LSTM based Encoder Decoder for Multi sensor Anomaly Detection
Mechanical devices such as engines, vehicles, aircrafts, etc. are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time series which are inherently unpredictable. For instance, manual controls and/or unmonitored environmental conditions or load may lead to inherently unpredictable time series. Detecting anomalies in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity, or prediction models that utilize prediction errors to detect anomalies. We propose a Long Short Term Memory Networks based Encoder Decoder scheme for Anomaly Detection (EncDec AD) that learns to reconstruct 'normal' time series behavior, and thereafter uses reconstruction error to detect anomalies. We experiment with three publicly available quasi predictable time series datasets: power demand, space shuttle, and ECG, and two real world engine datasets with both predictive and unpredictable behavior. We show that EncDec AD is robust and can detect anomalies from predictable, unpredictable, periodic, aperiodic, and quasi periodic time series. Further, we show that EncDec AD is able to detect anomalies from short time series (length as small as 30) as well as long time series (length as large as 500)
ArXiv