Compound bearing and gear faults require identification of multiple fault components under changes in operating speed. This study evaluates a fixed channel–frequency graph using 64 public recordings and recording-disjoint operating-condition holdouts. Each three-channel window is represented by 30 nodes containing full-window spectral statistics and eight local log-power descriptors. A two-layer graph network predicts seven binary fault labels. The fixed graph achieves mean recording-level Macro-F1 of 0.8284, compared with 0.7954 for a matched self-loop control and 0.8150 for a degree-matched random graph. The adaptive comparator achieves 0.8420. Five-seed condition results show higher adaptive than random graph means in all four holdouts, with differences of 0.0420, 0.0146, 0.0142, and 0.0370 at 1200, 1800, and 2400 r/min and variable speed, respectively. The fixed graph remains below the waveform CNN, which achieves 0.9708; validation-gated CNN–graph fusion reaches 0.9713. Validation rotation retains the low-speed deficit, and performance deteriorates at 0 and −5 dB test noise. The findings quantify the contribution of the prescribed topology within the evaluated graph representation, while showing that adaptive connectivity and waveform learning achieve higher overall scores on this benchmark.