Qualitative Assessment of Machine Learning Techniques in the Context of Fault Diagnostics

Nowadays, in the light of high data availability and computational power, Machine Learning (ML) techniques are widely applied to the area of fault diagnostics in the context of Condition-based Maintenance (CBM). Those techniques are able to learn intelligently from data to build suitable classification models, which enable the labeling of unknown data based on observed patterns. Even though plenty of research papers deal with this topic, the question remains open, which technique should be chosen for a specific problem. In order to select appropriate methods for a given problem, the problem characteristics have to be assessed against the strengths and weaknesses of relevant ML techniques. This paper presents a qualitative assessment of well-known ML techniques based on criteria obtained from literature. It is completed by a case study to identify the most suitable techniques to perform fault diagnostics in in-vitro diagnostic instruments with regard to the presented qualitative assessment.