In-situ classification of faulty sounds is an important issue in machine health monitoring and diagnosis. However, in a noisy environment such as a factory, machine sound is always mixed up with environmental noises, and noise-only periods can exist when a machine is not in operation. Therefore, a deep neural network (DNN)-based fault classifier has to be able to distinguish noise from machine sound and be robust to mixed noises. To deal with these problems, we investigate on-site noise exposure (ONE) that exposes a DNN model to the noises recorded in the same environment where the machine operates. Like the outlier exposure technique, noise exposure trains a DNN classifier to produce a uniform predicted probability distribution against noise-only data. During inference, the DNN classifier trained by ONE outputs the maximum softmax probability as the noise score and determines the noise-only period. We mix machine sound and noises of the ToyADMOS2 dataset to simulate highly noisy data. A ResNet-based classifier trained by ONE is evaluated and compared with those trained by other out-of-distribution detection techniques. The test results show that exposing a model to on-site noises can make a model more robust than using other noises or detection techniques.

翻译：现场分类故障声音是机器健康监测与诊断中的重要课题。然而，在工厂等嘈杂环境中，机器声音常与环境噪声混杂，且当机器未运行时可能存在纯噪声时段。因此，基于深度神经网络（DNN）的故障分类器需能区分噪声与机器声音，并对混合噪声具有鲁棒性。针对这些问题，我们研究了现场噪声暴露（ONE）技术，该技术将DNN模型暴露于机器运行环境中所记录的噪声中。与异常值暴露技术类似，噪声暴露通过训练DNN分类器，使其对纯噪声数据输出均匀预测概率分布。推理时，经ONE训练的DNN分类器将最大softmax概率输出为噪声分数，并判定纯噪声时段。我们通过混合ToyADMOS2数据集中机器声音与噪声来模拟高噪声数据，并评估了基于ResNet的分类器经ONE训练后的性能，同时与其他分布外检测技术训练的分类器进行了比较。测试结果表明，将模型暴露于现场噪声中比使用其他噪声或检测技术能使模型更具鲁棒性。