物联网技术在电机故障诊断方面取得了显著成果，其实时监控和数据处理能力强大，由于电机结构的复杂性及其信号的不稳定性、非线性和大数据量等特点，传统的诊断方法表现出较差的性能。为解决上述问题，将电机故障信号通过短时傅里叶变换（short-time Fourier transform, STFT）转换成时频谱图，同时利用卷积神经网络（convolutional neural network, CNN）算法对预处理后的样本进行训练，结合Triplet-Center Loss作为损失函数度量高维特征间的距离，并通过有监督微调网络，最终将数据传输至物联网云平台，实现电机故障的实时监测与诊断。实验结果表明：该方法能高效实时诊断矿山电机故障，弥补了传统诊断方法的不足。

In the rapidly developing era of IoT technology, significant progress has been made in enhancing real-time monitoring and data processing capabilities for motor fault diagnosis. Due to the complexity of motor structures and the characteristics of their signals, such as instability, nonlinearity, and large data volume, traditional diagnostic methods have exhibited poor performance. To address these challenges, motor fault signals were transformed into time-frequency spectrograms using the short-time Fourier transform(STFT). A convolutional neural network(CNN) algorithm was constructed to train preprocessed samples, and the Triplet-Center Loss was employed as a loss function to measure the distance between high-dimensional features of fault data. The network was fine-tuned in a supervised manner with labels, and the processed data was transmitted to an IoT cloud platform to enable real-time monitoring and diagnosis. Experimental results demonstrate the effectiveness of this method in efficiently diagnosing motor faults in real-time, thereby overcoming the shortcomings of traditional approaches.