Fault Log Recovery Using an Incomplete-data-trained FDA Classifier for Failure Diagnosis of Engineered Systems
In the 2015 PHM Data Challenge Competition, the goal of the competition problem was to diagnose failure of industrial plant systems using incomplete data. The available data consisted of sensor measurements, control reference signals, and fault logs. A detailed description of the plant system of interest was not revealed, and partial fault logs were eliminated from the dataset. This paper presents a fault log recovery method using a machine-learning-based fault classification approach for failure diagnosis. For optimal performance, it was critical to be able to utilize a set of incomplete data and to select relevant features. First, physical interpretation of the given data was performed to select proper features for a fault classifier. Second, Fisher discriminant analysis (FDA) was employed to minimize the effect of outliers in the incomplete data sets. Finally, the type of the missing fault logs and the duration of the corresponding faults were recovered. The proposed approach, based on the use of an incomplete-data-trained FDA classifier, led to the second-highest score in the 2015 PHM Data Challenge Competition.
data-driven, Diagnostics & Prognostics Methods, incomplete data, fault classification
He, X., Wang, Z., & Zhou, D. H. (2009). Robust fault detection for networked systems with communication delay and data missing. Automatica, 45(11), 2634-2639.
Hu, C., Youn, B. D., Wang, P., & Yoon, J. T. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering and System Safety, 103, 120-135.
Jeon, B. C., Jung, J. H., Youn, B. D., Kim, Y.-W., & Bae, Y.-C. (2015). Datum unit optimization for robustness of a journal bearing diagnosis system. International Journal of Precision Engineering and Manufacturing, 16(11), 2411-2425.
Kim, H., Hwang, T., Park, J., Oh, H., & Youn, B. D. (2014). Risk prediction of engineering assets: An ensemble of part lifespan calculation and usage classification methods. International Journal of Prognostics and Health Management, 5(2)
Lee, C., Choi, S. W., Lee, J. M., & Lee, I. B. (2004). Sensor fault identification in MSPM using reconstructed monitoring statistics. Industrial & Engineering Chemistry Research, 43(15), 4293-4304.
Li, J. R., Khoo, L. P., & Tor, S. B. (2006). RMINE: a rough set based data mining prototype for the reasoning of incomplete data in condition-based fault diagnosis. Journal of Intelligent Manufacturing, 17(1), 163-176.
Marwala, T., & Chakraverty, S. (2006). Fault classification in structures with incomplete measured data using autoassociative neural networks and genetic algorithm. Current Science-Bangalore, 90(4), 542.
Negnevitsky, M. & Pavlovsky, V. (2005). Neural networks approach to online identification of multiple failures of protection systems. IEEE Transactions on Power Delivery, 20(2), 588-594.
Oh, H., Han, B., McCluskey, P., Han, C., & Youn, B. D. (2015). Physics-of-failure, condition monitoring, and prognostics of insulated gate bipolar transistor modules: A review. IEEE Transactions on Power Electronics, 30(5), 2413-2426.
Razavi-Far, R., Zio, E., & Palade, V. (2014). Efficient residuals pre-processing for diagnosing multi-class faults in a doubly fed induction generator, under missing data scenarios. Expert Systems with Applications, 41(14), 6386-6399.
Rosca, J., Song Z., Willard, N., & Eklund, N. (2015). PHM15 challenge competition and data set: fault prognostics, NASA Ames Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA. http://ti.arc.nasa.gov/project/prognostic-data-repository
Salsbury, T. I., & Diamond, R. C. (2001). Fault detection in HVAC systems using model-based feedforward control. Energy and Buildings, 33(4), 403-415.
Schein, J., Bushby, S. T., Castro, N. S., & House, J. M. (2006). A rule-based fault detection method for air handling units. Energy and Buildings, 38(12), 1485-1492.
Vandawaker, R. M., Jacques, D. R., & Freels, J. K. (2015). Impact of prognostic uncertainty in system health monitoring. International Journal of Prognostics and Health Management, 6(2).
Wang, P., Wang, Z., Youn, B. D., & Lee, S. (2015). Reliability-based robust design of smart sensing systems for failure diagnostics using piezoelectric materials. Computers & Structures, 156, 110-121.
Wu, Y., Jiang, B., Lu, N. Y., & Zhou, Y. (2015). Bayesian network based fault prognosis via Bond graph modeling of high-speed railway traction device. Mathematical Problems in Engineering, 2015.
Yang, C., Zou, Y., Liu, J., & Mulligan, K. R. (2014). Predictive model evaluation for PHM. International Journal of Prognostics and Health Management, 5(2).
Yongli, Z., Limin, H., & Jinling, L. (2006). Bayesian networks-based approach for power systems fault diagnosis. IEEE Transactions on Power Delivery, 21(2), 634-639.