Fault Detection of Turntable Bearing of Engineering Lifting Machinery Based on Adaptive Fireworks Algorithm

Abstract

The traditional fault detection methods for turntable bearings mainly rely on manual inspection and simple vibration signal analysis. Although these methods can detect faults to a certain extent, they have limitations such as low efficiency, low accuracy, and susceptibility to human factors. To overcome the challenges and limitations of traditional methods, we propose a fault detection method for engineering crane turntable bearings based on the adaptive fireworks algorithm (AFA). Fault detection of turntable bearing of engineering lifting machinery based on an AFA is an innovative method using the fireworks algorithm (FWA) for fault detection. FWA is a kind of optimization algorithm with global search and local search ability, which can effectively solve complex engineering problems. In the fault detection of turntable bearing of engineering lifting machinery, the FWA adaptively adjusts the radius and number of fireworks explosions, so that the algorithm can search in the global scope and detect the fault more accurately. At the same time, the FWA also has a local search ability, which can carry out fine search of the fault area and improve the accuracy of fault detection. By applying the FWA to the fault detection of turntable bearing of engineering lifting machinery, the efficiency and accuracy of fault detection can be effectively improved, the cost of fault detection can be reduced, and the safe operation of engineering lifting machinery can be guaranteed. The fault detection method of turntable bearing of engineering lifting machinery based on an AFA is an innovative method with broad application prospects and can provide an effective solution for the fault detection of engineering lifting machinery.