Data quality and reliability: a cornerstone for PHM processes
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Abstract
In most of industrial processes, the measurement are central to the process control and quality management. This become even truer when measurement data are used to develop and support PHM strategies. In this context, many software are installed in order to collect data for providing quality assessment at each step of the manufacturing process. However, measurement error or drift are not considered leading to downgrading / rejected products / suboptimal running conditions that comes from measurement drift not detected on time. In concrete, these lead to bigger penalty than losses of production due to stopping time for repairing sensors. Indeed, generally speaking, process data is the “raw material” for many business processes, starting from process control strategy, PHM strategies to Business Intelligence. Thus being able to ensure data quality and reliability is of first importance. Towards this end, methods and tools are required to support online measurement monitoring, predictive diagnosis and reliability enhancement.
In this paper, a dedicated approach developed in collaboration with ArcelorMittal Research is presented. It consists in the development of intelligent software that would enable sensor measurement validation taking into account process parameters and operational conditions. An illustrative case study is extracted from an ongoing application developed for the finishing line in ArcelorMittal plant at Florange in France. Results regarding measurement reliability assessment as well as sensor failure anticipation will be described.
How to Cite
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diagnostics and prognostics, monitoring, Data Quality in industry
Léger J-B. (2004). A case study of remote diagnosis and e-maintenance information system, Keynote speech of IMS’2004, International Conference on Intelligent Maintenance Systems, Arles, France.
Miletic, I., Boudreau, F., Dudzic, M., Kotuza, G., Ronholm, L., Vaculik, V., & Zhang, Y. (2008). Experiences in applying data‐driven modelling technology to steelmaking processes. The Canadian Journal of Chemical Engineering, 86(5), 937-946.
Monnin, M., Voisin, A., Leger, J. B., & Iung, B. (2011a). Fleet-wide health management architecture. In Annual Conference of the Prognostics and Health Management Society. September, Montreal, Quebec, Canada.
Monnin, M, Leger, J-B., Morel, D. (2011b). KASEM®: e-Maintenance SOA Platform, in Proceedings of 24th International Congress on Condition Monitoring and Diagnostics Engineering Management, 29th May – 1st June, Stavanger, Norway.
Reppa, V., Polycarpou, M. M., & Panayiotou, C. (2012, August). Distributed sensor fault detection and isolation for nonlinear uncertain systems. In Fault Detection, Supervision and Safety of Technical Processes (Vol. 8, No. 1, pp. 1077-1082).
Samy, I., Postlethwaite, I., & Gu, D. W. (2011). Survey and application of sensor fault detection and isolation schemes. Control Engineering Practice, 19(7), 658-674.
Voisin, A., Medina-Oliva, G., Monnin, M., Leger, J. B., & Iung, B. (2013, October). Fleet-wide Diagnostic and Prognostic Assessment. In Annual Conference of the Prognostics and Health Management Society 2013.
Zhang, Y., Bingham, C., Gallimore, M., Yang, Z., & Chen, J. (2013, July). Sensor fault detection and diagnosis based on SOMNNs for steady-state and transient operation. In Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2013 IEEE International Conference on (pp. 116-121). IEEE
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