Actuators 2025, 14(9), 458; https://doi.org/10.3390/act14090458 - 19 Sep 2025
Abstract
Reliable fault diagnosis of rotating machines under extreme conditions—strong speed, load variation, intense noise, and severe class imbalance—remains a critical industrial challenge. We develop an ultra-light yet robust framework to accurately detect weak bearing, and gear faults when less than 5% labels, 10
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Reliable fault diagnosis of rotating machines under extreme conditions—strong speed, load variation, intense noise, and severe class imbalance—remains a critical industrial challenge. We develop an ultra-light yet robust framework to accurately detect weak bearing, and gear faults when less than 5% labels, 10 dB noise, 100:1 imbalance and plus or minus 20% operating-point drift coexist. Methods: The proposed Adaptive Feature Module–Conditional Dynamic GRU Auto-Encoder (AFM-CDGAE) first compresses 512 d spectra into 32/48 d “feature modules” via K-means while retaining 98.4% fault energy. A workload-adaptive multi-scale convolution with spatial attention and CPU-aware λ-scaling suppresses noise and adapts to edge–device load. A GRU-based auto-encoder, enhanced by self-attention, is trained with balanced-subset sampling and minority-F1-weighted voting to counter extreme imbalance. On Paderborn (5-class) and CWRU (7-class) benchmarks, the 0.87 M-parameter model achieves 99.12% and 98.83% Macro-F1, surpassing five recent baselines by 3.1% under normal and 5.4% under the above extreme conditions, with only 1.5 to 1.8% F1 drop versus 6.7% for baselines. AFM-CDGAE delivers state-of-the-art accuracy, minimal footprint and strong robustness, enabling real-time deployment at the edge.
Full article
(This article belongs to the Section Actuators for Manufacturing Systems)
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