Operator-Centred Visualization of Rolling-Element Bearing Faults: A Comparison of the Zhao–Atlas–Marks Distribution and CEEMDAN, with a Non-Specialist Readability Assessment of the ZAMD-Based Framework

Rolling-element bearings remain a leading cause of unplanned downtime in industrial machinery, while vibration-based condition monitoring has matured, the post-2018 literature has converged on machine-learning classifiers whose interpretability layer remains restricted to expert analysts. This paper presents an operator-centred visualization framework supported by two time-frequency methods: the Zhao–Atlas–Marks Distribution (ZAMD), a Cohen’s-class representation with a cross-term-suppressing cone kernel, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), evaluated through its Hilbert spectral analysis output. Both methods produce two-dimensional time-frequency artefacts with a similar visual structure—impact-related energy bursts that recur at the characteristic fault frequencies—and are presented in side-by-side form for each fault class. A four-stage framework wraps either method with the characteristic fault frequencies (supplied as a comparison reference) and colour-coded, healthy baseline-referenced scaling. The framework is demonstrated on a laboratory bearing rig (KOYO 6302, 600 RPM) across inner-race, outer-race, and ball-spin fault classes. A preliminary readability assessment of annotated ZAMD-generated artefacts, with twelve non-specialist participants from a brewing and packaging industrial context, recorded 89.8% aggregate classification accuracy (194 of 216 trials) at a mean response time of 15.4 s. Because no label-free or alternative-format control conditions were included, this result characterises the annotated artefact as a whole and does not isolate the contribution of the time-frequency representation from that of the annotation layer; it is established for the ZAMD engine only. The two methods are compared as visualization engines—qualitatively, through the structure of their side-by-side time-frequency artefacts, and quantitatively, through computational cost—whereas the non-specialist readability assessment characterises the ZAMD-based framework specifically. CEEMDAN is positioned as a candidate alternative engine whose time-frequency output is shown to be structurally similar but whose operator readability has not been tested with human participants and is identified as future work.