Predicting Faults in Mechanical Systems Using Artificial Intelligence and Machine Learning
TL;DR
A standardized benchmarking of multi-fidelity methodologies and explainability-augmented models is suggested, which shows that on these benchmarks, convolutional and recurrent deep architectures, attention-augmented LSTMs, and ensemble methods consistently outperform classical classifiers.
Abstract
The occurrence of unexpected malfunctions of mechanical systems such as bearings, gearboxes, turbofan engines, and other rotating machinery is still the main cause of downtime, safety hazards, and maintenance cost throughout the manufacturing, energy, and transportation sectors. Recent technological advances in artificial intelligence (AI) and machine learning (ML) technology have transformed fault prediction from an entirely physics-based activity to a data-based activity, in which sensors, signal processing, and learning algorithms estimate the health state of a machine together. This paper provides a structured assessment and comparative synthesis of AI/ML fault-prediction literature. Nineteen peer-reviewed studies obtained from scholarly and web-based databases for the period 2011–2026 are reviewed. A review of the two main tasks: fault diagnosis – classification of fault type, location and severity – prognosis – prediction of remaining useful life, RUL. The evidence is structured into a six-stage pipeline – data acquisition, pre-processing, feature extraction, model training, output, maintenance decision. The analysis is conducted using various test rigs including the CWRU bearings as well as NASA C-MAPSS turbofan engines and gearbox. The results show that on these benchmarks, convolutional and recurrent deep architectures, attention-augmented LSTMs, and ensemble methods consistently outperform classical classifiers; root-mean-square errors (RMSE) on C-MAPSS RUL errors are about 12 cycles and classification accuracies on CWRU exceed 98%. Simultaneously, the review also outlines a number of ongoing open challenges, like data scarcity, domain shift, class imbalance, limited interpretability, and overoptimistic evaluation, which must be overcome before lab results can become consistent with the reliable deployment in the industry. To conclude the paper suggests a standardized benchmarking of multi-fidelity methodologies and explainability-augmented models.