基于改进开关卡尔曼滤波的轴承故障特征提取方法
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摘要
提出了一种基于改进开关卡尔曼滤波的滚动轴承故障特征提取新方法,与传统卡尔曼滤波算法相比,该方法每次迭代只需当前监测数据测量值和上一时刻最优估计值,计算效率高,具有较强实时性。首先将故障轴承振动信号分为故障冲击振动和正常振动两种成分;其次,针对故障冲击振动和正常振动两种状态,分别建立基于轴承质量-弹簧-阻尼系统动力学脉冲响应的卡尔曼滤波器及线性卡尔曼滤波器模型;然后,应用基于贝叶斯估计的开关卡尔曼滤波算法对振动信号进行状态估计;最终,通过时域迭代滤波,滤除噪声并识别故障冲击成分,实现轴承故障特征提取。仿真和试验信号分析结果表明了所提方法的可行性和有效性。
A new method of fault feature extraction for rolling bearings based on improved switching Kalman filter is proposed. Compared with the traditional Kalman filter algorithm, this method only needs the current measurement value and the optimal estimation of the previous moment in each iteration, so it has high computational efficiency and strong real-time performance. Firstly, the vibration signals of fault bearings are divided into two parts: Fault impulse vibration and normal vibration. Secondly, the Kalman filter model based on the dynamic impulse response of the bearing mass-spring-damper system and the linear Kalman filter model are established respectively for the fault impulse vibration and the normal vibration. Then, the state estimation of vibration signals is carried out by using the switching Kalman filter algorithm based on Bayesian estimation. Finally, the bearing fault feature extraction is realized by filtering noise and identifying fault impulse components through time domain iteration filtering. The simulation and experimental results show the feasibility and effectiveness of the proposed method.
A new method of fault feature extraction for rolling bearings based on improved switching Kalman filter is proposed. Compared with the traditional Kalman filter algorithm, this method only needs the current measurement value and the optimal estimation of the previous moment in each iteration, so it has high computational efficiency and strong real-time performance. Firstly, the vibration signals of fault bearings are divided into two parts: Fault impulse vibration and normal vibration. Secondly, the Kalman filter model based on the dynamic impulse response of the bearing mass-spring-damper system and the linear Kalman filter model are established respectively for the fault impulse vibration and the normal vibration. Then, the state estimation of vibration signals is carried out by using the switching Kalman filter algorithm based on Bayesian estimation. Finally, the bearing fault feature extraction is realized by filtering noise and identifying fault impulse components through time domain iteration filtering. The simulation and experimental results show the feasibility and effectiveness of the proposed method.
引文
[1]雷亚国,贾峰,孔德同,等.大数据下机械智能故障诊断的机遇与挑战[J].机械工程学报,2018,54(5):94-104.LEI Yaguo,JIA Feng,KONG Detong,et al.Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J].Journal of Mechanical Engineering,2018,54(5):94-104.
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[3]贺王鹏,訾艳阳,陈彬强,等.周期稀疏导向超小波在风力发电设备发电机轴承故障诊断中的应用[J].机械工程学报,2016,52(3):41-48.HE Wangpeng,ZI Yanyang,CHEN Binqiang,et al.Periodic sparsity oriented super-wavelet analysis with application to motor bearing fault detection of wind turbine[J].Journal of Mechanical Engineering,2016,52(3):41-48.
[4]CUI Lingli,WU Na,MA Chunqing,et al.Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary[J].Mechanical Systems and Signal Processing,2016,68-69:34-43.
[5]CUI Lingli,MA Chunqing,ZHANG Feibin,et al.Quantitative diagnosis of fault severity trend of rolling element bearings[J].Chinese Journal of Mechanical Engineering,2015,28(6):1254-1260.
[6]JIANG Guoqian,XIE Ping,WANG Xiao,et al.Intelligent fault diagnosis of rotary machinery based on unsupervised multiscale representation learning[J].Chinese Journal of Mechanical Engineering,2017,30(6):1314-1324.
[7]刘东东,程卫东,万广通.基于故障特征趋势线模板的滚动轴承故障诊断[J].机械工程学报,2017,53(9):83-91.LIU Dongdong,CHENG Weidong,WAN Guangtong.Bearing fault diagnosis based on fault characteristic trend template[J].Journal of Mechanical Engineering,2017,53(9):83-91.
[8]AHMED H O A,WONG M L D,NANDI A K.Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features[J].Mechanical Systems&Signal Processing,2018,99:459-477.
