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.

翻译：在工业生产环境中，早期检测机器故障对于减少运营流程中断至关重要。近年来，深度学习方法逐渐成为机器故障检测领域的首选方案。深度学习凭借其自动特征提取能力，为故障检测过程提供了高效解决方案。本研究设计了一套基于深度学习的系统，用于分析工业机器产生的声学信号。将声学声信号转换为梅尔频谱图后，采用在图像分类任务中表现优异的深度学习架构DenseNet-169模型对频谱图进行分类。通过迁移学习方法，利用包含四种工业机器声信号的MIMII数据集对模型进行训练。结果表明，在不同信噪比水平下，该方法准确率介于97.17%至99.87%之间。