In order to calculation the enthalpy of wet steam in the secondary reheat turbine thermal system the thermodynamic system of the secondary reheat steam turbine based on the isentropic ideal expansion ...process line was corrected, This method simplifies the correction calculation step and increases the accuracy of the correction result. Analysis of the rationality of the improved method shows that: Compared with the existing secondary reheat steam turbine thermal system correction method, the maximum error of the improved thermal system correction method is 0.14%. Therefore, this method can better meet the test accuracy requirements.
Intelligent fault diagnosis of machines has long been a research hotspot and has achieved fruitful results. However, intelligent fault diagnosis is a difficult issue in the case of a small sample due ...to the lack of fault signals. To solve this problem, a small sample focused intelligent fault diagnosis method via multimodules gradient penalized generative adversarial networks is proposed. The proposed method consists of three network modules: generator, discriminator, and classifier. By adversarial training, the generator can generate mechanical signals in different health conditions. Because of the high similarity to the signals obtained in practice, the generated signals can also be used as training data so that the limited training dataset of the proposed method is expanded. The classifier has a strong ability to extract fault features from raw mechanical signals and then classify different health conditions. The experimental results on two bearing vibration datasets indicate that the proposed method can not only generate bearing vibration signals but also obtain fairly high fault classificati on accuracy under the small sample condition.
In recent years, the intelligent diagnosis technology of wind turbines has made great progress. However, in practical engineering applications, the operating states of wind turbine are various, ...accompanied by a large number of noise interference, which leads to the decline of the discrimination accuracy of intelligent diagnosis. In order to solve this problem, inspired by the Google team Inception model, this paper proposes a concurrent convolution neural network (C–CNN), the raw data is fed into the network without any prior knowledge, and the characteristics are learned directly and adaptively from the input. Even if the data is accompanied by noise, the model still has high accuracy and strong generalization ability. The model is composed of a CNN with multiple branches. Meanwhile, the convolutional layer of different branches selects the kernels with different scales in same level, thus improving the learning ability of entire network. In this paper, the feasibility of this method for fault diagnosis of bearings in wind turbines is demonstrated by three bearing datasets. The results show that the proposed method can extract discriminative features and classify bearing data accurately under the disturbance of different rotating speed, different load and random noise.
•The C–CNN realize each layer has multi-scale kernels, so as to data features in a multi-angle and deep level.•The C–CNN network not only has good classification accuracy, but also has good generalization ability.•The model can balance the depth and width of the network, and thus control the growth of parameters and calculations.•The feasibility of this method for fault diagnosis of bearings in wind turbines is demonstrated by three data sets.
Deep learning has demonstrated splendid performance in mechanical fault diagnosis on condition that source and target data are identically distributed. In engineering practice, however, the domain ...shift between source and target domains significantly limits the further application of intelligent algorithms. Despite various transfer techniques proposed, either they focus on single-source domain adaptation (SDA) or they utilize multisource domain globally or locally, which both cannot address the cross-domain diagnosis effectively, especially with category shift. To this end, we propose globally localized multisource DA for cross-domain fault diagnosis with category shift. Specifically, we construct a GlocalNet to fuse multisource information comprehensively, which consists of a feature generator and three classifiers. By optimizing the Wasserstein discrepancy of classifiers locally and accumulative higher order multisource moment globally, multisource DA is achieved from domain and class levels thus to reduce the shift on domain and category. To refine the classifier at sample level, a distilling strategy is presented. Finally, an adaptive weighting policy is employed for reliable result. To evaluate the effectiveness, the proposed method is compared with multiple methods on four bearing vibration datasets. Experimental results indicate the superiority and practicability of the proposed method for cross-domain fault diagnosis.
Accurate prediction of remaining useful life (RUL) is necessary to ensure stable and safe operations for rocket engines. The paper proposed a multi-head attention network coupled with adaptive ...meta-transfer learning for RUL prediction. By combining the convolution-based branch with an attention-based branch, the multi-head attention network is proposed for accurate RUL prediction of cryogenic bearings in rocket engines under the steady stage. In addition, an adaptive model-agnostic meta-transfer learning strategy is developed to further improve the performance under small sample circumstances with adaptive hyper-parameters. To demonstrate the superiority, the proposed method is compared with typical benchmark algorithms using real monitoring data from a high-precision cryogenic rocket engine experiment platform. Results indicate that the proposed method achieves better performance compared with existing models under multiple evaluation indexes.
•A multi-head attention network with dual branches is proposed for RUL prediction.•A coupling unit is designed to complete dimension alignment for two branches.•An adaptive meta-transfer learning strategy is proposed for knowledge transfer.•We built a cryogenic experiment platform in liquid nitrogen for verification.
