舵机异常检测模型的设计与研究

针对舵机测试数据量大且样本不均衡问题，提出了一种使用灰狼优化算法（GWO）优化深度神经网络（DNN）并与逻辑回归分类器（LRC）相结合的舵机异常检测模型（GWO-DNN-LRC）。模型的构建有效的解决了舵机测试数据中小样本难以被准确分类的问题，适用于舵机测试数据的深度特征提取与多故障分类。该方法的准确度达到99.261%，相较于LRC、DNN、GWO-DNN分别提高了4.931%、0.205%、0.087%，精确度、召回率、F-score达到98.417%、98.062%、98.217%。在不同类别分类准确度对比中，6种小样本的类别能够达到100%。实验结果表明，该方法充分提高了舵机异常检测的性能，是深度学习技术在舵机测试数据中的有效应用。

Model design and research on abnormal detection of steering gear

Aiming at the problem of large amount of the steering gear test data and unbalanced samples, an anomaly detection model is proposed that uses the Grey Wolf Optimization (GWO) to optimize the Deep Neural Networks (DNN) and combines it with the Logistic Regression Classification (GWO-DNN-LRC). The construction of the model effectively solves the problem that small samples in the steering gear test data are difficult to be accurately classified, and is suitable for the deep feature extraction and multi-fault classification of the steering gear test data. The accuracy of this method reaches 99.261%, which is 4.931%, 0.205%, and 0.087% higher than LRC, DNN, and GWO-DNN, respectively. The precision, recall, and F-score reach 98.417%, 98.062%, and 98.217%. In the comparison of classification accuracy of different categories, the categories of 6 small samples can reach 100%. Experimental results show that this method fully improves the performance of anomaly detection of steering gear, and is an effective application of deep learning technology in steering gear test data.