With the development of power systems, fault diagnosis in infrared images has become increasingly important for ensuring the stability of these systems. In this paper, we propose a multi-objective multilevel threshold image segmentation method based on the boost marine predators algorithm (BMPA) for infrared-image fault diagnosis. As fault points are small, it is difficult to detect the target in an infrared image using existing segmentation methods. To address this, we use 9DKapur as the fitness function and obtain a many-objective optimization problems (MaOPs). Then, we use adaptive weights and opposition-based learning to boost the optimization ability of the MPA and solve the MOP so that a Pareto front is obtained. The DTLZ and WFG test suits are used as benchmarks to evaluate the performance of the BMPA, and electric equipment in infrared images was used to assess the fault-diagnostic ability of the proposed method. The results demonstrate that the BMPA performs better than other optimization algorithms in terms of uniformity measurement, peak signal-to-noise ratio, feature similarity, hypervolume, spacing, and CPU time. On the actual test data, the recall rate and fault detection accuracy of the proposed method were 94.29% and 92.38%, respectively. Insulator faults under various circumstances can be correctly detected in infrared images.

随着电力系统的发展，红外图像中的故障诊断对于保证电力系统的稳定性变得越来越重要。在本文中，我们提出了一种基于增强海洋捕食者算法（BMPA）的多目标多级阈值图像分割方法，用于红外图像故障诊断。由于故障点较小，使用现有的分割方法难以检测红外图像中的目标。为了解决这个问题，我们使用 9DKapur 作为适应度函数并获得多目标优化问题 (MaOPs)。然后，我们使用自适应权重和基于对立的学习来提高 MPA 的优化能力并求解 MOP，从而获得帕累托前沿。DTLZ 和 WFG 测试套件用作评估 BMPA 性能的基准，并利用红外图像中的电气设备来评估所提出方法的故障诊断能力。结果表明，BMPA 在均匀性测量、峰值信噪比、特征相似性、超体积、间距和 CPU 时间方面的性能优于其他优化算法。在实际测试数据上，所提方法的召回率和故障检测准确率分别为94.29%和92.38%。可以在红外图像中正确检测各种情况下的绝缘子故障。所提方法的召回率和故障检测准确率分别为94.29%和92.38%。可以在红外图像中正确检测各种情况下的绝缘子故障。所提方法的召回率和故障检测准确率分别为94.29%和92.38%。可以在红外图像中正确检测各种情况下的绝缘子故障。