【摘要】 气阀故障是往复压缩机最常见的故障类型之一,由于往复压缩机的工作机理复杂,故障样本缺乏,难以应用常规方法对其进行有效的故障检测。为了能够准确检测气阀的各种故障,基于生物免疫系统的反面选择机理及反面选择算法,首先对设备故障检测问题进行了描述,引进了设备状态空间、自己—非己空间及模糊空间的概念,继而提出了适于往复压缩机气阀故障检测的新方法。通过对气阀常见故障的检测结果表明,所提出的方法能够以异常度曲线的形式较好地反映出气阀的各种故障,表明了该方法的有效性。基于免疫机理的设备故障检测方法,是在对设备正常运行数据学习的基础上,实现对设备的故障检测,不需要设备运行的故障数据,它适合对故障数据缺乏的设备进行有效的故障检测。更多还原

【Abstract】 The faults of gas valves often happen for reciprocat-ing compressors. Because of the complex mechanism and insu-fficient fault samples, it is often difficult to detect the faults of gas valves effectively for common detection methods. In order to detect the faults of gas valves accurately, the abnormal dete-ction problem to equipment is described and some terms (for e-xample, state space of equipment, self-nonself space and fuzzy space) are introduced based on the negative selection mechani-sm of natural immune system and the negative selection algor-ithm. Consequently, the approach suit for detecting the faults of gas valves is proposed. The result of detection for the common faults of gas valves shows the approach can efficiently detect the faults of gas valves in the way of abnormality curve. This shows the approach is valid. The fault detection approach based on immune mechanism can detect the faults of equipment by learning normal data without fault data. It can efficiently detect the faults of the equipment that lacks fault data.更多还原