In this paper, we introduce MIMII DUE, a new dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions. Conventional methods for anomalous sound detection face practical challenges because the distribution of features changes between the training and operational phases (called domain shift) due to various real-world factors. To check the robustness against domain shifts, we need a dataset that actually includes domain shifts, but such a dataset does not exist so far. The new dataset we created consists of the normal and abnormal operating sounds of five different types of industrial machines 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 showed significant performance differences between the source and target domains, indicating that the dataset contains the domain shifts. These findings demonstrate that the dataset will be helpful for checking 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

翻译：在本文中,我们引入了MIMII DUE, 这是一个用于因操作和环境条件的变化而造成领域变化的工业机器调查和检查故障的新数据集。 常规的异常探测方法面临实际挑战,因为由于各种现实世界因素,培训和操作阶段(所谓的域变换)之间的特征分布因各种培训与操作阶段(称为域变换)而不同。 为了检查对域变换的稳健性,我们需要一个实际上包括域变换的数据集,但迄今为止还没有这样一个数据集。 我们创建的新数据集由两种不同的操作/环境条件(源域和目标域)下的五种不同类型工业机器的正常和异常运行声音组成,这两种不同操作/环境条件(源域和目标域)与正常/异常状态无关,而两个领域之间发生域变换。 实验结果显示,源域和目标域之间的性差很大,表明数据集包含域变。 这些结果显示,数据集将有助于检查域变变的稳性。 数据集是DCSEE 2021挑战任务2数据集的一个子集,可在https://zenodo.org/recordordord/4744035555自由下载。