A Short Review on the Integration of Expert Knowledge in Prognostics for PHM in Industrial Applications

Optimizing maintenance is essential for industrials to stay competitive, and the development of appropriate predictive maintenance is necessary to achieve this objective. To this extent, the Prognostics and Health Management (PHM) paradigm is well established. One of the key steps of PHM is the prognostics of health states of the system. Various state-of-the-art approaches exist for prognostics, with an emerging orientation towards data-driven methods. Indeed, they have lot of potential for Industry 4.0 applications with high amount of data from sensors and control equipment. However, labelled data (i.e., failures of systems) is not always available on real-life applications where preventive maintenance is often already applied. Thus, the learning databases can be unbalanced, with few learning examples, consequently reducing the learning capacities of algorithms, as well as their generalization. One way to optimize learning on such applications is then to use Expert Knowledge, which can provide additional information on the system and its operating model. A challenging issue is herein the development of a general methodology to integrate the Expert Knowledge into data-driven methods.To face this challenge, this paper aims to propose a categorization of Expert Knowledge based on existing works to identify adapted methods that can help to integrate efficiently the available Knowledge into relevant prognostics algorithms. The proposed categorization will allow and facilitate the review and comparison of approaches and methodologies introduced in the literature and in further research. Finally, the proposed classification will be illustrated on a real case of prognostics for a hydraulic circuit from an ArcelorMittal plant.