Research On Top Drive Drilling Equipment Condition Monitoring And Diagnosis Technology Of Offshore Drilling Platform

As the key equipment of the offshore drilling platform,the status of Top Drive Drilling Equipment(TDS)highly relates to the efficiency and safety of drilling operations.All along,due to the complicated mechanical structure of the TDS and the changing operating condition,incomplete monitoring parameter,non-unique key parameters,and substandard diagnostic accuracy have become urgent problems in the field of top drive condition monitoring and fault diagnosis.To address these issues,this paper investigates the construction of multi-source heterogeneous monitoring system for TDS,and the health assessment and failure mode identification methods of TDS under multiple operating conditions.(1)Aiming at the problem that the complete TDS status parameters cannot be obtained from simple monitoring method.Firstly,the structure and fault types of the TDS are analyzed,and the monitoring parameters of the TDS are determined based on the characterization of each fault type.The Modbus-based Cyber Base system data and multi-source heterogeneous data integration are proposed in further.Considering the constraints on the bandwidth and reliability of remote monitoring on the land side,a remote monitoring method based on database synchronization is proposed.The proposed monitoring system includes data collection,data integration and data transmission,which can effectively and reliably achieve the full-range remote real-time monitoring of offshore platform TDS,and provide a data basis for the subsequent TDS health assessment and fault diagnosis.(2)Since the key state parameters of TDS vary greatly under changing operating conditions,it is difficult to apply conventional health assessment methods to TDS.Therefore,a GMM-SVM-MD based TDS health assessment method is proposed.First,by analyzing the field data and actual operating scenarios of the TDS,the common operating conditions of TDS are determined.Then,Gaussian mixture model(GMM)is used to identify the operating condition.Under various operating conditions,a Support Vector Machine(SVM)based TDS health state recognition model is used to evaluate TDS state.Next,Mahalanobis distance(MD)is employed to measuring the difference between the current state vector and the state vector obtained from normal operation condition.Finally,the health indicator of the TDS can be calculated,which can characterize the health status of the TDS and decide whether to proceed with the fault diagnosis process.(3)Traditional data-driven fault diagnosis model has difficulties in collecting fault data,and the diagnostic accuracy is strongly affected by the operating conditions.To handle these problems,a fault diagnosis method based on model residual analysis is proposed.This method establishes an observation model based on the TDS operating parameters.The difference between the actual monitoring parameters and the calculated parameters obtained from the model is used to construct the feature vector,which can suppress the influence of variable operating conditions.Finally,K-nearest neighbor(KNN)model is used to classify the feature vectors and determine the TDS failure mode.(4)Based on the proposed TDS comprehensive monitoring system,health assessment method and fault diagnosis method,combined with the traditional equipment intelligent monitoring and signal analysis technology,the offshore drilling platform TDS integrated monitoring and fault diagnosis system was developed.The system can monitor the status of the TDS in real time,then obtain the health status and analyze the cause of the fault.Finally it forms a comprehensive diagnostic report to assist the on-site personnel to repair and maintain the TDS,and provides support for the health monitoring and intelligent management of the TDS.