In this paper, we introduce a new dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions (MIMII DUE). Conventional methods for anomalous sound detection face challenges in practice because the distribution of features changes between the training and operational phases (called domain shift) due to some real-world factors. To check the robustness against domain shifts, we need a dataset with domain shifts, but such a dataset does not exist so far. The new dataset consists of normal and abnormal operating sounds of industrial machines of five different types under two different operational/environmental conditions (source domain and target domain) independent of normal/abnormal, with domain shifts occurring between the two domains. Experimental results show significant performance differences between the source and target domains, and the dataset contains the domain shifts. These results indicate that the dataset will be helpful to check the robustness against domain shifts. The dataset is a subset of the dataset for DCASE 2021 Challenge Task 2 and freely available for download at https://zenodo.org/record/4740355

中文翻译：

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MIMII DUE：声音数据集，用于因运行和环境条件的变化而导致域移位的工业机器故障调查和检查

在本文中，我们引入了一个新的数据集，用于由于运行和环境条件的变化而导致的工业机器故障调查和检查，以及由于领域变化而导致的域偏移（MIMII DUE）。常规的异常声音检测方法在实践中面临挑战，因为由于某些实际因素，特征的分布在训练阶段和操作阶段之间发生了变化（称为域偏移）。为了检查针对域偏移的鲁棒性，我们需要一个具有域偏移的数据集，但是到目前为止还没有这样的数据集。新的数据集由五种不同类型的工业机器在两种不同的运行/环境条件（源域和目标域）下的正常和异常运行声音组成，独立于正常/异常，并且在两个域之间发生了域偏移。实验结果表明，源域和目标域之间存在显着的性能差异，并且数据集包含域移位。这些结果表明，该数据集将有助于检查针对域移位的鲁棒性。该数据集是DCASE 2021挑战任务2的数据集的子集，可从https://zenodo.org/record/4740355免费下载。