Real-time bearing fault classification of induction motor using enhanced inception ResNet-V2

The rolling bearing is a vital part used in different rotating electrical devices. Detecting defects in bearings is crucial for the safe operation of these machines. However, it is challenging to use Deep Learning techniques to identify bearing defects when the machine is not under load. To resolve this issue, this paper presents the Constant-Q Non-stationary Gabor Transform with enhanced Inception ResNet-V2, proposed for the early-stage classification of ball bearing faults in induction motors. The proposed model obtained the vibration images, i.e. time-frequency images of unfiltered vibration signals from the laboratory experimental setup. These images are applied to the proposed model, which classifies the ball bearing faults under various load conditions while adjusting its hyperparameter values instead of employing default ones. Furthermore, the model underwent training using k-fold method to assess its resilience with the use of optimal values obtained from hyperparameter tuning. The model is evaluated by performance metrics like F1-score, Recall, Precision, Confusion Matrix and Training time. The proposed model accomplished an average classification accuracy of 99.84% in low load and full load conditions within a few epochs. Ultimately, when compared to Inception-V4 and ResNet-50, which achieved 91.41% and 91.65%, respectively, the experimental findings unambiguously demonstrate the superior performance of the proposed model over both models.滚动轴承是应用于各类旋转电气设备的关键零部件。对轴承缺陷开展检测，是保障此类设备安全运行的核心环节。但当设备处于无负载工况时，采用深度学习技术识别轴承故障仍存在较大挑战。为解决这一问题，本文提出一种结合改进型Inception ResNet-V2的常数Q非平稳Gabor变换（Constant-Q Non-stationary Gabor Transform）方法，用于感应电动机球轴承故障的早期分类任务。该模型通过实验室实验平台采集未滤波振动信号的时频图像（即振动图像）作为输入数据，将上述时频图像输入至所提模型，该模型可通过调整超参数（而非使用默认超参数），实现不同负载工况下的球轴承故障分类。此外，为评估模型的鲁棒性，本文采用k折交叉验证法（k-fold method），结合超参数调优得到的最优参数对模型开展训练。模型的性能通过F1分数（F1-score）、召回率（Recall）、精确率（Precision）、混淆矩阵（Confusion Matrix）以及训练时长（Training time）等指标进行综合评估。所提模型在轻载与满载工况下，仅需少量训练轮次即可实现99.84%的平均分类准确率。最终，与分别取得91.41%与91.65%准确率的Inception-V4和ResNet-50相比，实验结果清晰证明了所提模型的性能显著优于上述两种模型。