Deep learning can be applied to the field of fault diagnosis for its powerful feature representation capabilities. When a certain class fault samples available are very limited, it is inevitably to be unbalanced. The fault feature extracted from unbalanced data via deep learning is inaccurate, which can lead to high misclassification rate. To solve this problem, new generator and discriminator of Generative Adversarial Network (GAN) are designed in this paper to generate more discriminant fault samples using a scheme of global optimization. The generator is designed to generate those fault feature extracted from a few fault samples via Auto Encoder (AE) instead of fault data sample. The training of the generator is guided by fault feature and fault diagnosis error instead of the statistical coincidence of traditional GAN. The discriminator is designed to filter the unqualified generated samples in the sense that qualified samples are helpful for more accurate fault diagnosis. The experimental results of rolling bearings verify the effectiveness of the proposed algorithm.

Deep Learning Fault Diagnosis Method Based on Global Optimization GAN for Unbalanced Data

A systematic review of deep transfer learning for machinery fault diagnosis

\n Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification, has been attracting the attention of researchers. Nowadays, the research on S&I-IFD has achieved fruitful results, but a review of the latest achievements is still lacking, and the future research directions are not clear enough. To address this, we review the research results on S&I-IFD and provides some future perspectives in this paper. The existing research results are divided into three categories: the data augmentation-based, the feature learning-based, and the classifier design-based. Data augmentation-based strategy improves the performance of diagnosis models by augmenting training data. Feature learning-based strategy identifies faults accurately by extracting features from small & imbalanced data. Classifier design-based strategy achieves high diagnosis accuracy by constructing classifiers suitable for small & imbalanced data. Finally, this paper points out the research challenges faced by S&I-IFD and provides some directions that may bring breakthroughs, including meta-learning and zero-shot learning.

Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.

Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition

The film cooling of cylindrical holes embedded in transverse trenches under superposition has shown promise for protecting the critical components of a high-pressure turbine from thermal damage. To optimize the relevant parameters and provide a suitable film cooling strategy, it is important to predict the effectiveness of lateral-averaged adiabatic film cooling with the trench effect on the surface of a blade. However, high-fidelity semi-empirical correlations for film cooling under superposition conditions with a trench have rarely been examined. This study establishes a gated recurrent unit (GRU) neural network model to predict the effectiveness of lateral-averaged film cooling under multiple-row superposition conditions with a trench. In general, a gated recurrent unit (GRU) neural network model is built with a large sequence of one-dimensional (1D) parameters, including the depth and width of the trench, compound angle, location of the hole, and blowing ratio. The computational fluid dynamics (CFD) method is used to provide a training dataset for the model. After careful testing and validation, the results predicted by the GRU agreed well with the CFD results. Moreover, the performance and robustness of the GRU were better than those of other recurrent neural network model, such as the long short-term memory (LSTM) model. Integrated with the GRU model, the sparrow search algorithm was adopted to optimize the parameters of the trench. The film cooling effectiveness of the optimized case improved by 1.6% compared with the best case, 28.5% compared with the worst case in dataset and 23.5% compared with the no-trench case.

Predicting and Optimizing Multirow Film Cooling with Trenches Using Gated Recurrent Unit Neural Network

Uncertainty quantification of the superposition film cooling with trench using supervised machine learning

An optimization model based on Particle Swarm Optimization-Support Vector Machine(PSO-SVM) and improved NSGA-II algorithm is established in this paper to balance the heat dissipation performance and lightweight of battery. First, the data set is obtained by orthogonal experiments, and then the PSO-SVM model for predicting the battery heat dissipation performance (maximum temperature and maximum temperature difference) is acquired by optimizing the parameters of the support vector machine through particle swarm optimization. With the goal of improving the battery heat dissipation performance and reducing the system mass, the improved NSGA-II algorithm is used for multi-objective optimization to obtain the Pareto solution set. Results show that the R2 (goodness of fit) of the maximum temperature and maximum temperature difference prediction models are 0.99478 and 0.9787, respectively, which verifies the accuracy of the PSO-SVM model. The maximum temperature, the maximum temperature difference, and the mass of the phase change material are in conflict. The temperature rise and temperature difference of the battery can be effectively controlled by sacrificing the mass of the phase change material. Four optimal solutions were selected by using LINMAP and TOPSIS. Compared with the initial model, the mass of phase change materials was reduced by 34.5 %, 29.5 %, 55.3 %, and 2.5 %. On the premise of meeting the requirements of thermal management performance, the entire system becomes lighter. Based on the four optimal solutions, the decision maker can choose from the Pareto optimal solution set according to different requirements for heat dissipation performance and lightweight. 

Multi-objective optimization of phase change cooling battery module based on PSO-SVM

Intelligent power grid fault diagnosis is of great significance for speeding up fault processing and improving fault diagnosis efficiency. However, most of the current fault diagnosis methods focus on rule diagnosis, relying on expert experience and logical rules to build a diagnosis model, and lack the ability to automatically extract fault knowledge. For switch refusal events, it is difficult to determine a refusal switch without network topology. In order to realize the non-operating switch identification without network topology, this paper proposes a power grid fault diagnosis method based on deep reinforcement learning for alarm information text. Taking the single alarm information of the non-switch refusal sample as the research object, through the self-learning ability of deep reinforcement learning, it learns the topology connection relationship and action logic relationship between equipment, protection and circuit breakers contained in the alarm information, and realizes the detection of fault events. The correct prediction of the fault removal process after the occurrence, based on this, determines the refusal switch when the switch refuses to operate during the fault removal process. The calculation example results show that the proposed method can effectively diagnose the refusal switch of the switch refusal event, which is feasible and effective.

/pdf/power-system-fault-diagnosis-method-based-on-deep-2bn2udg7.pdf

Power System Fault Diagnosis Method Based on Deep Reinforcement Learning

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