Schmidt, StephanHeyns, P.S. (Philippus Stephanus)2019-07-112019-04Schmidt, S. & Heyns, P.S. 2019, 'Localised gear anomaly detection without historical data for reference density estimation', Mechanical Systems and Signal Processing, vol. 121, pp. 615-635.0888-3270 (print)1096-1216 (online)10.1016/j.ymssp.2018.11.051http://hdl.handle.net/2263/70683Performing condition monitoring on rotating machines which operate under fluctuating conditions remains a challenge, with robust diagnostic techniques being required for the condition inference task. Some of the developed diagnostic techniques rely on the availability of historical fault data, which is impractical or even impossible to obtain in many circumstances and therefore novelty detection techniques such as discrepancy analysis are used. However, discrepancy analysis assumes that the condition of the machine is the same throughout the signal in the model optimisation process i.e. no localised damage is present, which can pose problems if the training data unwittingly contain a component with localised damage. In this paper, an automatic procedure is proposed for diagnosing localised gear damage in the presence of fluctuating operating conditions, with no historical data being required to model the data used in the condition inference process. The continuous wavelet transform, principal component analysis and information theory are used to obtain divergence data of the gear under consideration. The divergence data are used with Bayesian data analysis techniques to automatically infer the presence of localised anomalies due to localised gear damage. The proposed technique is validated in two experimental investigations, with promising results being obtained.en© 2018 Elsevier Ltd. Notice : this is the author’s version of a work that was accepted for publication in Mechanical Systems and Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Mechanical Systems and Signal Processing, vol. 121, pp. 615-635, 2019, doi : 10.1016/j.ymssp.2018.11.051.BayesianKullback-Leibler divergenceFluctuating operating conditionsDiagnosticsEngineering, built environment and information technology articles SDG-04SDG-04: Quality educationEngineering, built environment and information technology articles SDG-08SDG-08: Decent work and economic growthEngineering, built environment and information technology articles SDG-09SDG-09: Industry, innovation and infrastructureEngineering, built environment and information technology articles SDG-12SDG-12: Responsible consumption and productionPostprint Article