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.
Abstract. Timely fault diagnosis in turbomachinery plays an important role in avoiding disastrous failures, reducing the number of unexpected downtimes, and optimizing the maintenance cycle in industrial applications that use a lot of energy. The paper suggests a new hybrid deep learning architecture, combining one-dimensional convolutional neural networks (1D-CNN) with bidirectional long short-term memory (BiLSTM) networks to the problem of automatic detection and early forecasting of mechanical faults based on raw vibration signals. The combination of a multi-domain feature extraction strategy, which covers time-domain statistical indicators, spectral descriptors based on the fast fourier transform (FFT), and continuous wavelet transform (CWT) scalogram representations into a single 128-dimensional feature vector is used as the input of the model. The suggested CNN-BiLSTM design effectively extracts both local spectrotemporal features and long-range sequential dynamics of the rotating machinery vibration data. Experiments on the CWRU Bearing Dataset and a custom turbomachine testbed dataset show that the proposed model can achieve a mean classification accuracy of 97.6% with clean signal conditions and 95.1% at a signal-to-noise ratio of 10 dB, and outperforms standalone CNN, BiLSTM, SVM, ANN, and Random Forest baselines by up to 10.2 The findings determine the appropriateness of the proposed framework on edge-deployable and real-time condition monitoring systems.
N. Bharani· Materials Research Proceedin...· 0 citations
This systematic review synthesizes recent advances in AI applications for SHM across civil infrastructure including bridges, buildings, tunnels, and dams and identifies interdisciplinary opportunities including federated learning for decentralized monitoring, explainable AI for stakeholder trust, and autonomous inspection systems.
M. Khan, Muhammad Shoaib Ashraf, Muhammad Jahanzeb et al.· International journal of com...· 0 citations
Ensuring the integrity of critical infrastructure, such as bridges, dams, and large-scale structures, is essential to safety, reliability, and operational continuity. These assets are exposed to mechanical, thermal, environmental, and operational loads that can accelerate fatigue, corrosion, wear, and other deterioration mechanisms. Traditional inspections are periodic and costly, while sensor-based monitoring systems generate large volumes of imperfect field data that are difficult to interpret without engineering context.
This paper presents a physics-informed machine learning and multi-sensor fusion framework for fatigue-oriented Structural Health Monitoring (SHM) and predictive maintenance. The method is demonstrated on an instrumented steel-concrete composite bridge using resistive strain, fiber Bragg grating (FBG) strain and temperature, and displacement measurements. In decision-critical SHM applications, uncertainty arises from noisy sensor signals, thermal effects, drift, missing data, and ambiguous operating events. The proposed framework reduces this uncertainty by applying physics and engineering principles to structure the correction, validation, and interpretation of sensor data.
Machine learning supports thermal compensation, anomaly screening, cross-sensor substitution, and confidence tagging, while deterministic engineering methods remain responsible for strain interpretation, event extraction, rainflow-style cycle counting, and fatigue damage indicators. Results show that multi-year monitoring data can be reduced into compact fatigue-relevant features while preserving traceability to raw measurements. A supervisory agentic layer coordinates data-quality checks, multi-sensor consistency review, and confidence-tagged substitution, creating an auditable workflow for engineering decision support. SHM value does not come only from better sensors or better ML models. It comes from traceable workflows that turn imperfect field data into reliable, reviewable, and actionable engineering evidence.
Guga Gugaratshan, A. Halfpenny, F. Kihm et al.· e-Journal of Nondestructive...· 0 citations
An end-to-end predictive maintenance system is proposed for high stress mechanical drivetrain and rotating machinery and the architecture proposed combines an industrial Internet of Things edge sensory network and hybrid machine learning and deep learning pipelines.
Ashish Kumar, Md Mohtab Alam, N. Priya et al.· International journal of com...· 0 citations
An unsupervised hybrid deep learning framework for unknown bearing fault diagnosis and severity assessment using vibration signals that combines Continuous Wavelet Transform, Convolutional Neural Networks, and Long Short-Term Memory autoencoders is presented.
Edris Shamsulhaq, Fikri Arif Wicaksana· Jambura Journal of Electrica...· 0 citations
