  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.
