一类支持向量机的设备状态自适应报警方法

为了提高对异常状态识别的适应性和有效性,提出了一种基于一类支持向量机的设备状态自适应报警方法.该方法使用一类支持向量机的在线算法,动态估计监测参数在高维特征空间中的最优分布区域,将新数据与上一时刻分布区域的相对距离作为异常指标,描述监测参数的统计特征变化,辨识出设备的异常状态.通过对仿真数据的报警效果分析,以及将该方法应用于对加热炉风机的振动监测中,得到的异常报警结果能够满足实际监测的需要,证明该方法具有异常的识别敏感性、缓慢劣化包容性和状态迁移适应性的特点.

Self-Adaptive Alarm Method for Equipment Condition Based on One-Class Support Vector Machine

To improve the adaptability and effectiveness of recognition on abnormal condition, a self-adaptive alarm method for equipment condition based on one-class support vector machine (OC-SVM) is proposed. The optimum distribution area of monitoring parameters in high-dimen-sional feature space is dynamically estimated with on-line algorithm of OC-SVM. The abnormal index, determined by the relative distance between the new data and the distribution area at the previous moment, describes the statistical feature variation of monitoring parameters and identi-fies the abnormal condition of the equipments. The characteristics, such as the sensitivity in ab-normal condition recognition, the toleration in slow deterioration and the adaptability in condition alternation, are verified by simulation data. The present method is further applied to the vibra-tion monitoring of heating furnace fan. The alarms under the actual abnormal condition meet the demand of equipment monitoring.