融合TKEO和Goerztel算法的感应电机转子断条故障诊断

Fault Diagnosis of Broken Rotor Bar in Induction Motor Based on TKEO and Goertzel Algorithm

针对电机电流信号特征分析进行转子断条故障诊断时,在低负载工况或早期,转子断条故障存在故障特征微弱以及难以分离等问题,同时需借助速度传感器获取转速信息,增加了硬件成本,该文提出了融合Teager-Kaiser能量算子(TKEO)和Goertzel算法的感应电机转子断条故障诊断方法。首先,采用Teager-Kaiser能量算子对定子电流进行解调处理,将基频分量转化为直流分量并去除,实现边带故障特征分量到转子断条故障特征分量以及固有偏心谐波分量到转子速度谐波分量的转变,抑制基频频谱泄露对故障诊断和转速估计造成的干扰;其次,利用Goertzel算法对归一化解调信号进行频谱分析,完成转子断条故障特征分量和转子速度谐波分量的快速提取,进而实现不同负载和不同严重程度工况下转子断条的故障诊断。此外,与归一化频域能量算子进行比较,分析其计算效率和稳定性。实验结果表明,所提方法在不同负载和不同严重程度工况下均能够有效地识别转子断条故障特征分量,并且计算量少、可靠性高、实时性强以及硬件成本低。

As an important energy conversion device,induction motors(IM)are widely used in electric vehicles,industrial machinery,and aerospace appliances.Although broken rotor bar(BRB)faults only account for about 10%of total IM failures,fault-related features are often submerged with strong background noise,which makes it difficult to detect at an early stage.Compared to other signals that indicate the condition of the IM,such as voltage,vibration,airgap flux,and acoustic emission,the current signal is low-cost,non-invasive,and simple to acquire.However,extracting fault components at low-load operating conditions is challenging for BRB detection using motor current signature analysis(MCSA).Furthermore,conventional approaches of MCSA fail to estimate the rotor speed and compute a small number of selected frequency components.This paper proposes a BRB fault diagnosis method for an IM based on Teager-Kaiser Energy Operator(TKEO)demodulation and the Goertzel algorithm.Firstly,based on the sideband frequency components caused by the BRB fault and the inherent eccentric harmonic component in the current signal,the demodulation performance of TKEO and frequency domain energy operator(FDEO)are analyzed.With the two demodulation methods,the supply frequency component is converted into a DC component and removed.Meanwhile,the modulation sideband components and the inherent eccentric harmonic components are transformed into the BRB fault characteristic and the rotor speed harmonic components.Therefore,the interference caused by the spectrum leakage of supply frequency components to the fault diagnosis and the speed estimation is suppressed.Then,to reduce the computational complexity,the Goertzel algorithm is used to compute a small number of selected frequency components of the normalized demodulated signal.The fast extraction of the BRB fault characteristic component and rotor speed harmonic component is realized.Finally,the theoretical value of the BRB fault characteristic component is calculated using the estimated rotor speed harmonic component.By comparing the frequencies and amplitudes of the detection and theoretical BRB fault characteristic components,the conclusion can be determined whether the BRB fault occurs.The effectiveness of the theoretical analysis and the proposed method is validated on the experimental database of Western Parana State University.Even if the severity of the BRB fault is relatively high,due to the spectrum leakage of the supply frequency component,the motor current signal cannot detect the BRB fault characteristic component under light load conditions.The performance of the proposed method and the FDEO under different loads and severity conditions are presented in terms of their efficiency and stability.Although both methods can suppress the spectrum leakage,comparative experiments demonstrate that the FDEO method can only identify the BRB fault characteristic components under several load cases,which is susceptible to noise interference.The proposed method can effectively recognize the BRB fault characteristic components under different loads and severity conditions with less calculation,high reliability,solid real-time performance,and low hardware cost.