CN113447815A - 一种基于实值esprit的电机故障在线检测方法及系统 - Google Patents
一种基于实值esprit的电机故障在线检测方法及系统 Download PDFInfo
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Abstract
本发明公开了一种基于实值ESPRIT的电机故障在线检测方法及系统,属于电机故障检测技术领域,包括步骤1:采集电机定子电流数据i(n)。步骤2:利用Hilbert变换构造解析信号y(n)。步骤3:获取电流信号矩阵Y。步骤4:定义实值化矩阵QM,构造实值信号矩阵Y。步骤5:计算信号子空间的Us,利用Us构造矩阵H。步骤6:奇异值分解H,并根据求得的特征值估计信号频率。步骤7:判断电机是否发生故障。本发明与现有方法相比,可以显著降低计算量,更快发现电机故障,具有高可靠性和高灵敏性等优点。
Description
技术领域
本发明属于电机诊断技术领域,涉及一种能够在线检测电动机故障的方法,具体地说是一种基于实值ESPRIT(real-valued estimation of signal parameters viarotational invariance techniques)的电机故障在线检测方法及系统。
背景技术
近年来,异步电动机在各个工业领域的广泛应用,已成为现代工业生产中重要的劳动保障和节能手段。为了保障电机健康、稳定运行,尽可能减少维修成本和停机时间,电机故障诊断具有重要意义。故障诊断可以通过观察电机的振动、电流、磁场等多项指标来实现。其中,基于定子电流的方法不需要特定的数据采集设备和额外的传感器,并且在电机运行期间更容易采集到信号。大量研究表明,当异步电动机发生故障时,定子电流频谱中会出现额外的频率分量,这些分量可以作为电机故障检测的指标。因此,定子电流频谱分析方法以其简单易行、成本低廉、可靠性高等优点受到了广泛关注。其中最经典的定子电流频谱分析方法是FFT(fast Fourier transform),但该方法强烈依赖FFT的分辨率。分辨率与时间成反比,而较长的测量时间会导致电流的变化,从而影响故障诊断结果。为了在短时间内获得高分辨率,子空间的方法被提出。例如在Trachi,et al."Induction Machines FaultDetection Based on Subspace Spectral Estimation."IEEE Transactions onIndustrial Electronics中提出了基于ESPRIT的电机故障诊断方法,但是该方法在实际应用过程中存在计算量过大的问题。
发明内容
针对现有方法的不足,利用短时间的电流信号,本发明提出一种基于实值ESPRIT的电机故障在线检测方法。
用于实现本发明的技术解决方案包括如下步骤:
步骤1:采集电机定子电流数据i(n)。
步骤2:利用Hilbert变换构造解析信号y(n)。
步骤3:获取电流信号矩阵Y。
步骤4:定义实值化矩阵QM,构造实值信号矩阵Y。
步骤5:计算信号子空间的Us,利用Us构造矩阵H。
步骤6:奇异值分解H,并根据求得的特征值估计信号频率。
步骤7:判断电机是否发生故障。
本发明还提出了实现上述一种基于实值ESPRIT的电机故障在线检测方法的检测系统,包括信号采集器和信息处理器;所述信号采集器用于采集电机定子电流,所述信息处理器内部集成上述步骤1-7的算法,在接收到电机定子电流后,根据集成的算法判断电机是否出现故障。
本发明的有益效果:
本发明与现有方法相比,可以显著降低计算量,更快发现电机故障,具有高可靠性和高灵敏性等优点。
附图说明
图1是本发明实施流程图。
图2(a)和图2(b)分别是相同条件下,一般ESPRIT与基于实值ESPRIT方法的电机故障频率检测图。
具体实施方式
下面结合附图对本发明作进一步说明。
如图1所示,本发明实施的步骤如下:
(2)构造解析信号y(n)=i(n)+jHT[i(n)],其中:HT[·]表示Hilbert变换,j表示虚数单位。
(3)设定窗口长度为M,重新排列解析信号,获得M×G维的电流信号矩阵Y=[y(0),y(1),...,y(G-1)],其中:
Λs为L个大奇异值构成的对角矩阵,Us是与Λs对应的左奇异向量构成的矩阵,Vs是与Λs对应的右奇异向量构成的矩阵,Λw为2G-L个小奇异值构成的对角矩阵,Uw是与Λw对应的左奇异向量构成的矩阵,Vw是与Λw对应的右奇异向量构成的矩阵,所述大奇异值和小奇异值的定义:奇异值分解之后,对角矩阵里的奇异值按照由大到小排序,前L个为大奇异值,其余为小奇异值;
(6)H的奇异值分解记为H=TΣPT,并将P划分为四个维度为L×L的子矩阵:
(7)查询估计频率中是否包括故障频率,若包括则判断电机出现故障,反之判断电机未出现故障。
上述电流信号采集由检测系统的信号采集器(比如电流传感器)实现,步骤(2)-(7)由检测系统的信息处理器(比如单片机)集成的算法实现。
实验条件
实验电机是额定功率3kW、额定电压380V、额定电流6.8A的鼠笼电机,在25%负载情况下,带有3根转子断条故障。本发明选取N=1000个样本点,窗口长度M=700,采样频率为Fs=1kHz,谐波个数L=20,进行电机故障检测,仿真结果如图2所示。
实验分析
根据表1可知:发生故障时,其定子电流频谱图中会出现(1±2s)f的故障频率分量,其中s为电机转差率,f为供电频率,本实验故障特征频率理论值为48.7Hz与51.3Hz。
表1是电机测试状态和对应的故障特征理论值
根据图2可以看出:基于一般ESPRIT和实值的ESPRIT方法分别对1000个数据进行了频谱分析,实验结果与理论值基本一致,供电侧电流频率f=50Hz两侧出现边频带。但一般ESPRIT方法检测出来是48.9Hz和51.49Hz,而本发明估计的电机故障频率为48.78Hz和51.29Hz,其准确性能明显优于现有方法。
上文所列出的一系列的详细说明仅仅是针对本发明的可行性实施方式的具体说明,它们并非用以限制本发明的保护范围,凡未脱离本发明技术所创的等效方式或变更均应包含在本发明的保护范围之内。
Claims (9)
1.一种基于实值ESPRIT的电机故障在线检测方法,其特征在于,包括如下步骤:
步骤1:采集电机定子电流数据i(n);
步骤2:根据Hilbert变换,使用电机定子电流数据i(n)构造解析信号y(n);
步骤3:根据解析信号y(n)构造电流信号矩阵Y;
步骤4:定义实值化矩阵QM,构造实值信号矩阵Y;
步骤5:计算电流信号子空间的Us,利用Us构造矩阵H;
步骤6:奇异值分解H,并根据求得的特征值估计电流信号频率;
步骤7:根据估计的电流信号频率判断电机是否发生故障。
3.根据权利要求1所述的一种基于实值ESPRIT的电机故障在线检测方法,其特征在于,所述步骤2的y(n)的表达式为:y(n)=i(n)+jHT[i(n)],其中:HT[·]表示Hilbert变换,j表示虚数单位。
8.根据权利要求5所述的一种基于实值ESPRIT的电机故障在线检测方法,其特征在于,所述步骤7的实现包括如下:
查询估计频率中是否包括故障频率,若包括则判断电机出现故障,反之判断电机未出现故障。
9.一种基于实值ESPRIT的电机故障在线检测系统,其特征在于,包括信号采集器和信息处理器;所述信号采集器用于采集电机定子电流,所述信息处理器内部集成权利要求1-8的算法,在接收到电机定子电流后,根据集成的算法判断电机是否出现故障。
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