This paper presents an effective health assessment and predictive maintenance technique for industrial assets. The technique and algorithms applied to data sets provided by the Prognostics and Health Management Society 2014 Data Challenge. The data contains usage and part consumption for three years. In short, the usage data contains a parameter that roughly measures asset usage, and the part consumption data includes information regarding part replacement and maintenance actions. The first two years of data are considered as "Training" data and the third year is considered as "Testing" data. The proposed method built on the probability of the failure risk during training dataset. The main objective is to develop a model based on first two years data set (training) and determine the high risk and low risk times of failure for each individual asset for the third year.
Training data shows many maintenance activities with 14 different codes. The principle difficulty is to detect the Preventive Maintenance (PM) in the training data. The paper presents the method in three main steps: the first step is to recognize the PM pattern based on time and type of maintenance activity via the training data. The second step is to determine the high-risk time intervals based on PM times by checking the frequency of the failures at specific times between each PM. The third step is to predict the high risk time intervals in the testing data using the information acquired from the training data. The score predicted by this probabilistic risk assessment method won the first place in the PHM Data Challenge Competition.
preventive maintenance, probabilistic risk assessment, corrective maintenance, hazard rate
Ebeling, C. (1997). An Introduction to Reliability and Maintainability Engineering. New York: McGraw-Hill.
El-Ferik, S., Ben-Daya, M. (2006). Age-based Hybrid Model for Imprefect Preventive Maintenance. IIE Transactions, 365–375.
Endrenyi, J. e. (2001). The Present Status of Maintenance Strategies and the Impact of Maintenance on Reliability. IEEE TRANSACTIONS ON POWER SYSTEMS, 638-646.
Garvey, D. (2014 August). From phm data challenge: http://www.phmsociety.org/events/conference/phm/14/data-challenge
Khan, F., Haddara, M. (2003). Risk-based maintenance (RBM): a quantitative approach for maintenance/inspection scheduling and planning. Journal of Loss Prevention in the Process Industries, 561-573.
Klutke, G.-A., Kiessler, P., Wortman, M. (2003). A Critical Look at the Bathtub Curve. IEEE TRANSACTIONS ON RELIABILITY, VOL. 52, NO. 1, 125-129.
Kotz, S., Seier, E. (2009). An analysis of quantile measures of kurtosis: cneter and tails. Statistical Papers, 553-568.
Lin, D. Ming, Y., Richard, M. (2001). Sequential Imperfect Preventive Maintenance Models with Two Categories of Failure Modes. Naval Research Logistics, 172-183.
Rezvanizaniani, S., Barabady, J., Valibeigloo, M., Asghari, M., Kumar, U. (2009). Reliability Analysis of the Rolling Stock Industry: A Case Study. International Journal of Performability Engineering, Vol. 5, No. 2, pp. 167-175.
Siegel, D. (2013). Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach. Doctoral Dissertaion. University of Cincinnati.
Siegel, D., Lee, J. (2011). An Auto-Associative Residual Processing and K-means Clustering Approach for Anemometer Health Assessment. International Journal Of Prognostics And Health Management, ISSN 2153-2648,.
Wang, K., Hsua, S., Liub, P. (2002). Modeling the bathtub shape hazard rate function in terms of reliability. Reliability Engineering & System Safety, 397–406.