Towards an adapted PHM approach: Data quality requirements methodology for fault detection applications

• Published in: Computers in industry 2021-05, Vol.127, p.103414, Article 103414
• Main Authors: Omri, N., Al Masry, Z., Mairot, N., Giampiccolo, S., Zerhouni, N.
• Format: Article
• Language: English
• Subjects: Data detectability; Data management; Data quality assessment; Data quality metrics; Data-driven PHM; Impact of data quality on PHM results
• ISSN: 0166-3615; 1872-6194
• DOI: 10.1016/j.compind.2021.103414

•The data quality problems are discussed in the Prognostics and Health Management (PHM) context.•The data suitability for faults detection tasks is formalized.•Based on this formalization, a novel metric to measure data detectability is developed empirically.•This metric is used to optimize the data acquisition cost while satisfying a fixed accuracy objective.•The proposed developments are applied in a real case study and the results are reported and discussed. Increasingly, extracting knowledge from data has become an important task in organizations for performance improvements. To accomplish this task, data-driven Prognostics and Health Management (PHM) is introduced as an asset performance management framework for data management and knowledge extraction. However, acquired data come generally with quality issues that affect the PHM process. In this context, data quality problems in the PHM context still an understudied domain. Indeed, the quality of the used data, their quantification, their improvement techniques and their adequacy to the desired PHM tasks are marginalized in the majority of studies. Moreover, many PHM applications are based on the development of very sophisticated data analysis algorithms without taking into account the adaptability of the used data to the fixed objectives. This paper aims to propose a set of data quality requirements for PHM applications and in particular for the fault detection task. The conducted developments in this study are applied to Scoder enterprise, which is a French SME. The feedback on the first results is reported and discussed.