One-dimensional Deep Convolutional Neural Network Research Articles

Mixture Gases Classification Based on Multi-Label One-Dimensional Deep Convolutional Neural Network

In this paper, we present a novel one-dimensional deep convolutional neural network (1D-DCNN) with a multi-label-way-based algorithm for comprehensively and automatically extracting features and classifying mixture gases. Although a number of pattern recognition methods have been used to analyze the mixed gases, the performances of these methods highly depend on the hand-crafted feature engineering. By contrast, the proposed implementation, based on one-dimensional convolution, is capable of automatically extracting features and distinguishing the individual component of binary mixture gases composed of ethylene, CO, and methane. To the best of our knowledge, the proposed 1D-DCNN algorithm is first applied in the mixture gases’ recognition. In addition, the proposed 1D-DCNN with multi-label way not only significantly reduces the label dimension but also quantifies the probability of each component in mixed gases. Compared with the conventional pattern recognition algorithms including support vector machine, artificial neural network, k-nearest neighbor, and random forest, the proposed 1D-DCNN exhibits a higher recognition accuracy (96.30%) based on our extensive experimental results using ten-fold cross validation.

Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis

Intelligent compound fault diagnosis of rotating machinery plays a crucial role for the security, high-efficiency, and reliability of modern manufacture machines, but identifying and decoupling the compound fault are still a great challenge. The traditional compound fault diagnosis methods focus on either bearing or gear fault diagnosis, where the compound fault is always regarded as an independent fault pattern in the process of fault diagnosis, and the relationship between the single fault and compound fault is not considered completely. To solve such a problem, a novel method called deep decoupling convolutional neural network is proposed for intelligent compound fault diagnosis. First, one-dimensional deep convolutional neural network is employed as the feature learning model, which can effectively learn the discriminative features from raw vibration signals. Second, multi-stack capsules are designed as the decoupling classifier to accurately identify and decouple the compound fault. Finally, the routing by agreement algorithm and the margin loss cost function are utilized to train and optimize the proposed model. The proposed method is validated by gearbox fault tests, and the experimental results demonstrate that the proposed method can effectively identify and decouple the compound fault.