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-dimensional 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 identifies the abnormal condition of the equipments. The characteristics, such as the sensitivity in abnormal 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 vibration monitoring of heating furnace fan. The alarms under the actual abnormal condition meet the demand of equipment monitoring.