船舶异步电机远程故障诊断技术的研究
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摘要
异步电机是船舶动力及电力系统的重要支撑设备。当电机出现严重故障无法工作时,会相应地造成动力和电力系统的运行中止,直接威胁船舶航行的安全。为了对电机实现及时的维修及保养,建立异步电机的远程故障诊断系统,准确及时地实现电机的故障诊断是保证船舶安全航行的关键技术。
首先分析了基于互联网的远程故障诊断系统的体系结构及数据处理技术的特点,结合船舶设备的现状,设计了基于卫星通讯的船舶远程故障诊断系统的混合结构方案。采用基于网页的浏览器/服务器结构实现在线故障诊断,基于电子邮件的客户端/服务器结构实现离线故障诊断。通过比较分析现有的几种电机故障诊断方法的特点,选择采用具有非侵入式测量特点的电流特征分析法来提取电机的故障特征。
分析了船舶异步电机远程故障诊断过程中的短数据长度及非同步采样对频谱估计结果所造成的影响,同时考虑到船舶特殊运行环境下采样数据中包含的强噪声背景,以及早期故障特征频率较低功率谱的特点,分别研究了三种常用频谱估计方法在频率分辨过程中的特点。
提出了采用多窗谱分析实现电机早期故障的检测方法。研究了多窗谱分析法在频率分辨率与方差间的权衡问题,并确定了适用于远程故障诊断的最佳权衡值;针对频谱估计结果中的特征频率根部的泄漏问题,确定了以数据窗的能量作为选择依据的方式,消除了特征频率的根部泄漏,使特征频率易于识别。通过对实测故障数据的应用分析,并与三种常用频谱估计方法的应用结果相比较,验证了多窗谱分析方法具有稳定的频率分辨能力及较好的抗噪性能。
针对电机进入故障明显期的分类问题,提出了基于小波包变换的特征频率信息提取及分类方法。研究了电机负荷波动对分解系数的影响,以及小波包分解过程中基波泄漏到其他频率段的问题,通过仿真验证了采用希尔伯特变换对原始信号进行预处理的方法,可以较好地解决了分解系数变化及基波泄漏问题;研究了小波包分解子频段的频率分析范围,并与电流特征频率相对应;针对电流特征频率之间相隔25Hz的特点,考虑电机运行参数的影响,故障特征频率会出现最多十几赫兹的负偏差,提出对采样率为3200赫兹的原始信号进行6层小波包分解,使得每隔25赫兹的子频段宽度内可以覆盖一个特征频率,从而解决了电机运行参数的不确定性问题。研究了小波包分解子频段的频域混叠问题,通过仿真验证了对于小波增加其波峰数的方法可部分地减少混叠及频谱泄漏现象。通过比较节点以及节点重构系数的特点,采用子频段节点重构系数的均方根值变化率作为表征电机故障的特征指标。通过对实测故障数据的应用,验证了基于小波包变换的故障特征提取及分类方法的有效性。
针对远程故障诊断系统中逐渐积累的故障特征指标,提出了采用自组织特征映射网络以及粗糙集理论相结合的分类方法。研究了连续属性值的聚类量化算法,选择一次函数型学习速率实现网络学习过程中较慢的收敛速度,采用高斯函数作为近邻函数实现训练过程中邻域的改变。在构建输入样本时,利用相邻子频段的均方根值变化率组成一组学习样本的方法,减少了子频段之间混叠问题对于聚类结果的影响。利用粗糙集理论建立可分辨矩阵,并从中提取诊断规则。在实测故障数据的应用中验证了该方法的可行性。
Asynchronous motors are main support devices for both marine propulsion and power systems. Marine safety may be threatened if any motor in the propulsion and power systems is not function normally. To increase the system safety, we have to implement a timely repairing and maintaining system for motors. It is very important that the motor fault is timely detected based on the remote fault diagnosis system for marine motors.
This thesis firstly studied the characteristic of systematic frame and data processing for the remote fault diagnosis system based on Internet. In view of the open-sea environment for ocean ships, a hybrid frame of marine remote fault diagnosis system was introduced of performing both on-line and off-line. On-line fault diagnosis is implemented based on web which adopts browser/server frame communicating by INMARSAT. Off-line fault diagnosis is implemented based on Email which adopts client/server frame. The proposed fault diagnosis technique being compared with other fault diagnosis methods, the eigenvalue for motor fault diagnosis based on motor current signature analysis is achieved a better result, which measure circuit is non-aggressive.
