The development of fault detection and diagnosis is of great significance to ensure the normal operation of the system and improve the reliability of the equipment. Extreme learning ma-chine (ELM) is widely used in the field of fault diagnosis because of its fast learning speed and high test accuracy, but its inherent randomness has a great influence on its generalization ability and diagnostic accuracy. To solve this problem, we propose a novel analog circuit fault diagnosis method based on the LOGFA algorithm and extreme learning machine in this paper. The internal parameters of the extreme learning machine are optimized by using the optimization capabilities of the LOGFA algorithm. In addition, the Sallen-Key low-pass filter is chosen as the test circuit. The simulation results show that the proposed method effectively improves the accuracy of analog circuit fault diagnosis.