Fault diagnosis decision model for switch machines considering sample diversity
To address the issue that existing research does not adequately consider the diagnosticmodel's applicability across various switch machine data sample conditions,and that a single diagnos-tic method struggles to meet fault diagnosis accuracy and decision-making requirements,this study proposes a fault diagnosis decision model that takes sample diversity into account. Firstly,time do-main,frequency domain,and time-frequency domain features are extracted from the oil pressure curves of eight fault modes and normal mode of the ZYJ7 electro-hydraulic switch machine. The Ker-nel Principal Component Analysis(KPCA) method is employed to dimensionally reduce the feature quantities in the three domains,resulting in the formation of feature matrices for each domain and thus different types of data samples. Then,the decision model is constructed using PSO-KNN,SA-PSO-PNN,and PSO-SVM algorithms. For general data samples,the model applies all three algorithms for data classification within the same domain. A two-out-of-three voting mechanism is then used to consolidate the diagnosis results from each algorithm within the same domain,yielding domain-specific diagnosis outcomes. For big data and unbalanced data samples,the model selects the recom-mended algorithm from the three based on sample characteristics to determine the diagnosis results for each domain. Finally,a final diagnosis is obtained by applying a two-out-of-three voting approach to the domain-specific diagnosis results. Simulation results demonstrate that the decision model achieves an average accuracy improvement of 1.01％ for big data samples,12.82％ for unbalanced data samples,and 6.18％ for general data samples compared to single diagnosis algorithms. These im-provements highlight the model's enhanced diagnostic precision through the integration of multidimen-sional features across multiple domains and algorithm-specific attributes,offering a novel approach for the application of ensemble learning in switch machine fault diagnosis.
万方数据
维普
