In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method’s denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals.

The brake system of a mine hoist is the key component and the last barrier to ensure the safe operation in a mine. Based on the vibration and the sound signals of a test brake system, this paper studied the characteristics of the signals and the relationship between both the signals and tries to propose a new fault diagnosis method for the brake system. First, the vibration and sound signals of the test set-up under different braking conditions at different brake disc rotation speeds were collected. Then, the vibration and sound signals were filtered and reduced by the wavelet packet, and the reconstructed signal eigenvalues were extracted. The results showed that the characteristics of vibration and sound signals in fault state and normal state can be drawn. It shows the combined fault diagnosis method based on vibration and sound signals can see a wide application to the fault diagnosis for mine hoist brake system.

Aiming at the intelligent design of underground roadway support and the precise positioning of unmanned full excavation faces, a positioning and measurement method of bolt robots based on the monocular vision principle was proposed. In this paper, a vehicle body positioning model based on image data was established. The data were obtained with a camera, and the conversion between image coordinates and world coordinates was carried out through coordinate system conversion. A monocular vision positioning system of the bolt robot was designed, and the simulation’s experimental model was established. Under the simulation’s experimental conditions, the effective positioning distance of the monocular vision positioning system was measured. An experimental platform for the bolt robot was designed, and real-time human positioning data measurement of the vehicle was carried out. The experimental error was analyzed, and the reliability of the method was proven. This method realizes the real-time positioning of underground mines through the bolt robot, improves the accuracy and efficiency of the positioning, and lays a foundation for the positioning control of the heading face and the unmanned bolt robot.