Abstract: To address the issues in existing acoustic signal enhancement methods for belt conveyor idler bearings, such as excessive noise reduction leading to signal distortion, poor adaptability, and ineffective extraction of complex sound field characteristics, a method based on histogram noise estimation and Wiener filtering was proposed. First, the power spectrum of each frequency band of acoustic signal was computed, and a power spectrum histogram was constructed to estimate the noise spectrum, followed by first-order recursive smoothing of the noise spectrum. Next, the estimated noise spectrum was used to calculate the Wiener filter gain function, obtaining the noise-reduced acoustic signal. Then, envelope spectrum analysis was performed on the denoised acoustic signal. By comparing the measured fault characteristic frequencies with theoretical fault characteristic frequencies, fault diagnosis of the belt conveyor idler bearing was achieved. Experimental and field test results show that the proposed acoustic signal enhancement method, based on histogram noise estimation and Wiener filtering, produces an envelope spectrum containing distinct fault characteristic frequencies and their harmonic components. The signal-to-noise ratio (SNR) of the experimental data improved by at least 1.14 dB, while the SNR of the field data improved by at least 1.04 dB. The method demonstrates good performance in extracting bearing fault features and can effectively enhance acoustic signals for belt conveyor idler bearings under severe environmental noise interference.