öğrenimi (ML) algoritmalarıyla kullanmak ve ekipman arızalarının yapılarını analiz etmek, üretim kalitesini ve kapasitesini artırmaya yardımcı olabilecek üretimde bakıma ilişkin daha bilinçli kararlar alınmasını sağlayabilir. Çalışmamızda fabrikalarda farklı amaçlarla yaygın olarak kullanılan indüksiyon motorları (IM) ve arıza senaryoları hedeflenmiştir. Farklı çalışma koşulları altında iki benzer indüksiyon motorundan toplanan üç eksenli titreşim verileri incelenmiştir. Titreşim verilerinin çeşitli öznitelikleri çıkarılarak, ölçeklenmiş ve çalışma durumuna ilişkin bir durum bilgisi ile etiketlenmiştir. Elde edilen veri seti altı farklı ML algoritması ile analiz edilmekte ve performansları birbirleriyle karşılaştırılmaktadır. Bu çalışmada, umut verici deneysel sonuçlarımızı sunuyoruz ve deneysel olarak, indüksiyon motorlarında anormal çalışma koşullarının Kestirimci Bakım uygulaması için makine öğrenimi algoritmaları kullanılarak başarıyla tespit edilebileceğini gösteriyoruz.
Anahtar Kelimeler
Proje Numarası
Project No: 118C252 and also Project No: 1170452
Kaynakça
- Ágostonak, K. (2015). Fault detection of the electrical motors based on vibration analysis. Procedia technology, 19, 547-553.
- Benbouzid, M. E. H. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, 47(5), 984-993. doi:10.1109/41.873206
- Deng, W., Yao, R., Zhao, H., Yang, X., & Li, G. (2019). A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Comput., 23(7), 2445–2462. doi:10.1007/s00500-017-2940-9
- Feng-qi, W., & Meng, G. (2006). Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM. Mechanical Systems and Signal Processing, 20, 2007-2021.
- Glowacz, A. (2019). Fault diagnosis of single-phase induction motor based on acoustic signals. Mechanical Systems and Signal Processing, 117, 65-80. doi:https://doi.org/10.1016/j.ymssp.2018.07.044
- Han, T., Jiang, D., Zhao, Q., Wang, L., & Yin, K. (2018). Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control, 40(8), 2681-2693. doi:10.1177/0142331217708242
- Jimenez, J. J. M., Schwartz, S., Vingerhoeds, R., Grabot, B., & Salaün, M. (2020). Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics. Journal of Manufacturing Systems, 56, 539-557.
- Kumar, P., & Hati, A. S. (2020). Review on Machine Learning Algorithm Based Fault Detection in Induction Motors. Archives of Computational Methods in Engineering. doi:10.1007/s11831-020-09446-w
- Liu, Z., Guo, W., Hu, J., & Ma, W. (2017). A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM. ISA Transactions, 66, 249-261. doi:https://doi.org/10.1016/j.isatra.2016.11.001
- Moosavian, A., Ahmadi, H., Sakhaei, B., & Labbafi, R. (2014). Support vector machine and K-nearest neighbour for unbalanced fault detection. Journal of Quality in Maintenance Engineering, 20(1), 65-75. doi:10.1108/JQME-04-2012-0016
- Rodriguez, P. J., Belahcen, A., & Arkkio, A. (2006). Signatures of electrical faults in the force distribution and vibration pattern of induction motors. IEE Proceedings - Electric Power Applications, 153(4), 523-529. doi:10.1049/ip-epa:20050253
- Saravanan, N., & Ramachandran, K. I. (2009). Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification. Expert Systems with Applications, 36(5), 9564-9573. doi:https://doi.org/10.1016/j.eswa.2008.07.089
- Saunders, C., Gammerman, A., & Vovk, V. (1998). Ridge Regression Learning Algorithm in Dual Variables. Paper presented at the Proceedings of the Fifteenth International Conference on Machine Learning.
- Sullivan, G., Pugh, R., Melendez, A. P., & Hunt, W. D. (2010). Operations & Maintenance Best Practices - A Guide to Achieving Operational Efficiency (Release 3) (PNNL-19634; Other: EL1703010; TRN: US201204%%75 United States 10.2172/1034595 Other: EL1703010; TRN: US201204%%75 PNNL English). Retrieved from https://www.osti.gov/servlets/purl/1034595
- Sun, W., Chen, J., & Li, J. (2007). Decision tree and PCA-based fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 21(3), 1300-1317. doi:https://doi.org/10.1016/j.ymssp.2006.06.010
- Wang, D. (2016). K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited. Mechanical Systems and Signal Processing, 70-71, 201-208. doi:https://doi.org/10.1016/j.ymssp.2015.10.007
- You, L., Fan, W., Li, Z., Liang, Y., Fang, M., & Wang, J. (2019). A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis. Shock and Vibration, 2019, 1908485. doi:10.1155/2019/1908485
- Zhou, Z., Wen, C., & Yang, C. (2016). Fault Isolation Based on k-Nearest Neighbor Rule for Industrial Processes. IEEE Transactions on Industrial Electronics, 63(4), 2578-2586. doi:10.1109/TIE.2016.2520898
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Bilgisayar Yazılımı |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Proje Numarası | Project No: 118C252 and also Project No: 1170452 |
| Yayımlanma Tarihi | 31 Ağustos 2021 |
| Kabul Tarihi | 29 Nisan 2021 |
| Yayımlandığı Sayı | Yıl 2021 |