We considered the data which supply to remote fault diagnosis system is short, unsynchronized and polluted with strong noise because of special marine operation environment. In addition, power spectral density of eigenfrequency during incipient fault period is low. For above problem, this thesis analysed the characteristic of three general spectral analyses methods using differently simulated data.
A detect method of motor incipient fault based on multi-taper method was introduced. A balance problem between frequency resolution and variance was studied, and the optimal balance value was chosen to be applied for remote fault diagnosis. By selecting high energy tapers, we eliminated root leakage of eigenfrequency, and brought the shape of eigenfrequency to be distinguishable. Experimental studies based on the data for artificial fault were conducted and results show that multi-taper method has a better steady and antinoise performance comparing with three other methods.
The eigenfrequency processing and fault classifying method based on Wavelet Packet Transform was introduced, aiming at classification for the outbreak fault. Preprocessing the motor current based on Hilbert transform was validated that has a better performance, aiming for avoiding leakage of supply frequency, and various decompose coefficient along with the vary of motor load. Frequency analysis range for every node of wavelet packet transform was studied, and be corresponded to range of current eigenfrequency. There is 25Hz interval between the two adjacent current eigenfrequencies, and actual value of current eigenfrequency presents a 2～15 Hz negative error because the slip is less than 1. Then, utilizing six-layer wavelet packets decompose for preprocessed signal, because that there was only one eigenfrequency in every bandwidth of wavelet packet decomposition, which solved the uncertainty problem of motor operation parameters. The overlap of frequency analysis range between the adjacent nodes was studied, and could be decreased by increasing the number of crests of wavelets. Simulation results show that the method is validated. The root mean square (RMS) method for reconstructed node coefficient that presents motor fault is more feasible than the RMS for node coefficient. Experiments were conducted using the data for artificial faults and results show that fault classifying method based on Wavelet Packet Transform is valid.
A classify method combining rough set theory with self organizing feature map, was introduced aiming at accumulated fault information. The clustered problem for continuous attribution was studied; slow convergent speed could be achieved by using proportion function. During the training phase, neighborhood region was changed based that Gaussian function was chosen as neighborhood function. The disturbance owing to wavelet overlap was effectively decreased, by using training samples composed with two mean-squared roots for the adjacent nodes. The discernibility matrix was constructed based on rough set theory. The diagnosis rules were extracted from the discernibility matrix. Application studies based on the data for artificial fault were conducted and results show that the above method is feasible.
首先分析了基于互联网的远程故障诊断系统的体系结构及数据处理技术的特点,结合船舶设备的现状,设计了基于卫星通讯的船舶远程故障诊断系统的混合结构方案。采用基于网页的浏览器/服务器结构实现在线故障诊断,基于电子邮件的客户端/服务器结构实现离线故障诊断。通过比较分析现有的几种电机故障诊断方法的特点,选择采用具有非侵入式测量特点的电流特征分析法来提取电机的故障特征。
分析了船舶异步电机远程故障诊断过程中的短数据长度及非同步采样对频谱估计结果所造成的影响,同时考虑到船舶特殊运行环境下采样数据中包含的强噪声背景,以及早期故障特征频率较低功率谱的特点,分别研究了三种常用频谱估计方法在频率分辨过程中的特点。
提出了采用多窗谱分析实现电机早期故障的检测方法。研究了多窗谱分析法在频率分辨率与方差间的权衡问题,并确定了适用于远程故障诊断的最佳权衡值;针对频谱估计结果中的特征频率根部的泄漏问题,确定了以数据窗的能量作为选择依据的方式,消除了特征频率的根部泄漏,使特征频率易于识别。通过对实测故障数据的应用分析,并与三种常用频谱估计方法的应用结果相比较,验证了多窗谱分析方法具有稳定的频率分辨能力及较好的抗噪性能。
针对电机进入故障明显期的分类问题,提出了基于小波包变换的特征频率信息提取及分类方法。研究了电机负荷波动对分解系数的影响,以及小波包分解过程中基波泄漏到其他频率段的问题,通过仿真验证了采用希尔伯特变换对原始信号进行预处理的方法,可以较好地解决了分解系数变化及基波泄漏问题;研究了小波包分解子频段的频率分析范围,并与电流特征频率相对应;针对电流特征频率之间相隔25Hz的特点,考虑电机运行参数的影响,故障特征频率会出现最多十几赫兹的负偏差,提出对采样率为3200赫兹的原始信号进行6层小波包分解,使得每隔25赫兹的子频段宽度内可以覆盖一个特征频率,从而解决了电机运行参数的不确定性问题。研究了小波包分解子频段的频域混叠问题,通过仿真验证了对于小波增加其波峰数的方法可部分地减少混叠及频谱泄漏现象。通过比较节点以及节点重构系数的特点,采用子频段节点重构系数的均方根值变化率作为表征电机故障的特征指标。通过对实测故障数据的应用,验证了基于小波包变换的故障特征提取及分类方法的有效性。
针对远程故障诊断系统中逐渐积累的故障特征指标,提出了采用自组织特征映射网络以及粗糙集理论相结合的分类方法。研究了连续属性值的聚类量化算法,选择一次函数型学习速率实现网络学习过程中较慢的收敛速度,采用高斯函数作为近邻函数实现训练过程中邻域的改变。在构建输入样本时,利用相邻子频段的均方根值变化率组成一组学习样本的方法,减少了子频段之间混叠问题对于聚类结果的影响。利用粗糙集理论建立可分辨矩阵,并从中提取诊断规则。在实测故障数据的应用中验证了该方法的可行性。
