The fault diagnosis of gearbox is a complex and important work. In this paper, a multilayer gated recurrent unit (MGRU) method is proposed for spur gear fault diagnosis, that is, three-layer gated recurrent unit (GRU). The vibration signals are firstly monitored on the test bench, and then extracted in both time domain and time-frequency domain. Finally, the MGRU is used to learn representation and classification. The MGRU can improve the representation of information and identify the features of fault types more precisely with the increasing number of layers. The proposed method was tested by two spur gears with 10 state modes. To evaluate the method’s classification accuracy, four methods were utilized for comparison, i.e., the GRU, long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM), respectively. In addition, the separability and robustness analysis are also discussed for the proposed MGRU performance. All of the results shown that the proposed MGRU approach is effective for spur gear fault diagnosis.

It is an important research topic to learn features automatically from raw signals of wind turbines. Although vibration signals have been widely used for fault diagnosis, the inevitable disturbances and measurement noise make it difficult for vibration signals to completely reflect the fault conditions. In order to capture more sensitive features and generate accurate fault diagnosis results, a multi-source data fusion method based on an autoencoder (AE) is proposed in this paper, where the vibration and motor current signals are adopted. Compared with standard AE, the proposed method applies label information to the training process of AE, which is achieved by adding a new supervised penalty term to the hidden layer of AE. In this way, the learned features are supposed to be close to the class features within, and separate from the inter-class features; thus, the separability of the learned features is enhanced. The proposed method is used to merge the vibration signal and motor current signal of the wind turbine gearbox, and the results show that the fused data generated by the proposed method achieve a higher and more robust performance.

The manuscript proposes a fault diagnosis method based on graph neural network (GNN) with one-shot learning to effectively diagnose rolling bearings under variable operating conditions. In this proposed method, the convolutional neural network is utilized for feature extraction, reducing loss in the process. Subsequently, GNN applies an adjacency matrix to generate codes for one-shot learning. Experimental verification is conducted using open data from Case Western Reserve University Rolling Bearing Data Center, where four different working conditions with six types of typical faults are selected as input signals. The classification accuracy of the proposed method reaches 98.02%. To further validate its effectiveness, traditional single-learning neural networks such as Siamese, Matching Net, Prototypical Net and (Stacked Auto Encoder) SAE are introduced as comparisons. Simulation results that the proposed method outperforms all chosen methods.

The stable operation of sewage treatment is of great significance to controlling regional water environment pollution. It is also important to forecast the inlet water quality accurately, which may ensure the purification efficiency of sewage treatment at a low cost. In this paper, a combined kernel principal component analysis (KPCA) and extreme learning machine (ELM) model is established to forecast the inlet water quality of sewage treatment. Specifically, KPCA is employed for feature extraction and dimensionality reduction of the inlet wastewater quality and ELM is utilized for the future inlet water quality forecasting. The experimental results indicated that the KPCA-ELM model has a higher accuracy than the other comparison PCA-ELM model, ELM model, and back propagation neural network (BPNN) model for forecasting COD and BOD concentration of the inlet wastewater, with mean absolute error (MAE) values of 2.322 mg/L and 1.125 mg/L, mean absolute percentage error (MAPE) values of 1.223% and 1.321%, and root mean square error (RMSE) values of 3.108 and 1.340, respectively. It is recommended from this research that the method may provide a reliable and effective reference for forecasting the water quality of sewage treatment.

Due to the complex working environment, effective fault data from wind turbine gears are often difficult to obtain. Aiming at this practical issue, a generative adversarial network (GAN)-based oversampling method is proposed in this paper, which can achieve fault classification with a small dataset. In the initial stage, wavelet packet transform is applied to generate and extract features. Then, the optimal discriminator and generator trained by GAN are used to generate data to compensate for the imbalanced fault dataset. Random forest, eXtreme gradient boosting and support vector machines are chosen to classify a real dataset, imbalanced dataset and generated dataset, respectively. Experiments indicate that the data generated by the proposed method stay at the same distribution as the real data. Therefore, for small or imbalanced dataset situations, the proposed method could be a solution to compensate for the dataset.

Based on the consumption of fossil energy, the CO₂ emissions of Chongqing are calculated and analyzed from 1997 to 2015 in this paper. Based on the calculation results, the consumption of fossil fuels and the corresponding CO₂ emissions of Chongqing in 2020 are predicted, and the supporting data and corresponding policies are provided for the government of Chongqing to reach its goal as the economic unit of low-carbon emission in the ‘13th Five-Year Plan’. The results of the analysis show that there is a rapid decreasing trend of CO₂ emissions in Chongqing during the ‘12th Five-Year Plan’, which are caused by the adjustment policy of the energy structure in Chongqing. Therefore, the analysis and prediction are primarily based on the adjustment of Chongqing’s coal energy consumption in this paper. At the initial stage, support vector regression (SVR) method is applied to predict the other fossil energy consumption and the corresponding CO₂ emissions of Chongqing in 2020. Then, with the energy intensity of 2015 and the official target of CO₂ intensity in 2020, the total fossil energy consumption and CO₂ emissions of Chongqing in 2020 are predicted respectively. By the above results of calculation, the coal consumption and its corresponding CO₂ emissions of Chongqing in 2020 are determined. To achieve the goal of CO₂ emissions of Chongqing in 2020, the coal consumption level and energy intensity of Chongqing are calculated, and the adjustment strategies for energy consumption structure in Chongqing are proposed.