To improve the fault recognition rate of the dynamic principal component spatial data drive method, a fault diagnosis and equipment health status assessment method based on similarity fuzzy dynamics principal component analysis was proposed. First, the data are fuzzified according to the error function, and an augmented matrix is constructed. The eigenvalues are decomposed to obtain a score matrix and residual matrix of the fuzzy principal component. Further, the similarity between fault data and normal data is calculated. Meanwhile, a health assessment of the equipment is realized. The contribution rate of the observed variables is calculated. Finally, general Tennessee Eastman data and health assessment of a hydraulic press are used to validate the algorithm. The results show that the SFDPCA has a 100 % fault recognition rate for some faults, and the recognition rate for other faults is also higher than that of DPCA-Diss, DPCA-SPE, and PCA-SPE. The SDDPCA accurately identifies abnormal phenomena. It can determine the health level of prefilling and effectively make up for the shortcomings of PCA − T 2 , PCA-SPE, DPCA-Diss, and other methods and also can be applied to data-driven fault diagnosis to improve the fault recognition rate.

To enable automatic transplantation of plug seedlings and improve identification accuracy, an algorithm to identify ideal seedling leaf sets based on Fourier descriptors is developed, and a classification method based on expert system is adopted to improve the identification rate of the plug seedlings. First, the image of the plug seedlings is captured by image acquisition system, followed by application of K-means clustering for image segmentation and binary processing and identification of the ideal seedling leaf set by Fourier descriptors. Then we obtain feature vectors, such as gray scale (R+B+G)/3, hue H, and rectangularity. After that the knowledge model of the plug seedlings is defined, and the inference engine based on knowledge is designed. Finally, the recognizing test is carried out. The success rate of the identification of 10 varieties of plug seedlings from 190 plates is 98.5%. For the same sample, the recognizing rate of support vector machine (SVM) is 85%, the recognizing rate of particle-swarm optimization SVM (PSOSVM) is 87%, the recognizing rate of back propagation neural network (BP) is 63%, and the recognizing rate of Fourier descriptors SVM (FDSVM) is 87%. These results show that our recognition method based on an expert system satisfies the requirements of automatic transplanting.

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