Data quality and reliability: a cornerstone for PHM processes



Published Jul 8, 2014
Jean-Baptiste Leger Pierre-Jean Krauth Guillaume Groussier Maxime Monnin Alain Mouchette Fayçal Lawayeb


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

Leger, J.-B., Krauth, P.-J., Groussier, G., Monnin, M., Mouchette, A., & Lawayeb, F. (2014). Data quality and reliability: a cornerstone for PHM processes. PHM Society European Conference, 2(1).
Abstract 1505 | PDF Downloads 166



diagnostics and prognostics, monitoring, Data Quality in industry

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