Discrimination and correction of abnormal data for condition monitoring of drilling process.

• The different kinds of outliers in drilling process are all detected, which need to be handled differently. • The causes of anomalies are discriminated intelligently and effectively by the DTW-FCM. • While improving the data quality, the data with drilling accident characteristics is reserved. • The performance of PNN-based monitoring models are improved by the discrimination and correction. During the drilling process, the data quality influences the reliability of condition monitoring results. However, the actual drilling data may encounter various kinds of abnormal data, and different kinds of them need to be handled differently. The abnormal data caused by external factors such as sensor failure, storage errors, etc., should be corrected, while the abnormal data caused by drilling accidents should be protected. Therefore, not only the anomaly detection is needed, but the causes of anomalies should be further discriminated. This paper proposes a method for discrimination and correction of abnormal data in the drilling process. First, the local outlier factor anomaly detection algorithm is developed to detect all kinds of the abnormal data. Then, the dynamic time warping and fuzzy c-means are combined for the discrimination of causes of anomalies. Finally, the discriminated abnormal data caused by external factors are corrected with the k nearest neighbor interpolation. Simulation results involving actual data illustrate that the causes of anomalies can be discriminated effectively, and the monitoring results of monitoring models based on neural network improve after using the proposed method, which verify the necessity of anomaly discrimination. [ABSTRACT FROM AUTHOR]