Real-Time Anomaly Detection for Flight Testing Using AutoEncoder and LSTM
Zhiqiang Que, Yanyang Liu, Ce Guo, Xinyu Niu, Yongxin Zhu, Wayne W. Luk · 2019
Flight testing is crucial in validating the functionality and safety in new commercial aircraft design before mass production. The challenge is to support real-time analysis of high-dimensional time series data generated from tens of thousands of sensors around the aircraft during test flights. We propose a novel 2-stage approach, using a fine-tuned autoencoder to extract the generic underlying features of high-dimensional data, followed by a stacked LSTM using the learned features to predict aircraft time series and to detect anomalies in real-time 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 AutoEncoder-LSTM on CPU and GPU, our FPGA design is respectively 36.3 and 23.9 times faster and consumes 247 and 499 times less energy.