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<p>Accurate, lightweight, and real-time diagnostic devices are required because undetected rolling element bearing problems result in unscheduled industrial downtime and substantial financial loss. Inspired by coordinate attention concepts, this work offers DS-Mobile-Net-CA, a Dual-Stream 1D Mobile-Net with a 1D Channel Attention block. The model uses raw 1D vibration signals without any intermediate 2D image transformation to classify five bearing fault conditions: Healthy, Inner Race Fault (IRF), Outer Race Fault (ORF), Ball Fault, and Combined Fault. A Spectra Quest MFS-PKG5 test-rig attached with a Kistler 8763A50 IEPE accelerometer recording at sampling rate of 12,800&#xa0;Hz was used to collect vibration data at three rotational speeds (1000, 1500, and 2000&#xa0;rpm) and three load conditions (0, 6, and 12&#xa0;g). In each stream, the architecture employs 1D depth-wise separable convolutions (Mobile-Net blocks), which are then concatenated and tuned via a channel attention technique. Through its ability to offer a mere 81,829 trainable parameters, 4.8&#xa0;M FLOPs, and a time required for inference equal to 3.98ms and being the most computationally inexpensive among all other designs studied. The architecture is capable of delivering a classification accuracy of 99.80%, a weighted F1-score of 99.74%, as well as a Matthews Correlation Coefficient (MCC) value of 0.9974. In order to check the statistical validity of the study results, K-fold cross-validation (K = 5, average = 99.17%), ablation analysis, Confidence Interval 95% calculation, and Friedman ranking test (<i>p</i> = 0.0067) were conducted.</p>

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Dual-stream 1D mobile-net with channel attention fusion for rolling element fault diagnosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Raghav Kumar,
  • Ramireddy Dheeraj Reddy,
  • T. Narendiranath Babu,
  • M. Pandiyan

摘要

Accurate, lightweight, and real-time diagnostic devices are required because undetected rolling element bearing problems result in unscheduled industrial downtime and substantial financial loss. Inspired by coordinate attention concepts, this work offers DS-Mobile-Net-CA, a Dual-Stream 1D Mobile-Net with a 1D Channel Attention block. The model uses raw 1D vibration signals without any intermediate 2D image transformation to classify five bearing fault conditions: Healthy, Inner Race Fault (IRF), Outer Race Fault (ORF), Ball Fault, and Combined Fault. A Spectra Quest MFS-PKG5 test-rig attached with a Kistler 8763A50 IEPE accelerometer recording at sampling rate of 12,800 Hz was used to collect vibration data at three rotational speeds (1000, 1500, and 2000 rpm) and three load conditions (0, 6, and 12 g). In each stream, the architecture employs 1D depth-wise separable convolutions (Mobile-Net blocks), which are then concatenated and tuned via a channel attention technique. Through its ability to offer a mere 81,829 trainable parameters, 4.8 M FLOPs, and a time required for inference equal to 3.98ms and being the most computationally inexpensive among all other designs studied. The architecture is capable of delivering a classification accuracy of 99.80%, a weighted F1-score of 99.74%, as well as a Matthews Correlation Coefficient (MCC) value of 0.9974. In order to check the statistical validity of the study results, K-fold cross-validation (K = 5, average = 99.17%), ablation analysis, Confidence Interval 95% calculation, and Friedman ranking test (p = 0.0067) were conducted.