The acoustical signal generated during the opening and closing process of circuit breaker contains a variety of information. Therefore

a kind of mechanical fault diagnosis method of high voltage circuit breaker based on the acoustical fingerprint is proposed in this paper. First

an experimental platform for the test of acoustical fingerprint fault simulationis set up to collect acoustical signals in the opening and closing test of one 252 kV high-voltage circuit breaker. Then

the acoustical fingerprint signal is analyzed and the feature is extracted through such methods as short-term energy

total energy

information entropy and wavelet packet decomposition

which overcomes the problem that a single type of feature value is difficult to distinguish between faults. The results show that effective identification high voltage circuit breaker at different working conditions can be realized by combining multiple characteristic quantities

and a criterion table for distinguishing the mechanical faults of acoustical fingerprint-based high voltage circuit breaker at different working conditionsis givenand the analysis example is provided. The characteristic quantities and trends of faults of different severity under the same working conditions are analyzed

finding that the energy coefficientof the wavelet packet decomposition of 90% of inter-turn short-circuit resistance show normal and the interturn short-circuit characteristics

respectively.