A unified fault modelling approach for machines with varying operating speeds is of interest in automating production facilities. Building a unified fault model is challenging due to the presence of speed-specific attributes in the features derived. In this work, it is shown how feature selection, mapping, normalisation and fusion of heterogeneous systems can help enhance the performance of speed-independent (SI) machine-fault diagnosis systems. Statistical features, obtained after applying feature selection, are used as input to a support vector machine (SVM) back-end classifier as the baseline system. Entropy-based feature selection algorithm is proposed to improve the performance of the fault diagnosis system. Furthermore, to make the fault diagnosis system independent of speed, locality constrained linear coding (LLC), Fisher vector encoding (FVE) and mean and variance normalisation (MVN) are used. The LLC-MVN system and FVE-MVN system map the input features in terms of SI basis vectors to make the features robust to speed-specific variations. Finally, the decision scores of the time-domain LLC-MVN-SVM, frequency-domain LLC-MVN-SVM systems and variational mode decomposition-based FVE-MVN-SVM system were fused with appropriate weighting factors. The detection error trade-off curve is also used as a performance measure for intelligent fault diagnosis systems.
K. T. Sreekumar, George, K. K., C. Kumar, S., and Dr. K. I. Ramachandran, “Performance enhancement of the machine-fault diagnosis system using feature mapping, normalisation and decision fusion”, IET Science, Measurement Technology, vol. 13, pp. 1287-1298, 2019.