We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line. Given heterogeneous time series data consisting of operation cycle signals and sensor signals, we aim at discovering abnormal events. Motivated by our empirical findings that conventional single-stage benchmark approaches may not exhibit satisfactory performance under our challenging circumstances, we propose a two-stage deep anomaly detection (TDAD) framework in which two different unsupervised learning models are adopted depending on types of signals. In Stage I, we select anomaly candidates by using a model trained by operation cycle signals; in Stage II, we finally detect abnormal events out of the candidates by using another model, which is suitable for taking advantage of temporal continuity, trained by sensor signals. A distinguishable feature of our framework is that operation cycle signals are exploited first to find likely anomalous points, whereas sensor signals are leveraged to filter out unlikely anomalous points afterward. Our experiments comprehensively demonstrate the superiority over single-stage benchmark approaches, the model-agnostic property, and the robustness to difficult situations.
翻译:我们利用从工厂组装线收集的制造数据集引入了一个数据驱动异常现象检测框架。考虑到由运行周期信号和传感器信号组成的不同时间序列数据,我们的目标是发现异常事件。我们根据我们的经验发现,常规的单阶段基准方法在我们富有挑战性的情况下可能不会取得令人满意的业绩。我们提议了一个两阶段深度异常现象检测框架,根据信号类型采用两种不同的、不受监督的学习模式。在第一阶段,我们通过使用经运行周期信号培训的模型选择异常候选人;在第二阶段,我们最终通过另一种模型从候选人中检测出异常事件,这种模型适合于利用感官信号培训的时空连续性。我们框架的一个显著特征是操作周期信号首先被利用来发现可能的异常点,而传感器信号则在事后被利用来过滤出不大可能的异常点。我们的实验全面展示了单阶段基准方法的优越性、模型-不可知性特性和对困难状况的稳健性。