Detecting machine malfunctions at an early stage is crucial for reducing interruptions in operational processes within industrial settings. Recently, the deep learning approach has started to be preferred for the detection of failures in machines. Deep learning provides an effective solution in fault detection processes thanks to automatic feature extraction. In this study, a deep learning-based system was designed to analyze the sound signals produced by industrial machines. Acoustic sound signals were converted into Mel spectrograms. For the purpose of classifying spectrogram images, the DenseNet-169 model, a deep learning architecture recognized for its effectiveness in image classification tasks, was used. The model was trained using the transfer learning method on the MIMII dataset including sounds from four types of industrial machines. The results showed that the proposed method reached an accuracy rate varying between 97.17% and 99.87% at different Sound Noise Rate levels.

中文翻译：

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使用深度神经网络进行声学信号分析以检测工业机器的故障诊断

早期检测机器故障对于减少工业环境中操作流程的中断至关重要。最近，深度学习方法开始成为机器故障检测的首选。由于自动特征提取，深度学习为故障检测过程提供了有效的解决方案。在这项研究中，设计了一个基于深度学习的系统来分析工业机器产生的声音信号。声学信号被转换成梅尔声谱图。为了对频谱图图像进行分类，使用了 DenseNet-169 模型，这是一种因其在图像分类任务中的有效性而被认可的深度学习架构。该模型是在 MIMII 数据集上使用迁移学习方法进行训练的，其中包括来自四种工业机器的声音。结果表明，该方法在不同声噪声等级下的准确率在97.17%到99.87%之间。