The week's pick
Reseach Article
A Machine Learning Approach for Temporal Vibration Analysis Applied to Predictive Maintenance
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 12 |
| Year of Publication: 2020 |
| Authors: Roberto Alexandre Dias, Pedro Von Hertwig, Mário De Noronha Neto |
10.5120/ijca2020920625
|
Roberto Alexandre Dias, Pedro Von Hertwig, Mário De Noronha Neto . A Machine Learning Approach for Temporal Vibration Analysis Applied to Predictive Maintenance. International Journal of Computer Applications. 175, 12 ( Aug 2020), 38-42. DOI=10.5120/ijca2020920625
Abstract
The operational condition of a machine affects the quality and efficiency of its work, and letting a problem arrive in a critical state results in negative consequences, which can cause equipment loss and extensive downtime in a factory. The maintenance of machines, therefore, is a concern that came to exist along with the creation of the industry. This work shows the development of a platform that takes advantage of recent advances in sensor technology and machine learning to assist the predictive maintenance process, identifying problems in advance before serious failures can occur. The work proposes a supervisory system which receives high frequency vibration data, stores it, and analyzes the functioning of a machine to classify its behavior as normal or anomalous, generating alerts. The results achieved show that it is appropriate to use machine learning to monitor machines, since well-structured algorithms can detect possible problems before they become apparent to humans.
References
- Marcorin, W. R., & Lima, C. R. (2003). Análise dos Custos de Manutenção e de Não-manutenção de Equipamentos Produtivos. Revista de Ciência & Tecnologica, 11(22), 35-42.
- Lebold, M., McClintic, K., Campbell, R., Byington, C., & Maynard, K. (2000). Review of Vibration Analysis Methods for Gearbox Diagnostics and Prognostics. 54th Meeting of the Society for Machinery Failure Prevention Technology, (pp. 623-634). Virginia Beach.
- Otani, M., & Machado, W. V. (2008). A proposta de desenvolmento de gestão da manutençãoindustrial na busca da excelência ou classe mundial. Revista Gestão Industrial, 4(2), 1-16.
- Pandiyan, J., Umapathy, M., Balachandar, S., Arumugam, M., Ramasamy, S., & Gajjar, N. C. (2006). Design of Industrial Vibration Transmitter Using MEMS Accelerometer. International MEMS Conference (pp. 442–447). Gujarat: Institute of Physics Publishing.
- Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing(23), 724-739.
- Paudyal, S., Atique, M. S., & Yang, C. X. (2019). Local Maximum Acceleration Based Rotating Machinery Fault Classification Using KNN. IEEE EIT 2019. Grand Forks.
- Cubillo, A., Perinpanayagam, S., & Esperon-Miguez, M. (2016). A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery. Advances in Mechanical Engineering, 8(8), 1-21.
- Stringer, D. B., Sheth, P. N., & Allaire, P. E. (2012). Physics-based modeling strategies for diagnostic and prognostic application in aerospace systems. Journal of Intelligent Manufacturing(23), 155-162.
- Jia, X., Huang, B., Feng, J., Cai, H., & Lee, J. (2018). Review of PHM Data Competitions from 2008 to 2017: Methodologies and Analytics. Annual Conference of the Prognostics and Health Management Society, (pp. 1-10). Cincinnati.
- Ahmad, R., & Kamaruddin, S. (2012). An overview of time-based and condition-based maintenancein industrial application. Computers & Industrial Engineering(63), 135-149.
- Labib, A. W. (2004). A decision analysis model for maintenance policy selection using a CMMS. Journal of Quality in Maintenance Engineering, 10(3), 191-202.
- Qiu, H., Lee, J., Lin, J., & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration(289), 1066-1090. Acesso em 2019, disponível em https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
- Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2014). Outlier Detection for Temporal Data: A Survey. IEEE Transactions on knowledge and data engineering, 26(9), 2250-2267.
- Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Wozniak, M. (2017). Ensemble learning for data stream analysis: A survey. Information Fusion(37), 132-156.
- Zhao, Y., Nasrullah, Z., Hryniewicki, M. K., & Li, Z. (2019). LSCP: Locally Selective Combination in Parallel Outlier Ensembles. SIAM International Conference on Data Mining. Calgary: Society for Industrial and Applied Mathematics.
- Ramaswamy, S., Rastogi, R., & Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. ACM SIGMOD international conference on Management of data (pp. 427-438). Dallas: ACM New York.
- Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD International Conference on Management of Data. 2, pp. 93-104. Dallas: ACM New York.
- Shyu, M.-L., Chen, S.-C., Sarinnapakorn, K., & Chang, L. (2003). A Novel Anomaly Detection Scheme Based on Principal Component Classifier. IEEE Foundations and New Directions of Data Mining Workshop, (pp. 172-179).
Index Terms
Keywords