XGBoost在故障预测及遗传算法在排班优化中的应用

Application of XGBoost in Fault Prediction and Genetic Algorithm in Scheduling Optimisation

摘要: 为了解决工业生产线中的故障智能识别与预测问题，本研究利用XGBoost算法及相关性等方法对生产线故障数据进行了深入研究，并基于遗传算法对排班问题进行了优化分析。首先整理了A工厂中生产线的详细记录，同时构建XGBoost模型对生产线M201进行训练和预测，提取各类故障的发生次数和持续时间，揭示了故障与产量之间的关系。此外，还探索了遗传算法在排班问题中的适用性，并设计出相应的排班优化模型。研究结果显示，XGBoost模型在故障预测中具有较高的拟合度，能够准确预测故障的发生及其持续时间，并且预测得到了各装置每月的故障总次数和最长与最短的持续时长，并且发现产品合格率与故障之间的关系。在排班优化方面，遗传算法表现出了良好的适用性，能够有效解决排班问题中的复杂性和不确定性。本研究将XGBoost算法应用于工业生产线故障预测中，提高了故障识别的准确性和及时性。同时，遗传算法在排班优化中的应用，也为工业生产的调度和管理提供了新的思路和方法。这些研究成果对于推动工业智能化进程和提高生产效率具有重要意义。

Abstract: In order to solve the problem of intelligent identification and prediction of faults in industrial production lines, this study conducted an in-depth study of production line fault data using XGBoost algorithm and correlation and other methods, and optimised the scheduling problem based on genetic algorithm. Firstly, the detailed records of the production line in Factory A were collated, while the XGBoost model was constructed to train and predict the production line M201, extracting the number of occurrences and duration of various types of faults, and revealing the relationship between the faults and the output. In addition, the applicability of genetic algorithms in scheduling problems was explored and corresponding scheduling optimisation models were designed. The results show that the XGBoost model has a high degree of fit in fault prediction, can accurately predict the occurrence of faults and their durations, and predicts the total number of faults and the longest and shortest durations of each unit per month, and finds the relationship between product qualification rate and faults. In scheduling optimisation, genetic algorithms show good applicability and can effectively solve the complexity and uncertainty in scheduling problems. In this study, XGBoost algorithm is applied to industrial production line fault prediction to improve the accuracy and timeliness of fault identification. Meanwhile, the application of genetic algorithm in scheduling optimisation also provides new ideas and methods for industrial production scheduling and management. These research results are of great significance for promoting the process of industrial intelligence and improving production efficiency.