Abstract It is critically important that windage alteration faults (WAFs) within mine ventilation systems be quickly identified and mitigated in order to ensure a safe mine production environment. Thus, we propose a Hybrid-Encoding adaptive Evolution Strategy (ES) Algorithm to diagnose the fault’s location and volume quickly and accurately, as it combines classification and regression features. The Euclidean distance between the airflow set calculated via fault diagnosis and the airflow set obtained by the monitoring system was used as the objective function value. Six benchmark functions and one thousand six hundred tests were carried out to verify the feasibility of using Hybrid-Encoding for WAFs diagnosis. The effectiveness of adaptive ES was demonstrated by Genetic Algorithm (GA), Differential Evolution Algorithm (DEA), and Particle Swarm Optimization (PSO). The experimental results fully validate the Hybrid-Encoding adaptive ES superiority in terms of accuracy, precision, diagnostic errors, robustness, computational efficiency, and convergence speed, etc. Diagnostic accuracy and precision during field testing were both 92.5 %, and 93.75 % of the results showed relative errors of

中文翻译：

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一种用于风阻变化故障诊断的混合编码自适应进化策略算法

摘要 快速识别和缓解矿井通风系统中的风阻改变故障 (WAF) 对确保安全的矿井生产环境至关重要。因此，我们提出了一种混合编码自适应进化策略 (ES) 算法，因为它结合了分类和回归特征，可以快速准确地诊断故障的位置和体积。以故障诊断计算出的气流集与监测系统得到的气​​流集之间的欧氏距离作为目标函数值。进行了六个基准功能和一千六百个测试，以验证使用混合编码进行 WAF 诊断的可行性。遗传算法 (GA)、差分进化算法 (DEA)、和粒子群优化 (PSO)。实验结果充分验证了Hybrid-Encoding自适应ES在准确率、精度、诊断误差、鲁棒性、计算效率、收敛速度等方面的优越性。诊断准确率和现场测试准确率分别为92.5%和93.75%。结果显示相对误差