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Aiming at the requirement of unattended intelligent substation and the shortcomings of existing fault diagnosis system, an audio monitoring and fault diagnosis system of power equipment is proposed. According to the low signal-to-noise ratio (SNR) of the audio signal in substation power equipment, the Mel-frequency cepstrum coefficients with strong robustness are used as the characteristic parameters to judge the audio signal anomaly. Based on the audio feature, a multi-sample observation sequence is constructed. The hidden Markov model (HMM) is used to diagnose the fault, and the fault type is identified by comparing the logarithm likelihood estimate output value. The method has the advantage of real-time and avoids the limitation of the existing fault diagnosis method which requires large sample size. The experimental result shows that the proposed fault diagnosis system has high recognition rate and robustness.