Department of IT Convergence and Application Engineering, Pukyong National University, Busan 48513, Korea.
Department of Electronics Engineering, Pukyong National University, Busan 48513, Korea.
Sensors (Basel). 2018 Aug 11;18(8):2634. doi: 10.3390/s18082634.
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.
机器故障诊断（MFD）自过去三十年中模式识别技术的发展以来，引起了广泛的关注。它指的是所有旨在使用机器生成的各种信号自动检测机器故障的研究。本工作提出了一种基于声音的钻床 MFD 系统。本文的第一个主要贡献是专门为钻头设计了一个系统，不仅试图检测有故障的钻头，还试图检测声音是在整个机械系统的活动阶段还是空闲阶段产生的，以便提供完整的远程控制。这项工作的第二个主要贡献是将声音的功率谱表示为图像，并对其进行一些变换，以揭示、暴露和强调隐藏在其中的健康模式。所创建的图像，即所谓的功率谱密度（PSD）图像，然后被馈送到深度卷积自动编码器（DCAE）中进行高级特征提取。该方案的最后一步包括采用所提出的 PSD 图像+DCAE 特征作为原始声音的最终表示，并将其用作非线性分类器的输入，其输出将表示最终诊断决策。实验结果证明了所提出的 PSD 图像+DCAE 特征具有很高的鉴别能力。它们还在嘈杂的数据集上进行了测试，结果表明它们对噪声具有鲁棒性。
