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.

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.

Early fault detection (EFD) in run-to-failure processes plays a crucial role in the condition monitoring of modern industrial rotating facilities, which entail increasing demands for safety, energy and ecological savings and efficiency. To enable effective protection measures, the evolving faults have to be recognized and identified as early as possible. The major challenge is to distil discriminative features on the basis of only the ‘health’ signal, which is uniquely available from various possible sensors before damage sets in and before the signatures of incipient damage become obvious and well-understood in the signal. Acoustic emission (AE) signals have been frequently reported to be able to deliver early diagnostic information due to their inherently high sensitivity to the incipient fault activities, highlighting the great potential of the AE technique for EFD, which may outperform the traditional vibration-based analysis in many situations. To date, the ‘feature-based’ multivariate analysis dominates the interpretation of AE waveforms. In this way, the decision-making relies heavily on experts’ knowledge and experience, which is often a weak link in the entire EFD chain. With the advent of artificial intelligence, practitioners seek an intelligent method capable of tackling this challenge. In the present paper, we introduce a versatile approach towards intelligent data analysis adapted to AE signals streaming from the sensors used for the continuous monitoring of rotating machinery. A new architecture with a convolutional generative adversarial network (GAN) is designed to extract the deep information embedded in the AE waveforms. In order to improve the robustness of the proposed EFD framework, a novel ensemble technique referred to as ‘history-state ensemble’ (HSE) is introduced and paired with GAN. The primary merits of HSE are twofold: (1) it does not require extra computing time to obtain the base models, and (2) it does not require a special design of the network architecture and can be applied to different networks. To evaluate the proposed method, a durability rolling contact fatigue test was performed with the use of AE monitoring. The experimental results have demonstrated that the proposed ensemble method largely improves the robustness of GAN.

Bearings are a crucial component of wind turbines. The acoustic emission (AE) technique offers the advantage of earlier detection of defects and failures of bearings in comparison to traditional vibration techniques. Parameter-based analysis is the most widely used approach to interpret AE waveforms, partly due to the challenges arising in the processing of large amounts of streaming data. In this work, the AE technique is applied to monitor a run-to-failure process of a roller bearing, and it is found that the use of multiple known parameters, such as the root mean square, skewness, crest factor, impulse factor etc, fails to characterise the evolution of the acquired AE signals, thus highlighting the long-standing necessity and significance of developing new AE indicators that are more adequate to detect the failure of rotating machines. We propose a hybrid parameter—the information entropy penalty factor (IEPF)—which uses the advantages of the entropy theory and deep learning methods. The effectiveness of the proposed method has been investigated and demonstrated for roller bearing contact fatigue experiments, and the results show that IEPF can timely and accurately detect the incipient sub-surface faults.

Due to the harsh working conditions of wind turbines, it is difficult to obtain sufficient fault data from the gearbox. To address this issue, this study proposes a graph neural network model based on few-shot learning (GNN-FSL) for the fault diagnosis on planetary gears of wind turbine gearbox with acoustic signals. The short-time Fourier transform (STFT) is chosen for preprocessing of acoustic signals as input data, which converts the raw data into two-dimensional data. Then, convolutional neural networks (CNN) are used to extract data features. Finally, the extracted features are input for the graph neural network for fault classification. The experimental results show that the model performs well in fault classification on small sample datasets, providing an effective method for fault diagnosis of planetary gears in wind turbines. By comparing EfficientNet-b0, ResNet-50, Densenet121, Mobilenet_v2 and DiffKendall models, it is verified that the proposed method is effective in planetary gear fault classification, and its performance is superior to all these networks.