In this paper, a fast bearing state detection method based on multi-sensor signal fusion and compression feature extraction is proposed. The best estimation in the random weighted fusion algorithm is adaptively adjusted by the fluctuation factor to realize the high-precision fusion of variable signals and reduce the noise component in the signals. In the compressed sensing framework, a partial Hadamard matrix is selected as the measurement matrix, and the signal reconstruction is abandoned, leading to reduced average sampling rate and less data for signal acquisition, transmission, and extraction of fault features. The proposed method for diagnosis of rolling bearing fault is fast, effective, and accurate, as verified by experimental results.