Full condition monitoring of geological drilling process based on just-in-time learning-aided slow feature analysis

Presently, the demand for precise process monitoring during geological drilling has increased dramatically. However, there exists complex dynamic characteristics due to the various forms of changes in operation conditions. A large number of false alarms are usually triggered when using the conventional static-based monitoring methods. In this paper, two types of dynamic behaviors are comprehensively considered, including the dynamic behaviors caused by the operating parameters adjustment and the operating mode switching, and then, a full condition monitoring method is proposed for the drilling process based on just-in-time learning (JITL)-aided slow feature analysis (SFA). On one hand, the JITL local modeling strategy is improved and adopted to deal with the dynamic behavior due to the operating mode switching. Specifically, a sequence spatiotemporal similarity analysis method is developed to improve the local modeling performance. On the other hand, the SFA-based concurrent monitoring of static deviations and dynamic anomalies is realized to cope with the dynamic behavior due to the operating parameters adjustment. Several industrial cases based on actual drilling data are conducted, which illustrate the effectiveness and superiority of the proposed method.