According to the smart manufacturing paradigm, the analysis of assets’ time series with a machine learning approach can effectively prevent unplanned production downtimes by detecting assets’ anomalous operational conditions. To support smart manufacturing operators with no data science background, we propose an anomaly detection approach based on deep learning and aimed at providing a manageable machine learning pipeline and easy to interpret outcome. To do so we combine (i) an autoencoder, a deep neural network able to produce an anomaly score for each provided time series, and (ii) a discriminator based on a general heuristics, to automatically discern anomalies from regular instances. We prove the convenience of the proposed approach by comparing its performances against isolation forest with different case studies addressing industrial laundry assets’ power consumption and bearing vibrations.

根据智能制造范例中，使用机器学习方法分析资产的时间序列可以通过检测资产的异常运行状况来有效地防止计划外的生产停机。为了支持没有数据科学背景的智能制造运营商，我们提出了一种基于深度学习的异常检测方法，旨在提供可管理的机器学习管道，并易于解释结果。为此，我们结合了（i）自动编码器，能够为每个提供的时间序列产生异常评分的深度神经网络，以及（ii）基于一般启发式算法的鉴别器，以从常规实例中自动识别异常。