Fault recognition approach applied to a rotating machine supported by hydrodynamic bearings using explainable artificial intelligence
TL;DR
A fault diagnosis technique based on vibration analysis and explainable AI models is presented, revealing that features such as the second harmonic of the rotational speed and skewness play a critical role in distinguishing between similar fault patterns.
Abstract
Rotating machines are widely employed in modern industry. The growing demand for efficiency and operational durability drives the development of intelligent rotors equipped with Artificial Intelligence (AI) methods that promote the extension of equipment lifespan through proactive maintenance actions and reducing costs associated with unscheduled downtime. Some common failures in rotating machines, such as unbalance, misalignment, and cracks, have similar symptoms, making it difficult to diagnose accurately. The absence of labeled historical data and explainable models that are understandable to end users also makes using AI difficult in the industry environment. This work presents a fault diagnosis technique based on vibration analysis and explainable AI models in this context. The proposed methodology achieved high diagnostic performance, with precision, recall, and F1-score exceeding 98.9% in numerical tests and demonstrating robust accuracy in experimental scenarios using a reduced set of 12 features. The methodology is demonstrated with interpretable applications that facilitate real-time fault diagnosis and proactive maintenance in rotating machinery, showcasing its potential for industrial implementation. Vibration responses are obtained through a representative finite element (FE) model of a horizontal rotor and its corresponding test rig. Techniques for feature extraction and selection are also employed. Ensemble clustering is performed for novelty detection, while traditional supervised approaches are used for classification purposes. The explainable AI tool is used to interpret the results, revealing that features such as the second harmonic of the rotational speed (A2X-S1) and skewness (R08-S1), both extracted from plane S1 in axial direction, play a critical role in distinguishing between similar fault patterns. The combination of robust performance and the possibility of interpretation from the output models demonstrates the potential of the proposed approach for fault recognition in rotating systems.