[9]SONG Liuyang,WANG Huaqing,CHEN Peng.Step-by-step fuzzy diagnosis method for equipment based on symptom extraction and trivalent logic fuzzy diagnosis theory[J].IEEE Transactions on Fuzzy Systems,2018,doi:10.1109/TFUZZ.2018.2833820.
[10]SONG Liuyang,WANG Huaqing,CHEN Peng.Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery[J].IEEETransactions on Instrumentation and Measurement,2018,67(8):1887-1899.
[11]KALMAN R E.A new approach to linear filtering and prediction problems[J].Journal of Basic Engineering Transactions,1960,82:35-45.
[12]KHANAM S,TANDON N,DUTT J K.Fault identification of rolling element bearings from vibration signals:An application of Kalman and H∞Filters[C]//10th International Conference on Vibrations in Rotating Machinery,September 11-13,2012,Woodhead,London:IMechE,2012:703-713.
[13]KHANAM S,DUTT J K,TANDON N.Extracting rolling element bearing faults from noisy vibration signal using Kalman filter[J].Journal of Vibration&Acoustics,2014,136(3):031008.
[14]KHANAM S,TANDON N,DUTT J K.A system dynamic approach to bearing fault identification with the application of Kalman and H-infinity filters[J].Journal of Vibration&Control,2014,22(13):3022-3056.
[15]MURPHY K P.Switching Kalman filters[J].International Journal of Control,1998,75(16):1269-1281.
[2]CUI Lingli,HUANG Jinfeng,ZHANG Feibin.Quantitative and localization diagnosis of a defective ball bearing based on vertical-horizontal synchronization signal analysis[J].IEEE Transactions on Industrial Electronics,2017,64(11):8695-8705.
[3]贺王鹏,訾艳阳,陈彬强,等.周期稀疏导向超小波在风力发电设备发电机轴承故障诊断中的应用[J].机械工程学报,2016,52(3):41-48.HE Wangpeng,ZI Yanyang,CHEN Binqiang,et al.Periodic sparsity oriented super-wavelet analysis with application to motor bearing fault detection of wind turbine[J].Journal of Mechanical Engineering,2016,52(3):41-48.
[4]CUI Lingli,WU Na,MA Chunqing,et al.Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary[J].Mechanical Systems and Signal Processing,2016,68-69:34-43.
[5]CUI Lingli,MA Chunqing,ZHANG Feibin,et al.Quantitative diagnosis of fault severity trend of rolling element bearings[J].Chinese Journal of Mechanical Engineering,2015,28(6):1254-1260.
[6]JIANG Guoqian,XIE Ping,WANG Xiao,et al.Intelligent fault diagnosis of rotary machinery based on unsupervised multiscale representation learning[J].Chinese Journal of Mechanical Engineering,2017,30(6):1314-1324.
[7]刘东东,程卫东,万广通.基于故障特征趋势线模板的滚动轴承故障诊断[J].机械工程学报,2017,53(9):83-91.LIU Dongdong,CHENG Weidong,WAN Guangtong.Bearing fault diagnosis based on fault characteristic trend template[J].Journal of Mechanical Engineering,2017,53(9):83-91.
[8]AHMED H O A,WONG M L D,NANDI A K.Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features[J].Mechanical Systems&Signal Processing,2018,99:459-477.
[9]SONG Liuyang,WANG Huaqing,CHEN Peng.Step-by-step fuzzy diagnosis method for equipment based on symptom extraction and trivalent logic fuzzy diagnosis theory[J].IEEE Transactions on Fuzzy Systems,2018,doi:10.1109/TFUZZ.2018.2833820.
[10]SONG Liuyang,WANG Huaqing,CHEN Peng.Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery[J].IEEETransactions on Instrumentation and Measurement,2018,67(8):1887-1899.
[11]KALMAN R E.A new approach to linear filtering and prediction problems[J].Journal of Basic Engineering Transactions,1960,82:35-45.
[12]KHANAM S,TANDON N,DUTT J K.Fault identification of rolling element bearings from vibration signals:An application of Kalman and H∞Filters[C]//10th International Conference on Vibrations in Rotating Machinery,September 11-13,2012,Woodhead,London:IMechE,2012:703-713.
[13]KHANAM S,DUTT J K,TANDON N.Extracting rolling element bearing faults from noisy vibration signal using Kalman filter[J].Journal of Vibration&Acoustics,2014,136(3):031008.
[14]KHANAM S,TANDON N,DUTT J K.A system dynamic approach to bearing fault identification with the application of Kalman and H-infinity filters[J].Journal of Vibration&Control,2014,22(13):3022-3056.
[15]MURPHY K P.Switching Kalman filters[J].International Journal of Control,1998,75(16):1269-1281.