Intelligent quay crane key mechanism health state evaluation method

Abstract

The invention discloses an intelligent quay crane key mechanism health state evaluation method. The method comprises the steps that firstly, vibration signals of a quay crane mechanism are preprocessed, multiple time domain features are extracted, feature selection is conducted, and a grid capable of measuring vibration feature data distribution is generated through an unsupervised clustering algorithm; then, a hidden Markov model is adopted to train the generated data distribution grid, mapping between data grid distribution and the mechanism health state is established, and a quay crane state evaluation model is obtained; and finally, inputting the to-be-tested data distribution grid into the state evaluation model, and evaluating the health state of the quay crane mechanism according tothe log-likelihood probability output by the model. According to the method, massive unlabeled data generated in quay crane operation can be effectively processed, the problem of lack of quay crane fault data in practical application can be solved, intelligent evaluation of the health state of a quay crane key mechanism is realized, intelligent maintenance of the quay crane is promoted, and the safety and reliability of quay crane operation are improved.

Description

technical field [0001] The invention belongs to the field of mechanical state monitoring and fault diagnosis, and in particular relates to an intelligent evaluation method for the health state of key mechanisms of quay bridges. [0002] technical background [0003] Quayside container crane, referred to as quayside crane, is an important large-scale port logistics equipment. The structure of the quay crane is complex, the loads are various, and the operating conditions are changeable and harsh. With the increasing workload, various failures are prone to occur during the operation process. Once a failure occurs, it will cause huge economic losses. At present, the maintenance of the quay crane equipment on the wharf still adopts the traditional passive maintenance mode, through regular manual inspections to find faults and repair them. This maintenance mode cannot detect the state changes of the quay crane in time, and cannot fundamentally avoid the occurrence of faults. . Th...

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Problems solved by technology

[0009] In order to effectively process the massive unlabeled data of the quay crane, overcome the problem of lack of fault data sources, promote the intelligent maintenance of the quay

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