Uncertainty quantification and negative transfer characterization are significant challenges in the context of deep learning-based remaining useful life prediction with limited data. Conventional ...Bayesian methods lack scalability to deep learning algorithms for uncertainty quantification due to intricate network connections and extensive training parameters. Additionally, although transfer learning is a promising way to improve the generalization ability of prediction algorithms, its effectiveness is not always guaranteed when leveraging source data undesirably reduces the prediction performance, which is named negative transfer. This manuscript proposed a meta-weighted neural network equipped with uncertainty estimation to discern in-distribution from out-of-distribution data and an adaptive sample meta re-weighting strategy is designed by specifying the weighting function from prediction loss to sample weight according to the gradient direction. Performance evaluations on cryogenic bearings demonstrate that the proposed algorithm can quantitatively determine the weights of source data based on prediction error, ultimately leading to accurate interval remaining life prediction.
•A meta re-weighting algorithm is proposed for transferability estimation.•Prediction uncertainty is quantified from the game theory perspective.•A cryogenic experiment platform in liquid nitrogen is built for verification.
Class imbalance issue has been a major problem in mechanical fault detection, which exists when the number of instances presenting in a class is significantly fewer than that in another class. This ...article focuses on the problem of zero-shot fault detection of rolling bearing, which is the extreme case of class imbalance. Aiming at this problem, a two-stage zero-shot fault recognition method is proposed. First, inspired by the conditional generative adversarial network, a novel feature generating network which is composed of a feature extractor, a discriminator, and a generator is designed to capture the potential distribution of normal samples. Then, the generator will generate abundant pseudofault features by adding an additional sequence to the condition. Second, an improved deep neural network is trained with these synthetic pseudofault features as the classifier. Specially, a condition index is designed to represent different fault classes so that it can recognize the unseen fault samples. Finally, the effectiveness of the proposed method is verified by three datasets and a comparison method is also given to show the superiority. Results show that the feature generation network can effectively detect the typical faults even though the fault data are unavailable during training, which is practical for industrial application.
The advances of intelligent fault diagnosis in recent years show that deep learning has strong capability of automatic feature extraction and accurate identification for fault signals. Nevertheless, ...data scarcity and varying working conditions can degrade the performance of the model. More recently, a tool has been proposed to address the above challenges simultaneously. Meta-learning, also known as learning to learn, uses a small sample to quickly adapt to a new task. It has great application potential in few-shot and cross-domain fault diagnosis, and thus has become a promising tool. However, there is a lack of a survey to conclude existing work and look into the future. This paper comprehensively investigates deep meta-learning in fault diagnosis from three views: (i) what to use, (ii) how to use, and (iii) how to develop, i.e. algorithms, applications, and prospects. Algorithms are illustrated by optimization-, metric-, and model-based methods, the applications are concluded in few-shot cross-domain fault diagnosis, and open challenges, as well as prospects, are given to motivate the future work. Additionally, we demonstrate the performance of three approaches on two few-shot cross-domain tasks. Typical meta-learning methods are implemented and available at https://github.com/fyancy/MetaFD.
•Review the advances of meta-learning in fault diagnosis for the first time.•Demonstrate deep meta-learning in fault diagnosis via algorithms and applications.•Illuminate meta-learning algorithms by mathematical optimization.•Stimulate future work with open challenges and prospects.
Intelligent fault detection is an important application of artificial intelligence and has been widely used in many mechanical systems. The shipborne antenna that is a typical and an important ...mechanical system plays an irreplaceable role in ships. Considering the tough working environment and heavy background noise, fault detection is difficult for the shipborne antenna. Therefore, the paper presents an intelligent fault detection method via multiscale inner product with locally connected feature extraction for shipborne antenna fault detection. Inspired by inner product principle, this paper takes advantage of inner product to capture fault information in the vibration signals and detect the faults in rolling bearing of the shipborne antenna. Meanwhile, multiscale analysis is employed in two layers of the network to improve the feature extraction ability. The local features under different scales are collected and used for fault classification. Finally, the proposed method is verified by three datasets and comparison methods are also developed to show its superiority. Results show that the proposed method can learn sensitive features directly from raw vibration signals and detect the faults in rolling bearing of shipborne antenna effectively.
Intelligent fault diagnosis has been a promising way for condition-based maintenance. However, the small sample problem has limited the application of intelligent fault diagnosis into real industrial ...manufacturing. Recently, the generative adversarial network (GAN) is considered as a promising way to solve the problem of small sample. For this purpose, this paper reviews the related research results on small-sample-focused fault diagnosis methods using the GAN. First, a systematic description of the GAN, and its variants, including structure-focused and loss-focused improvements, are introduced in the paper. Second, the paper reviews the related GAN-based intelligent fault diagnosis methods and classifies these studies into three main categories, deep generative adversarial networks for data augmentation, adversarial training for transfer learning, and other application scenarios (including GAN for anomaly detection and semi-supervised adversarial learning). Finally, the paper discusses several limitations of existing studies and points out future perspectives of GAN-based applications.
•Generative adversarial network for intelligent fault diagnosis under small sample is discussed.•A systematic description of the generative adversarial network, and its variants, is provided.•Existing studies based on generative adversarial network for mechanical fault diagnosis are systematically reviewed and classified in this paper.•Limitations of existing studies, as well as future perspectives, are provided in this paper.
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