Drawworks Diagnosis by a Temporal Probabilistic Method Using a Microprocessor

The paper dwells upon diagnosing drilling rig electrics with material, time, and labor costs reduction in mind. SciVal analytics and overview of literature on equipment diagnosis reinforce the relevance of this research. To create an automatic fault detection system, it is proposed to combine mathematical models of Boolean objects of diagnosis with the microprocessor capabilities. The research team used an Uralmash 6500/450 BMCh drilling rig to develop electrical equipment diagnosis flowcharts and a drawworks logical model; then the researchers estimated the costs of checking the model elements and compiled a table of a fault functions. The proposal was to program the fault location algorithm in the controller programming language following the author-developed troubleshooting graph which uses a temporal probabilistic method. To visualize the solution, the paper presents an original method that diagnoses faults of individual drilling rig components; case study herein analyzes an inductive sensor as such a component. The method consists in using additional feedback and implementing an algorithm for automatic fault detection in a high-level programming language.