Automatic fault monitoring of ship mechanical and electronic equipment based on pattern recognition
The data volume of ship mechanical and electronic equipment faults is relatively large,and the patterns are complex and diverse.To meet its complexity requirements,a pattern recognition based automatic monitoring method for ship mechanical and electronic equipment faults is proposed.The temperature,pressure,vibration and other data during the opera-tion of ship mechanical and electronic equipment are collected as the raw data for fault monitoring.The similarity coeffi-cient and Euclidean distance between the data are calculated,and the K-means algorithm is combined to achieve data cluster-ing processing.By using the wavelet packet algorithm to extract features from the clustered data and inputting them into a convolutional neural network,the monitoring model is trained to achieve automatic monitoring of ship mechanical and elec-tronic equipment faults.Through experimental analysis,this method is highly consistent with the fault conditions monitored by relevant personnel,and can maintain monitoring time within 5ms for different types of faults,with high monitoring effi-ciency and accuracy.
万方数据
维普
