Abstract:
Historical fault data are often difficult and expensive to acquire, which can prohibit the application of supervised learning techniques in the condition-based maintenance field. Hence, novelty detection techniques such as discrepancy analysis are useful because only healthy historical data are required. However, even if discrepancy analysis is implemented on a machine, some historical fault data will become available during the operational lifetime of the machine and could be utilised to improve the efficiency of the condition inference process. An open set recognition methodology relying on discrepancy analysis is proposed that is capable of detecting novelties when only healthy historical data are available. It is also capable of inferring the condition of the machine if historical fault data are available and it is further able to detect novelties in regions not well supported by the historical fault data. In the condition recognition procedure, Gaussian mixture models are used with Bayes’ rule and a decision rule to infer the condition of the machine in an open set recognition framework, where it is emphasised that it is beneficial to use the complete datasets (i.e. data acquired throughout the life of the component) for optimising the models. The benefit of the open set recognition model is that it is easy to incorporate new historical data in the framework as the data become available. In this work, practical aspects of the condition inference process such as the importance of good decision boundaries are highlighted and discussed as well. The methodology is validated on a synthetic dataset and experimental datasets acquired under varying operating conditions and it is also compared to a conventional classification process where discrepancy analysis is not used. The results indicate the potential of using the proposed methodology for rotating machine diagnostics under varying operating conditions.
