基于小波神经网络的异步电机故障诊断
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摘要
作为传动机械,异步电动机被广泛运用于发电厂、炼钢厂、海军舰艇等工业生产与国防领域,其安全运行至关重要。异步电动机故障检测,特别是初发故障检测是保障异步电动机安全运行的关键措施之一。因此防止故障发生,减少维修支出就显得尤为重要和迫切。
本文首先对异步电动机常见的四种故障(转子断条、轴承故障、定子匝间短路和气隙偏心)的故障机理进行了深入的分析,选取定子电流信号作为故障特征信号,推出信号故障特征频率分量。
由于小波分析具有时频局部化功能,能将信号分解到各个不同的频率段。因此,本文先采用小波分解对模拟的包含有转子断条故障信息的定子电流信号进行消噪处理,再用小波包将信号分解,并将故障所在的频率段进行信号重构,最后用FFT变换求出其功率谱。仿真结果表明:采用小波变换能将故障频率成功的提取出来,消除了泄露的基波频率分量对故障频率的淹没影响,而直接的FFT变换则不能检测出转子断条故障频率分量。该方法验证了小波分析强大的信号处理功能,是故障特征向量提取的好工具。
根据故障特征向量与故障模式之间的映射关系,采用小波神经网络建立了故障诊断模型。该模型采用最速梯度下降算法,用动量法和自适应学习速率相结合来优化,并对小波神经网络参数进行了初始化研究。仿真结果表明:与常规小波神经网络和经过各种优化的BP网络相比,该模型明显缩短了训练时间,且同时又有好的精确度。因此,将该方法用于异步电动机故障诊断是有效的。
As the transmission machinery, asynchronous motors are widely used in industrial production and defense field, such as power plants, steel mills, naval vessels, so its safe operation is essential. Asynchronous motor fault detection, especially the initial issuance of the fault detection is one of the key measures which protect the safe operation of asynchronous motors. Therefore, it is important and urgent to prevent the fault from occurring and reduce maintenance expenditure.
The paper analyzes faults mechanism for four types of common faults of AC motor. The faults are shown as follows: inter-turn short circuits of stator windings, rotor bar broken, rotor eccentricity and the bearing fault. This paper selects the stator current signal as the signal of motor faults and induces the fault characteristic frequency .
Wavelet analysis has the character of time-frequency localization and can decompose the signal into different frequency bands. So the paper uses the wavelet decompose to eliminate signal noise of stator current signal which contains information of the broken rotor bar fault, and uses the wavelet packet to decompose the signal, and then reconstructs the wavelet coefficients. At last the paper uses the FFT to compute the power spectrum. The simulation results show that, wavelet transform has successfully extracted the fault frequency, and it can eliminates influences of the fundamental frequency components covering the fault frequency. But the method that uses the FFT can not detect broken rotor bar fault frequencies. It is proved that the wavelet analysis method is a powerful signal processing and it is a good tool to extract the fault feature vector.
The paper uses the wavelet neural network (WNN) to establish the asynchronous motor fault diagnosis model according to the mapping relationship between the common symptoms of fault in the asynchronous motor and fault mode. The model adopts the conjugate gradient descent algorithm, which is optimized by the momentum and adaptive learning rate. The initialization of parameters of the WNN is also analyzed in the paper. It is shown from the simulation results that, compared with conventional wavelet neural network and BP network, this model significantly reduces the training time and is valid for motor fault diagnosis.
