Abstract
The combination of machine learning techniques and signal analysis is a well-known solution for the fault diagnosis of industrial equipment. Efficient maintenance management, safer operation, and economic gains are three examples of benefits achieved by using this combination to monitor the equipment condition. In this context, the selection of meaningful information to train machine learning models arises as an important issue, since it influences the model accuracy and complexity. Aware of this, we propose to use the ratio between the interclass and intraclass Kullback-Leibler divergence to identify promising data for training fault diagnosis models. We assessed the performance of this metric on compressor fault datasets. The results suggested a relation between the model accuracy and the ratio between the average interclass and intraclass divergences.
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This study was financed in part by the Coordenaçǎo de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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Monteiro, R.P., Bastos-Filho, C.J.A. (2020). Using Kullback-Leibler Divergence to Identify Prominent Sensor Data for Fault Diagnosis. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_13
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DOI: https://doi.org/10.1007/978-3-030-62362-3_13
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