Support-Bounded Confidence-First Selective Diagnosis of Bearing Faults Under Speed and Source Shifts

Bearing fault diagnosis under varying operating conditions faces speed and data-source shifts that can make models unreliable outside their development regime. Record-level diagnosis therefore requires both fault classification and an automatic accept-or-defer decision. This study proposes Support-Bounded Confidence-First Selective Evidence Diagnosis (CSED), which separates evidence applicability from selective acceptance. CSED combines a frequency-domain logistic regression anchor with an acceleration-only WaveCNN expert. A prespecified support gate and calibration criterion admit waveform evidence; otherwise, the frequency anchor is retained. Records are ranked by a fixed confidence-risk score and accepted at prespecified coverage levels without a learned router or evaluation-label tuning. On the primary HUST (Huazhong University of Science and Technology) held-speed profile, five predeclared seed pairs yielded 270 pooled descriptive model-record predictions from 54 held record identifiers. Full-coverage Macro-F1/error was 0.8701/0.1333; at the prespecified 80% target, accepted Macro-F1/error was 0.9406/0.0602 and deferred error was 0.4259. Removing the support boundary increased observed accepted error, but the paired record-identifier-cluster bootstrap interval included zero, so this result is directional rather than statistically established. Additional variable-speed, cross-source, and frequency-anchor tests assess behavior beyond the primary profile. The results support the selective-diagnosis policy only under the evaluated record-wise protocol, not cross-source waveform transfer or bearing-wise generalization.