Abstract: The wire rope damage signal is a kind of non-stationary and non-periodic impact signal, and the noise reduction processing and characteristic extraction of its characteristic signal become difficult problems to be solved urgently. If the wavelet base or decomposition layer number of wavelet transform method is not suitable, which will introduce other noise interference while reducing signal noise, and affect the effect of signal processing and characteristic extraction. Compared with the wavelet transform, the moving average method only needs to select a certain shift window width to achieve effective noise reduction, but the shift window width needs to be selected artificially, and the blindness is large. In order to solve the above problems, a noise reduction method of wire rope damage signal under strong noise background is proposed. Different types of broken wire data are collected by magnetic flux leakage (MFL) sensor of wire rope, and strong Gaussian white noise is added to the signal to simulate the strong noise background. The adaptive moving average method is used to reduce the noise of the wire rope damage signal, and the quantum particle swarm optimization (QPSO) algorithm is used to optimize the window width of the moving average method. The signal-to-noise ratio (SNR) of the damage signal is used as the fitness function, and the SNR of damage characteristic signal is maximized by the QPSO algorithm, so as to achieve the optimal signal noise reduction effect. The experimental results show that compared with wavelet transform, the adaptive moving average method has more obvious noise reduction effect, higher signal-to-noise ratio and smoother signal for wire rope stationary and fluctuating signals under strong noise background. The measured results show that the noise reduction effect of the adaptive moving average method is also better than that of the wavelet transform for the wire rope damage signals with relatively weak noise on site, which verifies that the adaptive moving average method has good universality.