For low-speed heavy-duty rotating structures, the fault sound source is prone to low signal-to-noise ratio and feature submergence issues when subjected to environmental noise and reverberation interference. A method combining Integrated Sidelobe Canceling and Linear Prediction (ISCLP) Kalman filter with high-resolution spatial spectrum estimation is proposed to address these challenges. By applying the Kalman filter to remove reverberation and noise from the original signal, late reverberations can be effectively eliminated, and characteristic values can be extracted. Reconstruction of the acoustic field based on the characteristic values enables azimuth estimation of the fault. Experimental results demonstrate that this method can select suitable signal frequency bands for localization estimation, effectively eliminate interference from environmental reverberations, enhance the performance of microphone arrays in fault source localization, and significantly improve localization accuracy.