Failure Detection and Isolation by LSTM Autoencoder

Abstract

AbstractFailure diagnosis on some system is often preferred even the data of the system is not designed for the condition monitoring and does not contain any or contains little example cases of failures. For this kind of system, it is unrealistic to directly observe condition from single feature or neither to build a machine learning system that has been trained to detect known failures. Still if any data describing the system exists, it is possible to provide some level of diagnosis on the system. Here we present an LSTM (Long Short Term Memory) autoencoder approach for detecting and isolating system failures with insufficient data conditions. Here we also illustrate how the failure isolation capability is effected by the choice of input feature space. The approach is tested with the flight data of F-18 aircraft and the applicability is validated against several leading edge flap (LEF) control surface seizure failures. The method shows a potential for not only detecting a potential failure in advance but also to isolate the failure by allocating the anomaly on the data to the features that are related to the operation of LEFs. The approach presented here provides diagnostic value from the data than is not designed for condition monitoring neither contain any example case failures.