Computational Analysis of Sparse Datasets for Fault Diagnosis in Large Tribological Mechanisms

This paper presents the most up-to-date methods for the task of designing a system to accurately classify abnormal events, or faults, in a complex tribological mechanism, using elemental analysis of lubrication oil as an indicator of engine condition. The discussion combines perspectives from numerous fault diagnosis applications, both online and offline, to focus upon the task of offline event detection and diagnosis of datasets from elemental analysis, and although this does not suffer from complexity issues as in real-time processing, it introduces a number of other problems such as sparsity and selecting an accurate knowledge representation as well as reasoning under uncertainty and ignorance. The role of confounding variables is significant in sparse datasets, and as such this paper demonstrates an alternative perspective on both eliminating to an extent the effect of confounding variables and inferring unseen variables from measured variables. There has been little review work on this subject, and as a result this paper helps to join disparate research from a number of different domains to achieve some unification of alternative perspectives. This paper concludes by providing a case study to identify the methods that can be utilized in combination.