Rotating machines are one of the most common equipments in modern industry, effective fault detection and diagnosis methods are vital to equipment health monitoring. In industrial production, the known information of fault types is insufficient generally, especially for constructing complex equipment and components. In previous studies of equipment fault detection, accurate fault classification and diagnosis methods have been presented, while seldom takes the condition of paucity of fault data into account. Therefore, this paper presents a novel antibody population optimization based artificial immune system (APO-AIS) for rotating equipment anomaly detection. The proposed approach can detect abnormal events while monitoring the operating condition. Meanwhile, an antigen-based antibody selecting method, a density-based antibody screening method and an optimized judgment rule based on individual difference are presented for improving the iteration evolution. The presented methods and optimized judgment rule enhance the robustness and reduces training burden for the proposed approach, which leads to accurate anomaly detection in strong background noise and in practical industrial environment. The effectiveness and robustness of the proposed method has been proven experimentally by bearing fault diagnosing and centrifugal pump condition monitoring in this paper.

旋转机械是现代工业中最常见的设备之一，有效的故障检测和诊断方法对于设备健康状况的监测至关重要。在工业生产中，故障类型的已知信息通常不足，特别是对于构造复杂的设备和组件。在先前的设备故障检测研究中，已经提出了准确的故障分类和诊断方法，而很少考虑到故障数据很少的情况。因此，本文提出了一种基于抗体群体优化的新型人工免疫系统（APO-AIS），用于旋转设备异常检测。所提出的方法可以在监视操作状况的同时检测异常事件。同时，一种基于抗原的抗体选择方法，提出了基于密度的抗体筛选方法和基于个体差异的优化判断规则，以改善迭代过程。所提出的方法和优化的判断规则提高了鲁棒性，并减轻了该方法的训练负担，从而在强背景噪声和实际工业环境中实现了准确的异常检测。本文通过轴承故障诊断和离心泵状态监测，通过实验证明了该方法的有效性和鲁棒性。在强烈的背景噪声和实际工业环境中，可以进行准确的异常检测。本文通过轴承故障诊断和离心泵状态监测，通过实验证明了该方法的有效性和鲁棒性。在强烈的背景噪声和实际工业环境中，可以进行准确的异常检测。本文通过轴承故障诊断和离心泵状态监测，通过实验证明了该方法的有效性和鲁棒性。