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Sensors (Basel). 2018 Aug 11;18(8). pii: E2634. doi: 10.3390/s18082634.

A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals.

Author information

1
Department of IT Convergence and Application Engineering, Pukyong National University, Busan 48513, Korea. exen.xmen@gmail.com.
2
Department of Electronics Engineering, Pukyong National University, Busan 48513, Korea. ksmoon@pknu.ac.kr.
3
Department of Information Security, Tongmyong University, Busan 48520, Korea. skylee@tu.ac.kr.
4
Department of IT Convergence and Application Engineering, Pukyong National University, Busan 48513, Korea. krkwon@pknu.ac.kr.

Abstract

Machine fault diagnosis (MFD) has gained an important enthusiasm since the unfolding of the pattern recognition techniques in the last three decades. It refers to all of the studies that aim to automatically detect the faults on the machines using various kinds of signals that they can generate. The present work proposes a MFD system for the drilling machines that is based on the sounds they produce. The first key contribution of this paper is to present a system specifically designed for the drills, by attempting not only to detect the faulty drills but also to detect whether the sounds were generated during the active or the idling stage of the whole machinery system, in order to provide a complete remote control. The second key contribution of the work is to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. The created images, the so-called power spectrum density (PSD)-images, are then given to a deep convolutional autoencoder (DCAE) for a high-level feature extraction process. The final step of the scheme consists of adopting the proposed PSD-images + DCAE features as the final representation of the original sounds and utilize them as the inputs of a nonlinear classifier whose outputs will represent the final diagnosis decision. The results of the experiments demonstrate the high discrimination potential afforded by the proposed PSD-images + DCAE features. They were also tested on a noisy dataset and the results show their robustness against noises.

KEYWORDS:

artificial neural network; deep convolutional autoencoder; deep learning; machine fault diagnosis; machine learning; pattern recognition; smart factory; sound and acoustic processing

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