本文首先对异步电动机常见的四种故障(转子断条、轴承故障、定子匝间短路和气隙偏心)的故障机理进行了深入的分析,选取定子电流信号作为故障特征信号,推出信号故障特征频率分量。
由于小波分析具有时频局部化功能,能将信号分解到各个不同的频率段。因此,本文先采用小波分解对模拟的包含有转子断条故障信息的定子电流信号进行消噪处理,再用小波包将信号分解,并将故障所在的频率段进行信号重构,最后用FFT变换求出其功率谱。仿真结果表明:采用小波变换能将故障频率成功的提取出来,消除了泄露的基波频率分量对故障频率的淹没影响,而直接的FFT变换则不能检测出转子断条故障频率分量。该方法验证了小波分析强大的信号处理功能,是故障特征向量提取的好工具。
根据故障特征向量与故障模式之间的映射关系,采用小波神经网络建立了故障诊断模型。该模型采用最速梯度下降算法,用动量法和自适应学习速率相结合来优化,并对小波神经网络参数进行了初始化研究。仿真结果表明:与常规小波神经网络和经过各种优化的BP网络相比,该模型明显缩短了训练时间,且同时又有好的精确度。因此,将该方法用于异步电动机故障诊断是有效的。
As the transmission machinery, asynchronous motors are widely used in industrial production and defense field, such as power plants, steel mills, naval vessels, so its safe operation is essential. Asynchronous motor fault detection, especially the initial issuance of the fault detection is one of the key measures which protect the safe operation of asynchronous motors. Therefore, it is important and urgent to prevent the fault from occurring and reduce maintenance expenditure.
The paper analyzes faults mechanism for four types of common faults of AC motor. The faults are shown as follows: inter-turn short circuits of stator windings, rotor bar broken, rotor eccentricity and the bearing fault. This paper selects the stator current signal as the signal of motor faults and induces the fault characteristic frequency .
Wavelet analysis has the character of time-frequency localization and can decompose the signal into different frequency bands. So the paper uses the wavelet decompose to eliminate signal noise of stator current signal which contains information of the broken rotor bar fault, and uses the wavelet packet to decompose the signal, and then reconstructs the wavelet coefficients. At last the paper uses the FFT to compute the power spectrum. The simulation results show that, wavelet transform has successfully extracted the fault frequency, and it can eliminates influences of the fundamental frequency components covering the fault frequency. But the method that uses the FFT can not detect broken rotor bar fault frequencies. It is proved that the wavelet analysis method is a powerful signal processing and it is a good tool to extract the fault feature vector.
The paper uses the wavelet neural network (WNN) to establish the asynchronous motor fault diagnosis model according to the mapping relationship between the common symptoms of fault in the asynchronous motor and fault mode. The model adopts the conjugate gradient descent algorithm, which is optimized by the momentum and adaptive learning rate. The initialization of parameters of the WNN is also analyzed in the paper. It is shown from the simulation results that, compared with conventional wavelet neural network and BP network, this model significantly reduces the training time and is valid for motor fault diagnosis.
引文
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[2]虞和济,故障诊断的基本原理[M].北京:冶金工业出版社,1989.
[3] Mohamed El Hachemi Benbouzid. A review of induction motors signature analysis as a medium for faults detection[J ]. IEEE Trans. on Industrial Electronics, 2000, 47(5): 984-993.
[4] Olaf Moseler and Rolf Isermann. Model-based fault detection for abrushless DC motor using parameter estimation[C ]. Proceedings from IEEE Industrial Electronics Society, 1998, 4: 1956 -1960.
[5] Subhasis Nandi, Hamid A.Toliyat. Fault diagnosis of electrical machines-a review[C ]. Proceedings from Electric Machines and Drives, 1999:219 -221.
[6] Kenneth A.Loparo, M.L.Adams and M.Farouk Abdel-Magied,et al. Fault detection and diagnosis of rotating machinery[J ].IEEE Trans. on Industrial Electronics, 2000, 47(5): 1005-1014
[7] Olaf Moseler and Rolf Isermann. Model-based fault detection for abrushless DC motor using parameter estimation[C ]. Proceedings from IEEE Industrial Electronics Society, 1998, 4: 1956 -1960.
[8] Olaf Moseler and Rolf Isermann. Application of model-based fault detection to a brushless DC motor[J ]. IEEE Trans. on Industrial Electronics, 2000,47(5):1015-1020.
[9]沈艳霞,纪志成,姜建国.电机故障诊断的人工智能方法综述[J].微特电机,2004,2(2):39-42.
[10]陈凯,许伯强,李和明,等.基于小波变换的改进LMS算法在异步电动机轴承故障诊断中应用[J].电力自动化设备,2008,28(9):77-81.
[11]刘振兴,尹项根,张哲.基于Hilbert模量频谱分析的异步电动机转子故障在线监测与诊断方法[J].中国电机工程学报,2003,23(7):158-161.
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