Anomaly detection based on multivariate data for the aircraft hydraulic system

It is almost impossible to detect the health status of the aircraft hydraulic system via a single variable, because of the complexity and the coupling relationship between components of the system. To serve the purpose, a novel anomaly detection method considering multivariate monitoring data is proposed in this article. The unsupervised auto-encoder model with the long short-term memory layers is used to reconstruct multivariate time series data, and a new comprehensive decision-making index based on two conventional ones is proposed to measure the difference between the observation and the reconstruction. Then, the health threshold of the decision-making index can be calculated by the kernel density estimation. The flight data are divided into several samples, and the anomaly detection of each sample is determined by the specific rule. The healthy status of each flight is determined by voting based on the detection results of all samples included in the flight. The performance of the proposed method is validated on the real continuous monitoring data, and the results confirm that the proposed model overcomes the problems of multistage and multivariate parameters in the anomaly detection of the aircraft system and improves the detection efficiency.