This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.

Predictions of renewable energy (RE) generation and electricity load are critical to smart grid operation. However, the prediction task remains challenging due to the intermittent and chaotic character of RE sources, and the diverse user behavior and power consumers. This article presents a novel method for the prediction of RE generation and electricity load using improved stacked gated recurrent unit-recurrent neural network (GRU-RNN) for both univariate and multivariate scenarios. First, multiple sensitive monitoring parameters or historical electricity consumption data are selected according to the correlation analysis to form the input data. Second, a stacked GRU-RNN using a simplified GRU is constructed with improved training algorithm based on AdaGrad and adjustable momentum. The modified GRU-RNN structure and improved training method enhance training efficiency and robustness. Third, the stacked GRU-RNN is used to establish an accurate mapping between the selected variables and RE generation or electricity load due to its self-feedback connections and improved training mechanism. The proposed method is verified by using two experiments: prediction of wind power generation using multiple weather parameters and prediction of electricity load with historical energy consumption data. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods of machine learning or deep learning in achieving an accurate energy prediction for effective smart grid operation.

Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.

The degradation of bearings plays a key role in the failures of industrial machinery. Prognosis of bearings is critical in adopting an optimal maintenance strategy to reduce the overall cost and to avoid unwanted downtime or even casualties by estimating the remaining useful life (RUL) of the bearings. Traditional data-driven approaches of RUL prediction rely heavily on manual feature extraction and selection using human expertise. This paper presents an innovative two-stage automated approach to estimate the RUL of bearings using deep neural networks (DNNs). A denoising autoencoder-based DNN is used to classify the acquired signals of the monitored bearings into different degradation stages. Representative features are extracted directly from the raw signal by training the DNN. Then, regression models based on shallow neural networks are constructed for each health stage. The final RUL result is obtained by smoothing the regression results from different models. The proposed approach has achieved satisfactory prediction performance for a real bearing degradation dataset with different working conditions.