The state recognition method of ship electrical equipment based on multi source information fusion
To reliably grasp the status of ship electrical equipment and ensure its safe operation,a method for identifying the status of ship electrical equipment based on multi-source information fusion is proposed.Using a time series model to detect and correct continuous abnormal data and independent abnormal data in the multi-source historical data of ship electrical equipment;Based on the fusion of corrected multi-source historical data of electrical equipment using the joint Kalman filtering algorithm,a ship electrical equipment status recognition network model is constructed by training spectral clustering and deep neural networks based on the fused multi-source data.Combined with real-time operating data of electrical equipment,the ship electrical equipment status is identified.The test results show that this method can determine the continuous abnormal data and independent abnormal data in the data,and complete the correction of all abnormal data to ensure the integrity of the data;The dispersion results are all below 0.016;Capable of identifying normal,abnormal,and emergency states of electrical equipment,with a minimum root mean square error value below 0.0044,indicating good recognition performance.
multi source information fusionship electrical equipmentstate recognitionabnormal data correctiontime series model
NETL
NSTL
万方数据
