AI-assisted structural health monitoring for high-performance mechanical components

Abstract

Abstract. High-performance mechanical component structural health monitoring (SHM) is a vital issue in contemporary engineering, especially in the aerospace, automotive, and industrial turbomachinery sectors where component failure may be disastrous. This article introduces a new AI-aided SHM framework with multimodal sensor fusion and ensemble deep learning architecture with one-dimensional convolutional neural networks (1D-CNN) and bidirectional long short-term memory (Bi-LSTM) networks, which can be used to detect fault, classify fault, and predict remaining useful life (RUL) in real-time. The proposed system takes in time-series streams of vibration, acoustic emission, and strain gauge data, runs them through an adaptive signal preprocessing pipeline, and derives hierarchical features of fault relevance without using manually specified features. The tests are done on two benchmark datasets, which include the CWRU bearing fault dataset and a custom gas turbine blade fatigue dataset that were obtained under controlled laboratory settings and a real gas turbine compressor testbed. The CNN-BiLSTM ensemble suggested has an accuracy of fault classification of 98.7 and a mean absolute percentage error (MAPE) of 3.14 to predict RUL with average inference latency of 12.3 ms, which is appropriate to be integrated into embedded systems in real-time. These findings constitute a statistically significant step forward compared to the current state-of-the-art baselines and they generalize and scale to provide an AI-SHM paradigm of safety-critical mechanical systems.