Improving spare part search for maintenance services using topic modelling

Abstract

To support the decision-making process in various industrial applications, many companies use knowledge management and Information Retrieval (IR). In an industrial setting, knowledge is extracted from data that is often stored in a semi-structured or unstructured format. As a result, Natural Language Processing (NLP) methods have been applied to a number of IR steps. In this work, we explore how NLP and particularly topic modelling can be used to improve the relevance of spare part retrieval in the context of maintenance services. A proposed methodology extracts topics from short maintenance service reports that also include part replacement data. An intuition behind the proposed methodology is that every topic should represent a specific root cause. Experimental were conducted for an ad-hoc retrieval system of service case descriptions and spare parts. The results have shown that our modification improves a baseline system thus boosting the performance of maintenance service solution recommendation