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.