煤矿防爆电气设备在运行过程中产生的振动会损害其机械完整性，导致紧固件松动、零部件磨损，并改变设备的结构与振动模态，进而引发信号特征的复杂变化，使得正常振动频率与故障引发的新频率成分相互混淆，模糊了正常信号与故障信号之间的界限，从而降低传统检测方法在故障检测中的准确性。针对上述问题，提出一种基于单分类支持向量机（OCSVM）的煤矿防爆电气设备振动故障自动检测方法。首先，构造设备的正常状态特征和振动故障状态特征，根据OCSVM的特性，将正常状态特征序列设定为OCSVM核函数的决策边界学习目标。考虑煤矿防爆电气设备振动故障信号呈现非线性和高维特征，选定多项式核作为OCSVM的核函数。然后，采用网格搜索和K−交叉验证相结合的方式对OCSVM进行参数调优，以使OCSVM达到更好的性能。最后，通过求取OCSVM目标函数的最优解，确定最优决策边界，以此实现煤矿防爆电气设备振动故障的自动检测。实验结果显示：① 在迭代次数为20时，OCSVM算法算法可完成收敛，达到稳定。② 基于OCSVM的电气设备信号划分实验中，借助多项式核函数能精准划分样本实现检测。③ 振动故障自动检测性能分析中，所提方法在各样本量下准确率均显著高于红外热成像技术检测方法、基于灰狼优化支持向量机模型检测方法，小样本量时准确率达98.25%且稳定性好。

The vibration generated by explosion-proof electrical equipment in coal mines during operation can compromise its mechanical integrity, leading to fastener loosening, component wear, and changes in the structure and vibration modes of the equipment. This can cause complex changes in signal features, resulting in confusion between normal vibration frequency and new frequency components induced by faults. As a result, the boundary between normal and fault signals becomes unclear, reducing the accuracy of traditional fault detection methods. To address this issue, an automatic vibration fault detection method for coal mine explosion-proof electrical equipment was proposed based on One-Class Support Vector Machine (OCSVM). First, the normal state features and vibration fault state features of the equipment were constructed. Based on the characteristics of OCSVM, the normal state feature sequence was set as the learning target for the decision boundary of the OCSVM kernel function. Due to the nonlinear and high-dimensional characteristics of vibration faults in explosion-proof electrical equipment, a polynomial kernel was selected as the OCSVM kernel function after comprehensive consideration. Then, grid search combined with K-fold cross-validation was used to optimize the parameters of the OCSVM, ensuring better performance. Finally, by obtaining the optimal solution of the OCSVM objective function, the optimal decision boundary was determined to realize automatic fault detection of vibration faults in coal mine explosion-proof electrical equipment. Experimental results showed that: ① When the number of iterations is 20, the OCSVM algorithm can complete convergence and achieve stability. ② In the electrical equipment signal classification experiment based on OCSVM, the use of the polynomial kernel function accurately classified samples for detection. ③ In the performance analysis of automatic vibration fault detection, the proposed method showed significantly higher accuracy across different sample sizes than infrared thermography and detection methods based on grey wolf optimization and support vector machine. Under small sample sizes, it achieved an accuracy of 98.25% with good stability.