With the assumption of sufficient labeled data, deep learning based machinery fault diagnosis methods show effectiveness. However, in real-industrial scenarios, it is costly to label the data, and unlabeled data is underutilized. Therefore, this paper proposes a semi-supervised fault diagnosis method called Bidirectional Wasserstein Generative Adversarial Network with Gradient Penalty (BiWGAN-GP). First, by unsupervised pre-training, the proposed method takes full advantage of a large amount of unlabeled data and can extract features from vibration signals effectively. Then, using only a few labeled data to conduct supervised fine-tuning, the model can perform an accurate fault diagnosis. Additionally, Wasserstein distance is used to improve the stability of the model’s training procedure. Validation is performed on the bearing and gearbox fault datasets with limited labeled data. The results show that the proposed method can achieve 99.42% and 91.97% of diagnosis accuracy on the bearing and gear dataset, respectively, when the size of the training set is only 10% of the testing set.
In this study, event‐triggered fault estimation (FE) problem for a class of discrete‐time dynamic systems subject to sector‐bounded nonlinearity and time‐varying coefficients is investigated. For a given event‐triggered measurement transmission scheme, the event‐induced output non‐persistence for the fault estimator is modeled by norm‐bounded observation uncertainty. After giving a suitable H∞ performance index and formulating the estimation problem for the concerned nonlinear system with event‐triggered measurements, an auxiliary model in a quasi‐linear form and an associated H∞ performance function are established. With the aid of this auxiliary model and performance function, the sector‐bounded nonlinearity condition and the induced observation uncertainty are packaged simultaneously, and the considered H∞ FE problem in Hilbert space is recast as an H2 deconvolution filtering issue in Krein space. Through designing Krein space based model with appropriate inner products, and using the orthogonally projection technique, fault estimator is derived in an analytical and recursive manner. The condition that ensures the existence of the estimator is also obtained. Two examples are adopted to demonstrate the applicability of the proposed method.
At present, the operators needs to carry out complicated teaching and programming work on the welding path planning for the welding robot before welding the steel mesh. In this work, an automatic welding path planning method of steel mesh based on point cloud is proposed to simplify the complicated teaching and programming work in welding path planning. The point cloud model of steel mesh is obtained by three-dimensional vision structured light camera. Then we use the relevant point cloud processing algorithm to calculate the welding path of the steel mesh, and obtain the 3D information of the welding path for the welding localization of the robot welding process. Experimental results show that the method can accurately realize the welding path planning of the steel mesh and accomplish the welding task without teaching and programming before welding, which improves the production efficiency.Keywords Without teaching and programming • 3D structured light camera • Steel mesh point cloud • Welding path planning
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