The research team, led by Daniel F. Goncalves together with Aldemir A. Cavallini Jr. and Valder Steffen Jr., focused on a class of machines that is notoriously difficult to monitor: horizontal rotors supported by hydrodynamic bearings. Unlike conventional rolling-element bearings, hydrodynamic bearings support the rotating shaft on a thin, pressurized film of oil, and this fluid-film behavior is inherently nonlinear. That nonlinearity distorts vibration signatures in ways that complicate diagnosis, which is precisely why most existing AI-based fault detection studies, which typically concentrate on rolling bearings, do not translate easily to this class of machinery.

The specific faults the team targeted are among the most common and most confounding in rotating equipment: unbalance, misalignment, and shaft cracks. Each of these defects produces vibration symptoms that overlap substantially, meaning that a maintenance engineer looking at raw vibration data may struggle to distinguish a developing crack from a simple misalignment. The stakes of getting this wrong are high, because a crack that goes undetected can propagate until the shaft fails outright, while an incorrect diagnosis can trigger unnecessary and costly maintenance interventions.

To build their diagnostic system, the researchers combined physical experimentation with high-fidelity simulation. They constructed a test rig consisting of a horizontal steel shaft, 19 millimeters in diameter and 950 millimeters long, driven by an electric motor at a fixed speed of 1,200 revolutions per minute and supported by two hydrodynamic bearings lubricated with ISO VG 68 oil. A corresponding finite element model of the rotor, built from 44 Timoshenko beam elements, was carefully calibrated against the experimental rig using a differential evolution optimization procedure, ensuring that the virtual model faithfully reproduced the machine’s dynamic behavior. This calibrated digital model, implemented with the open-source Rotordynamic Software ROSS, then allowed the team to generate large volumes of synthetic training data covering fault conditions that would be impractical or dangerous to create physically.

From the vibration signals, gathered at two measurement planes with orthogonally positioned proximity sensors, the team extracted features spanning three domains: statistical measures such as skewness, kurtosis, and root-mean-square values in the time domain; harmonic amplitudes in the frequency domain; and wavelet packet sub-band energies in the time-frequency domain. The raw feature set was then distilled using a hybrid strategy that pairs the Relief algorithm, an unsupervised feature ranking method, with SHAP, a model-dependent explainability technique rooted in cooperative game theory. This dual ranking produced a remarkably compact set of just 12 features, a reduction that slashed computational cost while actually improving diagnostic performance compared with using the full feature set or principal component analysis.

The architecture itself operates in two tiers. First, an ensemble of three clustering algorithms, hierarchical agglomerative clustering, DBSCAN, and Gaussian mixture models, votes by majority to flag whether a new vibration sample represents a novelty or deviation from healthy behavior. This unsupervised layer is crucial because real industrial machines rarely come with labeled databases covering every possible operating condition. Then, once an anomaly is detected, supervised classifiers, specifically k-nearest neighbors and support vector machines, take over to identify which specific fault is present. Hyperparameters for every model were tuned through grid search with cross-validation, and the feature selection was performed strictly on training data to prevent any information leakage that could inflate the results.

The performance figures are striking. In numerical tests, both classifiers achieved precision, recall, and F1-scores exceeding 98.9 percent, with the support vector machine reaching a recall of 99.64 percent and the k-nearest neighbors model posting a weighted F1-score of 99.13 percent. In experimental validation on the physical test rig, using 400 samples that included healthy, unbalanced, misaligned, and cracked conditions, the support vector machine achieved a weighted F1-score of 99.30 percent, while the crack class alone reached an F1-score of 98.95 percent despite being the smallest category in the dataset. The few misclassifications that did occur almost exclusively involved confusion between misalignment and cracks, the two faults with the most similar vibration signatures, and even these errors were statistically marginal.

Equally important for industrial deployment is speed. The researchers measured the complete computational pipeline for a single data window, including sliding-window processing, feature extraction via the discrete Fourier transform and wavelet decomposition, feature scaling, and final prediction. The full cycle took roughly 0.17 to 0.18 seconds for classification and about 0.34 seconds for the clustering-based detection stage, comfortably fast enough for real-time monitoring of continuously operating machinery without computational bottlenecks.

What truly sets this work apart, however, is its insistence on transparency. Using SHAP values, the team opened the black box and verified that the classifiers were reasoning in physically meaningful ways. The analysis revealed that the amplitude of the second harmonic of the rotational speed, extracted from measurement plane S1, was the single most influential feature for the support vector machine, contributing a total SHAP value of approximately 0.35, with the largest share attributable to crack conditions. This aligns perfectly with rotor dynamics theory, since misalignment and cracks are known to induce periodic disturbances at twice the rotational frequency. Even more compelling, the skewness of the vibration signal emerged as a critical discriminator for cracks, a result the researchers could explain directly: a propagating crack introduces asymmetry into the rotor system, which skews the distribution of the vibration response. The AI, in other words, had independently rediscovered a genuine physical phenomenon.

The Brazilian team is now developing an application programming interface to bring the methodology into real industrial environments, and they acknowledge that the current results were obtained at a single fixed rotational speed, a deliberate choice that isolated fault signatures from the complications of variable operating conditions. Future work will test the framework across different speeds, imbalanced datasets, and bearing-specific failure modes such as journal ovalization and lubrication problems. Still, the implications are considerable. By pairing robust, lightweight classifiers with explanations that a maintenance engineer can verify against physical intuition, the study offers a template for the kind of trustworthy, interpretable AI that Industry 5.0 demands, where intelligent machines and human experts collaborate rather than compete, and where a computer’s diagnosis of a cracked shaft comes with evidence a seasoned engineer can actually check.

Subject of Research: Explainable AI-based vibration fault diagnosis in rotating machines supported by hydrodynamic bearings

Article Title: Fault recognition approach applied to a rotating machine supported by hydrodynamic bearings using explainable artificial intelligence

Article References: Goncalves, D. F., Cavallini, A. A., Jr., & Steffen, V., Jr. (2026). Fault recognition approach applied to a rotating machine supported by hydrodynamic bearings using explainable artificial intelligence. Applied Intelligence, 56(15), Article 461. https://doi.org/10.1007/s10489-026-07491-9

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07491-9

Keywords: rotating machinery, hydrodynamic bearings, fault diagnosis, explainable artificial intelligence, SHAP, vibration analysis, predictive maintenance, machine learning, support vector machine, k-nearest neighbors, ensemble clustering, rotor dynamics