Rolling bearings play a vital role in the overall operation of rotating machinery. In practice, many learning methods for variable-speed fault diagnosis ignore task-specific decision boundaries, making it very difficult to completely match feature distribution between different domains. Therefore, to overcome this problem, an adversarial domain adaptation of asymmetric mapping with CORAL alignment is presented. The asymmetric mapping feature extractor is able to extract more specific-domain features with obvious distinction. Meanwhile, combining the maximum classifier discrepancy of deep transfer to give an adversarial approach and taking the task-specific decision boundaries into account, class-level alignment between the features of the source domain and target domain can be attempted. To prevent degenerate learning, which is possibly caused by asymmetric mapping and adversarial learning, the model is constrained by deep CORAL alignment to extract more domain-invariant features. Experimental results show that the proposed method can solve the variable-speed (a small span of intermediate vehicle speeds) fault diagnosis problem well, with high transfer accuracy and strong generalization.

Big data condition monitoring in the industrial of internet era is indispensable, and intelligent fault diagnosis plays an important role in it. The adversarial learning method is widely used because of its ability to extract domain invariant features to solve the variable speed fault diagnosis problem. However, its training process is often unstable and difficult to converge to the optimal solution, which brings great challenges to the fault detection of equipment. In view of this exasperating problem, a novel model, called deep domain adversarial method with central moment discrepancy, is proposed. The presented model mainly consists of four modules: a shared weight feature extraction network with wide convolution kernel, a supervised classification network, an adversarial domain classification network, and a CMD alignment network. Adversarial domain classification network is employed to extract features that have both category distinction and domain invariance in the process of mutual game learning between features of source domain and target domain. The CMD alignment network can be devoted to align the higher-order moments of two domain features to constrain the instability in adversarial learning. Through the above regularization method, the model shows a relatively stable and higher accuracy of transferring diagnosis in the non-standardized data. The public test data set and the private data set are applied to validate the model. The results show that the proposed model successfully solves the problem of training instability in adversarial learning and has a relatively high diagnostic accuracy.

At present, most of the fault diagnosis methods with extensive research and good diagnostic effect are based on the premise that the sample distribution is consistent. However, in reality, the sample distribution of rotating machinery is inconsistent due to variable working conditions, and most of the fault diagnosis algorithms have poor diagnostic effects or even invalid. To dispose the above problems, a novel symmetric stacked autoencoder (NSSAE) for adversarial domain adaptation is proposed. Firstly, the symmetric stacked autoencoder network with shared weights is used as the feature extractor to extract features which can better express the original signal. Secondly, adding domain discriminator that constituting adversarial with feature extractor to enhance the ability of feature extractor to extract domain invariant features, thus confusing the domain discriminator and making it unable to correctly distinguish the features of the two domains. Finally, to assist the adversarial training, the maximum mean discrepancy (MMD) is added to the last layer of the feature extractor to align the features of the two domains in the high-dimensional space. The experimental results show that, under the condition of variable speed, the NSSAE model can extract domain invariant features to achieve the transfer between domains, and the transfer diagnosis accuracy is high and the stability is strong.

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of $0.094\mathrm{s}$ in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of $0.113\mathrm{s}$ . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of $140\times 260$ pixels could achieve high-precision vision navigation while the course deviation angle was not more than $7.5°$ . The maximum tractor speed of the optimal template and global template were $51.41\mathrm{km}/\mathrm{h}$ and $27.47\mathrm{km}/\mathrm{h}$ , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.

Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same sample distribution, but cannot correctly predict the faults of samples with domain shift in a real situation. To settle this problem, a new intelligent fault diagnosis method, domain adaptation network with double adversarial mechanism (DAN-DAM), is proposed. The DAN-DAM model is mainly composed of a feature extractor, two label classifiers and a domain discriminator. The feature extractor and the two label classifiers form the first adversarial mechanism to achieve class-level alignment. Moreover, the discrepancy between the two classifiers is measured by Wasserstein distance. Meanwhile, the feature extractor and the domain discriminator form the second adversarial mechanism to realize domain-level alignment. In addition, maximum mean discrepancy (MMD) is used to reduce the distance between the extracted features of two domains. The DAN-DAM model is verified by multiple transfer experiments on some datasets. According to the transfer experiment results, the DAN-DAM model has a good diagnosis effect for the domain shift samples. Moreover, the diagnostic accuracy is generally higher than other mainstream diagnostic methods.

Due to the non-standardization and complexity of the farmland environment, Global Navigation Satellite System (GNSS) navigation signal may be affected by the tree shade, and visual navigation is susceptible to winged insect and mud, which makes the navigation information of the plant phenotype detection robot unreliable. To solve this problem, this study proposed a multi-sensor information fusion intelligent navigation algorithm based on dynamic credibility evaluation. First, three navigation methods were studied: GNSS and Inertial Navigation System (INS) deep coupling navigation, depth image-based visual navigation, and maize image sequence navigation. Then a credibility evaluation model based on a deep belief network was established, which used dynamically updated credibility to intelligently fuse navigation results to reduce data fusion errors and enhance navigation reliability. At last, the algorithm was loaded on the plant phenotype detection robot for experimental testing in the field. The result shows that the navigation error is within 2.7 cm and the navigation effect of the multi-sensor information fusion method is better than that of the single navigation method. The multi-sensor information fusion method proposed in this study uses the credibility model of the deep belief network to perform navigation information fusion, which can effectively solve the problem of reliable navigation of the plant phenotype detection robot in the complex environment of farmland, and has important application prospects. Keywords: plant phenotype detection, robot, dynamic credibility evaluation, intelligent navigation, multi-sensor information fusion DOI: 10.25165/j.ijabe.20211406.6615 Citation: Lu W, Zeng M J, Qin H H. Intelligent navigation algorithm of plant phenotype detection robot based on dynamic credibility evaluation. Int J Agric & Biol Eng, 2021; 14(6): 195–206.