A novel adaptive kernel-guided multi-condition abnormal data detection method

Abstract

Data acquisition devices and environmental factors may lead to abnormal data and misjudgment of mechanical health status in mechanical work. A new method for abnormal data detection in mechanical equipment health monitoring is proposed in this paper. Firstly, an adaptive kernel bandwidth local outlier factor (AKLOF) detection method is proposed. Furthermore, multiple time-domain statistical features are obtained from sliding window segmentation data, and the anomaly degree of each data segment is calculated. Then, the relationship between the sliding window length and the maximum AKLOF function is studied, and the optimal sliding window length is obtained. Subsequently, the AKLOF value and threshold are calculated according to the optimal sliding window length to detect the final abnormal data. Four groups of experiments result show that the method can accurately identify all the missing data, and can also effectively detect other types of abnormal data, which provide guarantee for equipment condition monitoring.