Genetic Algorithm in Fault Diagnosis System of Construction Machinery

With the continuous progress of the times, the speed of economic development is also gradually accelerating. In various industries, construction machinery plays a very important role. However, due to the improvement of scientific and technological level and the continuous improvement of people's quality requirements, the difficulty of construction has also increased accordingly. Therefore, there are enormous difficulties in solving these problems. In response to this issue, this article analyzed and compared the commonly used traditional methods and testing techniques, and draws conclusions. At the same time, the performance of the engineering machinery fault diagnosis system was tested. The test results showed that the diagnostic accuracy was above 94%, and the diagnostic time was within 0.4 ms; the work efficiency was above 0.84; the robustness of genetic algorithm was above 0.04; the interpretability of the system was above 50%. Among them, genetic algorithm was applied to the field of fault diagnosis in construction machinery, and the goal of improving work efficiency was achieved by selecting appropriate methods under different conditions. Genetic algorithms can automatically extract effective features and reduce the need for manual intervention. By optimizing feature weights or selecting the optimal feature subset, the algorithm can effectively reduce the interference of redundant features, extract important features related to faults, and provide a more reliable basis for fault diagnosis.