一种用于微电机故障诊断与质量控制的数据驱动振动分析框架
A Data-Driven Vibration Analysis Framework for Micro-Motor Fault Diagnosis and Quality Control
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中文总结 AI 辅助
本研究针对电动牙刷微电机故障诊断与质量控制需求，设计振动信号采集装置，结合RF特征选择与SVM分类模型，实现微电机故障的精准高效检测，多项性能指标优异。
中文摘要 AI 辅助
内置微电机的可靠性对电动牙刷的性能和使用寿命至关重要。本文提出一种基于振动的故障检测方法，用于识别电动牙刷中微电机的缺陷。研究人员设计并开发了专用信号采集装置，采用高精度加速度计采集微电机的振动信号。为有效表征微电机状态，研究人员从原始振动数据的时域和频域中提取了全面的特征。随后采用随机森林（RF）算法评估所有提取特征的重要性。为基于故障机制更好地解释提取的特征，同时减少维度和计算开销并避免过拟合，研究人员选择重要性得分最高的前三个特征，构成最优特征子集。最后，采用支持向量机（SVM）模型，基于所选特征对电机状态进行分类。实验结果表明，所提结合RF特征选择与SVM分类的方法，具有出色的诊断性能。具体而言，该模型的平衡准确率达94.44%，缺陷召回率为88.89%，缺陷F1分数为94.12%，马修斯相关系数为93.74%，几何均值为94.28%，受试者工作特征曲线下面积达100.00%。这些稳健的指标证实，所提方法可准确高效地检测电动牙刷中微电机的故障，为制造领域的质量控制与状态监测提供了实用可靠的解决方案。
英文摘要
The reliability of the internal micro-motors is crucial for the performance and lifespan of electric toothbrushes. In this paper, a vibration-based fault detection method is proposed to identify micro-motor defects in electric toothbrushes. A dedicated signal acquisition device was designed and developed to capture the vibration signals of micro-motors using a high-precision accelerometer. To effectively characterize the micro-motor conditions, comprehensive features were extracted from the raw vibration data in both the time and frequency domains. A random forest (RF) algorithm was then employed to evaluate the importance of all extracted features. To better interpret the extracted features based on fault mechanisms, and to reduce dimensionality and computational overhead while avoiding overfitting, the top three features with the highest importance scores were selected to form the optimal feature subset. Finally, a support vector machine (SVM) model was utilized to classify the motor states based on the selected features. Experimental results demonstrate that the proposed method, combining RF-based feature selection and SVM classification, achieves outstanding diagnostic performance. Specifically, the model yields a balanced accuracy of 94.44%, a defect recall of 88.89%, a defect F1-score of 94.12%, a Matthews correlation coefficient of 93.74%, a geometric mean of 94.28%, and an area under the receiver operating characteristic curve of 100.00%. These robust metrics confirm that the proposed approach can accurately and efficiently detect micro-motor faults in electric toothbrushes, providing a practical and reliable solution for quality control and condition monitoring in manufacturing.
