Abstract
A fundamental challenge in artificial intelligence is to predict the system’s state by detecting anomalies generated due to the faults in the systems. Sound data that deviates significantly from the default sounds generated by the system is referred to as anomalous sounds. Predicting anomalous sounds has gained importance in various applications as it helps in maintaining and monitoring machine conditions. The goal of anomaly detection involves training the system to distinguish default sounds from abnormal sounds. As self-supervised learning helps in improvising representations when labeled data are used, it is employed where only the normal sounds are collected and used. The largest interval on the feature space defines the support vector machine, which is a linear classifier. We propose a self-supervised support vector machine (SVM) to develop a health prediction model that helps understand the current status of the machinery activities and their maintenance, enhancing the system’s health accuracy and efficiency. This work uses a subset of MIMII and ToyADMOS datasets. The implemented system would be tested for the performance measure by obtaining the training accuracy, validation accuracy, testing accuracy, and overall mean accuracy. The proposed work would benefit from faster prediction and better accuracy.
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Pai, M., Patil, A.P. (2022). Predicting the Health of the System Based on the Sounds. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-16-9416-5_36
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DOI: https://doi.org/10.1007/978-981-16-9416-5_36
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