用隐Markov模型的陀螺电机故障诊断方法（英文）  

Failure detection and diagnosis of gyro motors using hidden Markov models

作　　者：董磊[1,2] 李德才[2] 韦俊新[2] 李为民[1] 潘龙飞[2] 孙晓晋 陈云飞[3] 

机构地区：[1]河北工业大学机械工程学院,天津300130 [2]天津航海仪器研究所,天津300131 [3]河北工业大学控制科学与工程学院,天津300130

出　　处：《中国惯性技术学报》2014年第6期829-833,共5页Journal of Chinese Inertial Technology

基　　金：装备预研支撑技术项目(62101050802);国防预先研究重点项目(513090501)

摘　　要：为满足机电陀螺仪高可靠性的要求,准确地检测和诊断陀螺仪核心部件——陀螺电机的各类故障是十分必要的。提出了一种陀螺电机检测和诊断的新方法,即基于隐Markov模型的模式识别方法。该方法从母线电流时域信号提取特征并作为电机状态的监测指标,通过顺序后推法选择最佳信号特征建立特征空间,并用于隐Markov模型的参数训练,进而使用隐Markov模型作为分类器对陀螺电机进行故障检测和诊断。为验证方法的有效性,用一台无刷直流陀螺电机作为样本进行了实验,构造了轴承故障和定子故障,并在不同的温度条件下进行了测试。实验结果表明:该方法对于陀螺电机故障检测和诊断的正确率达到96.8%。To meet the reliability requirement of electro mechanical gyroscopes, a new method for accurately detecting and diagnosing the faults of gyro motors is presented, which is a pattern recognition method based on hidden markov model (HMM) and uses time domain features extracted from the bus current signals as health indicators. By using a sequential backward selection (SBS) method, the best features are selected to build the representation space and train the parameters of HMM. Then the HMMs are used as classifier for failure detection and diagnosis. The proposed method has been tested on a brushless DC gyro motor to detect bearing faults and stator faults at different temperature levels. The experimental results show that the accuracy of the proposed method is 96.8% for failure detection and diagnosis of gyro motors. ©, 2014, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.

关 键 词：故障检测 故障诊断 隐MARKOV模型 陀螺电机 

分 类 号：TH165.3[机械工程—机械制造及自动化]