(1) Consult relevant literature, fully understand the development history and current situation of condition monitoring and fault diagnosis at home and abroad, and determine the research methods of condition monitoring and fault diagnosis. The main shaft system and working principle of the artificial turf tufting machine are studied, and the common faults and vibration sources of the tufting machine are studied. Based on the analysis of the fault monitoring of the tufting machine, the monitoring object of the system—the transmission bearing on the main shaft is determined. The overall structure of the condition monitoring and fault diagnosis system is designed, the data transmission method and remote access method are determined, and the system hardware is designed.
(2) The time-frequency analysis method is studied-the modal aliasing and end effect that affect the performance of the EMD method are studied. Aiming at the shortcomings of the EMD method, a further improvement is made on the basis of the EEMD method, and an adaptive I-EEMD method is proposed. The signal is used to prove the effectiveness of the method.
(3) The pattern recognition method based on support vector machine (SVM) is studied, and the adaptive selection of SVM parameters based on genetic algorithm is proposed. The nonlinear dynamic parameter-entropy function is studied, and the parameter value of fuzzy entropy function is studied. It is further proposed to perform I-EEMD decomposition of the fault signal to achieve the effect of multi-level analysis. The coefficient is used to select the effective IMF, and the fuzzy entropy value is calculated as the fault signal feature, and then input into the SVM for fault diagnosis. Carrying out example verification, the trained model classifies the test samples and effectively identifies the fault information.
(4) The program structure of the queue message processor is used to realize the pattern recognition of the fault diagnosis. The system realizes the collection, transmission, storage, analysis and fault diagnosis of the vibration signal.
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