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

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study focuses on data quality issues in data-driven Prognostics and Health Management (PHM), proposing a set of data quality requirements for PHM applications, particularly for fault detection tasks. The developments in this study are applied to the French SME Scoder, with feedback and discussion on the initial results.