Fixed Channel Frequency Graphs for Cross Speed Compound Fault Diagnosis of Gearboxes

Shen, Junwei

doi:10.5194/ms-2026-186

Preprints

https://doi.org/10.5194/ms-2026-186

Preprints

15 Oct 2026

| 15 Oct 2026

Status: this preprint is currently under review for the journal MS.

Fixed Channel Frequency Graphs for Cross Speed Compound Fault Diagnosis of Gearboxes

Junwei Shen

Abstract. 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.

Received: 10 Oct 2026 – Discussion started: 15 Oct 2026

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Junwei Shen

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RC1:
'Comment on ms-2026-186', Anonymous Referee #1, 16 Oct 2026 reply
The manuscript evaluates a fixed 30-node channel-frequency graph for cross-speed compound fault diagnosis of gearboxes using recording-disjoint operating-condition holdouts. The independently retrained self-loop control and additional topology comparisons provide a useful basis for assessing graph connectivity. The authors also appropriately acknowledge the stronger overall performance of the waveform CNN and the limitations of the evaluation. However, the independence of design selection, reproducibility of the comparison models, and reliability of the reported topology gains require further evidence. I therefore recommend major revision before the manuscript can be considered for publication. My specific comments are as follows.
The discussion on p. 21 states that architecture and frequency-band selection followed earlier inspection of this dataset. Please describe the data, performance measures, and tuning decisions involved, and clarify whether the held-out conditions influenced design selection. Recording-disjoint splits alone do not exclude bias introduced during model development. Please freeze the design and confirm the topology gains using data not used for development, clearly distinguishing exploratory from confirmatory results.

Sections 3.4-3.6 do not sufficiently specify the random and adaptive graphs. Please provide the degree-preserving randomization algorithm, number of graph realizations, and equations for generating and normalizing the adaptive adjacency matrix. The residual-block operation order, loss weights, dropout rate, and threshold-search procedure also need clarification. Providing executable code, configurations, and recording-level split manifests would allow the central comparisons in Tables 6-8 to be reproduced.

The reported difference in mean Macro-F1 between the fixed and random graphs is 0.0134, but Table 8 does not provide the fixed graph's scores for individual training seeds. Please supply paired results and recording-level predictions for the fixed graph and its controls. Assess training variability separately from variability across random graph realizations, and quantify uncertainty in the performance differences while preserving the recording-level dependence structure; windows and training seeds are not additional independent acquisitions.

The 12 training-validation allocations in Table 11 and Fig. 9 cover only the fixed graph. They therefore do not establish whether its advantage over the controls depends on the reference split. Please evaluate the self-loop and random graph controls under the same allocations and report paired performance differences. Also report the decision thresholds and fusion weights selected in each fold to assess selection stability with the small validation sets.

The frequency-band boundaries in Section 3.2 need stronger justification, particularly because three narrow bands contain only one local FFT bin each. Table 5 reports fixed-graph Macro-F1 values of 0.593 at 1200 r/min and 0.765 under variable speed. Please examine representative spectra and misclassified recordings to explain these deficits. An order-aligned comparison or a band-boundary perturbation study would help establish the operating range over which this representation is effective.

Tables 9-10 mainly report aggregate exact-match and per-label F1 scores, while rolling-element and cage faults each have only one positive recording per condition. Please provide the mapping from the 64 recordings to the seven-label targets, missed and spurious labels for each compound combination, and the false-positive rate on normal recordings. For labels represented in both single and compound faults, report their performance separately to clarify the effect of fault coexistence.

Please specify the FFT normalization and treatment of zero power in the log-power descriptors of Eq. (3). For the noise experiments, define the power reference used for SNR calculation, whether noise is independent across channels, and the number of noise realizations. Please also repair the sentence fragment "removal of the eight local descriptors." in Section 3.4 and use consistent model names, particularly "fixed graph" and "connected graph", throughout the manuscript.

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Citation: https://doi.org/10.5194/ms-2026-186-RC1
- AC1: 'Reply on RC1', Junwei Shen, 20 Oct 2026 reply
  
  Dear reviewer, in response to your comments, we have provided corresponding replies. Please refer to the point-by-point reply letter attached for details.
  
  Reply
  
  Citation: https://doi.org/10.5194/ms-2026-186-AC1

Junwei Shen

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Short summary

Gearboxes often have multiple simultaneous faults, which are hard to identify when speeds change. We tested a new computer method that converts vibration data into simple network graphs. Using 64 public recordings, it reached 83 % average accuracy in finding seven fault types. It beat random graphs but scored lower than standard wave-based deep learning. This shows the promise and current limits of graph methods for machinery health monitoring.


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