AI-Based Anomaly Detection Techniques for Structural Fault Diagnosis Using Low-Sampling-Rate Vibration Data

Abstract

Rotorcrafts experience severe vibrations during operation. To ensure the safety of rotorcrafts, it is necessary to perform anomaly detection to detect small-scale structural faults in major components. To accurately detect small-scale faults before they grow to a fatal size, HR (high sampling rate) vibration data are required. However, to increase the efficiency of data storage media, only LR (low sampling rate) vibration data are generally collected during actual flight operation. Anomaly detection using only LR data can detect faults above a certain size, but may fail to detect small-scale faults. To address this problem, we propose an anomaly detection technique using the SR3 (Super-Resolution via Repeated Refinement) algorithm to upscale LR data to HR data, and then applying the LSTM-AE model. This technique is validated for two datasets (drone arm data, CWRU bearing data). First, the necessity for HR data is illustrated by showing that anomaly detection using LR data fails, and the upscaling performance of the SR3 algorithm is validated in the frequency and time domain. Finally, the anomaly detection for a structural fault diagnosis is performed for the upscaled data and the HR data using the LSTM-AE model. The quantitative evaluation of the Min–Max normalized reconstruction error distribution is performed through the MSE (Mean Square Error) value of the anomaly detection results. As a result, it is confirmed that the anomaly detection using upscaled test data can be performed as successfully as the anomaly detection using HR test data for both datasets by the proposed technique.