RealTime Anomaly Detection for Flight Testing Using AutoEncoder and LSTM Flight testing is crucial in validating the functionality and safety in new commercial aircraft design before mass production. The challenge is to support realtime analysis of highdimensional time series data generated from tens of thousands of sensors around the aircraft during test flights. We propose a novel 2stage approach, using a finetuned autoencoder to extract the generic underlying features of highdimensional data, followed by a stacked LSTM using the learned features to predict aircraft time series and to detect anomalies in realtime for flight testing. A novel Timestep(TS)buffer is introduced to avoid redundant calculations of LSTM gate operations to reduce system latency. Compared with a software implementation of the AutoEncoderLSTM on CPU and GPU, our FPGA design is respectively 36.3 and 23.9 times faster and consumes 247 and 499 times less energy.

Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing The emergence of IoT and AI has brought revolutionary change in various application domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to achieve highly flexible and automated production processes. In this paper, we study a use case of anomaly detection in smart manufacturing using the real data collected from the sensing devices of a factory production line. Our goal is to improve the anomaly detection accuracy at an earlier stage of production line, so that cost and time wasted by possible production failures can be reduced. To overcome the limited and irregular anomaly patterns found from our multivariate sensor dataset, we proposed an unsupervised realtime anomaly detection algorithm based on LSTMbased AutoEncoder. Our evaluations show that our approach achieved almost 90% accuracy for both precision and recall while other classification or regression based methods only reached 70%85%.