The height limiter of crane is an important component to ensure the safety of crane,and its quality is very important for the safety operation of cranes.Therefore,a crane height limit failure identification method based on support vector machines(SVM)is proposed.The feature extraction algorithm of principal component analysis was adopted to obtain the failure signal characteristics of limiters,and the monitoring signals of the crane limiter were classified and identified by combining with the support vector machine algorithm to obtain the signals for real-time quality monitoring of limiters.The research shows that the feature extraction algorithm of principal component analysis can effectively reduce the data amount of the limiter detection signal and capture the effective limiter signal features,and effectively identify the crane limiter failure signal through the support vector machine algorithm with a recognition accuracy reaching 95%,which significantly improves the efficiency and accuracy of the limiter failure diagnosis.
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维普
