An improved OAKR approach to condition monitoring of rotating machinery



Published Sep 4, 2023
Kexin Zhang Xiaomo Jiang


Faults in main subsystems or components of a rotating machine often causes unscheduled shutdown, which may lead to not only huge economic losses, but also safety accidents. As an important part of intelligent maintenance, condition monitoring becomes a powerful tool in reducing maintenance costs through automatic fault alarming, thereby reducing potential downtime while improving system safety and reliability. An optimized auto-associative kernel
regression (OAKR) model has been proposed recently and demonstrated as a promising tool for condition monitoring of various turbomachines, which is independent of fault mode and machine type. However, the fault identification accuracy of this approach largely relies on data quality in practical applications. Data incompleteness, parameter variation and system complexity often result in the inaccuracy of fault alarming for complicated rotating machinery. This paper proposes an improved OAKR method to address these issues, including utilizing wavelet packet Bayesian thresholding method (WPB) to reduce noise in the raw multivariate data, developing the Manhattan distance to calculate the sample similarity, and constructing a multivariate health index based on Multivariate Permutation Entropy to identify potential faults in equipment condition monitoring. Parametric analysis and a comparison study with original AAKR and OAKR methods by using the actual data of a gas turbine are conducted to illustrate the effectiveness and feasibility of the proposed methodology.

Abstract 164 | PDF Downloads 153



Bayesian thresholding, wavelet packet, OAKR, condition monitoring, rotating machine

Baraldi, P., Di Maio, F., Turati, P., & Zio, E. (2015). Robust signal reconstruction for condition monitoring of industrial components via a modified Auto Associative Kernel Regression method. Mechanical Systems and Signal Processing, 60, 29-44.

Guo, P., & Bai, N. (2011). Wind turbine gearbox condition monitoring with AAKR and moving window statistic methods. Energies, 4(11), 2077-2093.

Jiang, X., Mahadevan, S., & Adeli, H. (2007). Bayesian wavelet packet denoising for structural system identification. Structural Control and Health Monitoring: The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures, 14(2), 333-356.

Jiang, X., & Mahadevan, S. (2008). Bayesian wavelet methodology for structural damage detection. Structural Control and Health Monitoring: The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures, 15(7), 974-991.

Kämpjärvi, P., Sourander, M., Komulainen, T., Vatanski, N., Nikus, M., & Jämsä-Jounela, S. L. (2008). Fault detection and isolation of an on-line analyzer for an ethylene cracking process. Control engineering practice, 16(1), 1-13.

Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008, December). Isolation forest. In 2008 eighth ieee international conference on data mining (pp. 413-422). IEEE.

Lee, S., & Mortari, D. (2017). Quasi-equal area subdivision algorithm for uniform points on a sphere with application to any geographical data distribution. Computers & Geosciences, 103, 142-151.

Melter, R. A. (1987). Some characterizations of city block distance. Pattern recognition letters, 6(4), 235-240.

Morabito, F. C., Labate, D., Foresta, F. L., Bramanti, A., Morabito, G., & Palamara, I. (2012). Multivariate multiscale permutation entropy for complexity analysis of Alzheimer’s disease EEG. Entropy, 14(7), 1186-1202.

Qian, F., Feng, Y., & Ling, J. (2018, June). Condition monitoring of turbine generator using stator winding temperature. In 2018 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1- 5). IEEE.

Wang, L., Zhang, Y., & Feng, J. (2005). On the Euclidean distance of images. IEEE transactions on pattern analysis and machine intelligence, 27(8), 1334-1339.

Yu, J., Jang, J., Yoo, J., Park, J. H., & Kim, S. (2017). Bagged auto-associative kernel regression-based fault detection and identification approach for steam boilers in thermal power plants. Journal of Electrical Engineering and Technology, 12(4), 1406-1416.

Zhang, G., & Gai, Y. (2020). A study on the application of local outlier factor algorithm (LOF) in anomaly detection. Network Security Technology & Application, (11), 49-50.
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