基于电机电流分析的万能式断路器机械故障诊断

电机电流信号常用于分析电动机本身的故障问题,但对其应用于与电机相连机构的故障分析的研究较少。提出一种基于储能电机电流分析的万能式断路器操作机构故障诊断方法。首先采用Hilbert幅值解调法和改进的小波包阈值法相结合获取交流电流信号的包络线,以解决随机噪声干扰造成的所提取包络线粗糙的问题;然后通过包络线提取电流信号的时间量、电流量以及峭度作为不同故障状态电流波形的特征参数;最后融合模糊聚类和量子粒子群优化的相关向量机实现对断路器正常状态、传动齿轮卡涩、储能弹簧卡涩以及脱落的4种状态的辨识。构建了基于电流分析的万能式断路器故障诊断系统,在不同工况下进行了验证,结果表明该方法能有效提取操作机构储能相关部件的故障特征,实现了对操作机构储能相关部件的故障诊断。

Diagnosis on the mechanical fault of universal circuit breaker based on motor current analysis

The motor current signal is usually used to analyze the fault of the electrical machine. However, few research works utilize the current to diagnose the fault of the mechanism connected with the motor. A fault diagnosis method for the universal circuit breaker operating mechanism based on the energy storage motor current signal analysis is proposed. Firstly, Hilbert amplitude demodulation and improved threshold wavelet packet method are used to obtain the envelope of the AC current signal, which can solve the rough problem of the extracted envelope caused by random noise interference. Then, the time characteristics, amplitude characteristics and kurtosis of the current signal are extracted according to the envelope as the characteristic parameters of current waveforms of different states. Finally, the fuzzy clustering and Quantum Particle Swarm Optimization (QPSO) Relevance Vector Machine (RVM) are combined to realize the classification of normal state of circuit breaker, jam fault of drive gear, jam fault and abscission of the energy storage spring. The fault diagnosis system of universal circuit breaker based on current analysis is constructed. Evaluations are conducted under different working conditions. Experimental results show that the proposed method can effectively extract the fault features of the circuit breaker operating mechanism and realizes the diagnosis of the conventional circuit breaker operating mechanism fault.