Abstract
Numerous techniques have been proposed for detection and localization of fault sources in motorcycles based on acoustic signatures. However, these systems are inadequate to assess the severity of faults present in motorcycles. In this paper, a mechanism to estimate the degree of fault severity based on acoustic signals is presented. The process involves three stages: fault detection, fault source localization and fault severity analysis. The distribution of energies in first five subbands of a wavelet packet is considered as features set for signal processing and analysis. The model is tested against several classifiers including ANN, DTW, k-NN and k-Means. Essentially, same set of features and classifiers are employed at each stage of the process. The augmented decision vector output of the first and second stage are provided as inputs to the second and third stage, respectively. Three levels of fault severity are evaluated: high, medium and low. The classification accuracy up to 92% is observed for fault detection and localization stage, whereas, 90% accuracy is observed for severity analysis. The proposed work finds interesting applications in allied areas such as fault severity analysis of engines, machinery, musical instruments and electronic gadgets.
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Pagi, V.B., Wadawadagi, R.S., Anami, B.S. (2018). Severity Analysis of Motorcycle Faults Based on Acoustic Signals. In: Guru, D., Vasudev, T., Chethan, H., Kumar, Y. (eds) Proceedings of International Conference on Cognition and Recognition . Lecture Notes in Networks and Systems, vol 14. Springer, Singapore. https://doi.org/10.1007/978-981-10-5146-3_5
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DOI: https://doi.org/10.1007/978-981-10-5146-3_5
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