Due to the advantage of automatically extracting features from raw data, deep learning (DL) has been increasingly favored in the field of machine fault diagnosis. However, DL exposes the problems of large sample size and long training time, and in actual working conditions, the amount of labeled fault data available is relatively small, so a DL model of good generalization and high accuracy is difficult to be trained. In order to solve these problems, a deep transfer convolutional neural network (DTCNN) is proposed in this research. ResNet-50 is selected as the pre-trained model of deep convolutional neural network, and is transferred to solve the problem of bearing fault classification based on the idea of transfer learning. Firstly, raw fault signals are converted into time-frequency images by using continuous wavelet transform (CWT). Then, the images are further converted into RGB formats, which are used as the input of DTCNN. Finally, an end-to-end fault diagnosis model based on DTCNN is designed. The proposed method is validated on two datasets collected from motor bearing and self-priming centrifugal pump, respectively. Most sub-datasets from motor bearing show the prediction accuracies near 100%, and in the self-priming centrifugal pump dataset, we achieve improvement in accuracy from 99.48% 0.1966 to 99.98% 0.0332. The experimental results demonstrate that the proposed method outperforms other DL methods and traditional machine-learning methods.
Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.
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