LAUSR

The walking part of the wall‐building robot is an important component whose health influences the robot's overall performance, and the bearing of the walking part is a critical but often disregarded component. As a result, based on acoustic signals from the roadside, this study proposes a method of bearing fault monitoring of walking parts to ensure the more stable operation of the wall‐building robot. To begin, due to the Doppler effect caused by the relative displacement between the sensor and the walking part, the collected acoustic signal has frequency shift and amplitude attenuation, so the multiple signal classification spectrum estimation method with added windows is first used to determine the position and speed of the walking part bearing in real time, and then obtain a resampled time series of the distorted signal. Second, the distorted signal is resampled by Morse acoustic theory to correct the distorted sound signal collected by the array sensor. Third, a multitask deep learning method with an attention mechanism is designed to jointly diagnose the fault type and damage degree of bearing by analyzing the corrected acoustic signal. The experimental results show that the method can effectively restore the distorted signal while still accurately and stably detecting the fault type and damage degree of the bearing under a variety of working conditions and external disturbances, making it more suitable for the work of wall‐building robots.