An Overview of Useful Data and Analyzing Techniques for Improved Multivariate Diagnostics and Prognostics in Condition-Based Maintenance



Published Oct 3, 2016
Carolin Wagner Philipp Saalmann Bernd Hellingrath


The reliability of production machines gains in importance in today’s optimized and highly productive business environments. Unexpected machine breakdowns do not only lead to loss of production time and production outages but also to diminishing customer satisfaction due to deterioration in quality and declining availability of products. The condition-based maintenance (CBM) strategy aims at preventing these machine breakdowns through real-time monitoring of machine conditions. Sensor data are collected and analyzed using diagnostic and prognostic approaches to identify the type of fault and the remaining useful life. Identifying the reasons and time of breakdowns fosters improved planning of maintenance and spare parts demand, leading to higher machine reliability. In general, machine sensor data are regarded as a useful source of information to assess the machine’s operating condition. However, in some specific cases, the machine sensors lack the ability to correctly represent the health of the machine or the specific component under consideration. Therefore, additional information by further available data is required to improve diagnostic and prognostic techniques for more accurate and precise analysis. Current research focuses on the analysis of sensor data for condition-based maintenance, while other data like the operating history and environment temperature have only been considered to a limited extend so far. Hence, this paper gives an overview on potential data for machine health assessment and remaining useful life prediction in condition-based maintenance. Furthermore, corresponding approaches and techniques for fault diagnostics and prognostics are presented targeting the analysis of individual data sources as well as of multivariate settings featuring multiple integrated data sources.

How to Cite

Wagner, C., Saalmann, P., & Hellingrath, B. (2016). An Overview of Useful Data and Analyzing Techniques for Improved Multivariate Diagnostics and Prognostics in Condition-Based Maintenance. Annual Conference of the PHM Society, 8(1).
Abstract 257 | PDF Downloads 196



Condition Based Maintenance, Multivariate analysis, Diagnostics & Prognostics Methods, Data sources

Angeli, C. (2010). Diagnostic expert systems: from expert’s knowledge to real-time systems. In P. S. Sjja & R. Akerkar (Eds.), Advanced knowledge based systems: Model, Applications & Research (Vol. 1, pp. 50-73).
Bengtsson, M., Olsson, E., Funk, P., & Jackson, M. (2004). Design of condition based maintenance system - A case study using sound analysis and case-based reasoning. Condition Based Maintenance Systems - An Investigation of Technical Constituents and Organizational Aspects. Malardalen University, Eskilstuna, Sweden, 57.
Bonissone, P. P., & Varma, A. (2005). Predicting the best units within a fleet: prognostic capabilities enabled by peer learning, fuzzy similarity, and evolutionary design process. Fuzzy Systems, 2005. FUZZ'05. The 14th IEEE International Conference on (pp. 312-318), May 25-25. IEEE.
Cecati, C. (2015). A Survey of Fault Diagnosis and Fault-Tolerant Techniques - Part II: Fault Diagnosis with Knowledge-Based and Hybrid/Active Approaches. IEEE Transactions On In Transactions Electronics.
Dai, X., & Gao, Z. (2013). From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis. Industrial Informatics, IEEE Transactions on, 9(4), 2226-2238.
Ghodrati, B. (2005). Reliability and operating environment based spare parts planning. Doctoral dissertation. University of Technology, Lulea, Sweden.
Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6-23.
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20.7, 1483-1510.
Li, Q., Gao, Z. B., & Shao, L. Q. (2014). An operating condition classified prognostics approach for Remaining Useful Life estimation. Prognostics and Health Management (PHM), 2014. IEEE Conference on (pp. 1-9), June 22-25. IEEE.
Liao, L., & Lee, J. (2009). A novel method for machine performance degradation assessment based on fixed cycle features test. Journal of Sound and Vibration, 326(3), 894-908.
Liu, J., Djurdjanovic, D., Ni, J., Casoetto, N., & Lee, J. (2007). Similarity based method for manufacturing process performance prediction and diagnosis. Computers in industry, 58(6), 558-566.
Medina-Oliva, G., Voisin, A., Monnin, M., Peysson, F., Léger, J.-B. (2012). Prognostics assessment using fleet-wide ontology. Annual Conference of the Prognostics and Health Management Society 2012, PHM 2012, September, p. CDROM.
Mobley, R. Keith (2002). An introduction to predictive maintenance. Butterworth-Heinemann.
Monnin, M., Abichou, B., Voisin, A., & Mozzati, C. (2011). Fleet historical cases for predictive maintenance. The International Conference Surveillance, 6, 25-26.
Moss, T. R. (1991). Uncertainties in reliability statistics. Reliability Engineering & System Safety, 34(1), 79-90.
Pecht, M., & Jaai, R. (2010). A prognostics and health management roadmap for information and electronics - rich systems. Microelectronics Reliability, 50.3, 317-323.
Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: a review. The International Journal of Advanced Manufacturing Technology, 50.1-4, 297-313.
Rodríguez, G. (2007). Lecture Notes on Generalized Linear Models. URL:
Saxena, A., Sankararaman, S., & Goebel, K. (2014). Performance evaluation for fleet-based and unit-based prognostic methods. Second European conference of the Prognostics and Health Management society.
Si, X. S., Wang, W., Hu, C. H., & Zhou, D. H. (2011). Remaining useful life estimation - a review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1-14.
Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803-1836.
Tsang, A. H., Yeung, W. K., Jardine, A. K., & Leung, B. P. (2006). Data management for CBM optimization. Journal of Quality in Maintenance Engineering, 12(1), 37-51.
Turrin, S., Subbiah, S., Leone, G., & Cristaldi, L. (2015). An algorithm for data-driven prognostics based on statistical analysis of condition monitoring data on a flee level. Instrumentation and Measurement Technology Conference (I2MTC), 2015. IEEE International (pp. 629-634), May 11-14. IEEE.
Voisin, A., Medina-Oliva, G., Monnin, M., Leger, J. B., & Iung, B. (2013). Fleet-wide diagnostic and prognostic assessment. Annual Conference of the Prognostics and Health Management Society, October, p. CDROM.
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008). A similarity-based prognostics approach for remaining useful life estimation of engineered systems. Prognostics and Health Management (PHM), 2008. International Conference (pp. 1-6), October 06-09. IEEE.
Technical Research Papers