Asynchronous motors are main support devices for both marine propulsion and power systems. Marine safety may be threatened if any motor in the propulsion and power systems is not function normally. To increase the system safety, we have to implement a timely repairing and maintaining system for motors. It is very important that the motor fault is timely detected based on the remote fault diagnosis system for marine motors.
This thesis firstly studied the characteristic of systematic frame and data processing for the remote fault diagnosis system based on Internet. In view of the open-sea environment for ocean ships, a hybrid frame of marine remote fault diagnosis system was introduced of performing both on-line and off-line. On-line fault diagnosis is implemented based on web which adopts browser/server frame communicating by INMARSAT. Off-line fault diagnosis is implemented based on Email which adopts client/server frame. The proposed fault diagnosis technique being compared with other fault diagnosis methods, the eigenvalue for motor fault diagnosis based on motor current signature analysis is achieved a better result, which measure circuit is non-aggressive.
We considered the data which supply to remote fault diagnosis system is short, unsynchronized and polluted with strong noise because of special marine operation environment. In addition, power spectral density of eigenfrequency during incipient fault period is low. For above problem, this thesis analysed the characteristic of three general spectral analyses methods using differently simulated data.
A detect method of motor incipient fault based on multi-taper method was introduced. A balance problem between frequency resolution and variance was studied, and the optimal balance value was chosen to be applied for remote fault diagnosis. By selecting high energy tapers, we eliminated root leakage of eigenfrequency, and brought the shape of eigenfrequency to be distinguishable. Experimental studies based on the data for artificial fault were conducted and results show that multi-taper method has a better steady and antinoise performance comparing with three other methods.
The eigenfrequency processing and fault classifying method based on Wavelet Packet Transform was introduced, aiming at classification for the outbreak fault. Preprocessing the motor current based on Hilbert transform was validated that has a better performance, aiming for avoiding leakage of supply frequency, and various decompose coefficient along with the vary of motor load. Frequency analysis range for every node of wavelet packet transform was studied, and be corresponded to range of current eigenfrequency. There is 25Hz interval between the two adjacent current eigenfrequencies, and actual value of current eigenfrequency presents a 2～15 Hz negative error because the slip is less than 1. Then, utilizing six-layer wavelet packets decompose for preprocessed signal, because that there was only one eigenfrequency in every bandwidth of wavelet packet decomposition, which solved the uncertainty problem of motor operation parameters. The overlap of frequency analysis range between the adjacent nodes was studied, and could be decreased by increasing the number of crests of wavelets. Simulation results show that the method is validated. The root mean square (RMS) method for reconstructed node coefficient that presents motor fault is more feasible than the RMS for node coefficient. Experiments were conducted using the data for artificial faults and results show that fault classifying method based on Wavelet Packet Transform is valid.
A classify method combining rough set theory with self organizing feature map, was introduced aiming at accumulated fault information. The clustered problem for continuous attribution was studied; slow convergent speed could be achieved by using proportion function. During the training phase, neighborhood region was changed based that Gaussian function was chosen as neighborhood function. The disturbance owing to wavelet overlap was effectively decreased, by using training samples composed with two mean-squared roots for the adjacent nodes. The discernibility matrix was constructed based on rough set theory. The diagnosis rules were extracted from the discernibility matrix. Application studies based on the data for artificial fault were conducted and results show that the above method is feasible.
引文
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