This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases. We combine a well-designed feature extractor with GAN to help train the whole network. Aimed at obtain data distribution and hidden pattern in both original distinguishing features and latent space, the encoder-decoderencoder three-sub-network is employed in GAN, based on Deep Convolution Generative Adversarial Networks (DCGAN) but without Tanh activation layer and only trained on normal samples. In order to verify the validity and feasibility of our approach, we test it on rolling bearing data from Case Western Reserve University and further verify it on data collected from our laboratory. The results show that our proposed approach can achieve excellent performance in detecting faulty by outputting much larger evaluation scores.

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Diagnosis of ice accretion on wind turbine blades is all the time a hard nut to crack in condition monitoring of wind farms. Existing methods focus on mechanism analysis of icing process, deviation degree analysis of feature engineering. However, there have not been deep researches of neural networks applied in this field at present. Supervisory control and data acquisition (SCADA) makes it possible to train networks through continuously providing not only operation parameters and performance parameters of wind turbines but also environmental parameters and operation modes. This paper explores the possibility that using convolutional neural networks (CNNs), generative adversarial networks (GANs) and domain adaption learning to establish intelligent diagnosis frameworks under different training scenarios. Specifically, PGANC and PGANT are proposed for sufficient and non-sufficient target wind turbine labeled data, respectively. The basic idea is that we consider a two-stage training with parallel GANs, which are aimed at capturing intrinsic features for normal and icing samples, followed by classification CNN or domain adaption module in various training cases. Model validation on three wind turbine SCADA data shows that two-stage training can effectively improve the model performance. Besides, if there is no sufficient labeled data for a target turbine, which is an extremely common phenomenon in real industrial practices, the addition of domain adaption learning makes the trained model show better performance. Overall, our proposed intelligent diagnosis frameworks can achieve more accurate detection on the same wind turbine and more generalized capability on a new wind turbine, compared with other machine learning models and conventional CNNs.

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The present study compared the effects of green tea (–)-epigallocatechin gallate (EGCG) and black tea theaflavin on vascular endothelial cell (VEC) function via the gasotransmitters, hydrogen sulfide (H2S) and nitric oxide (NO). The reaction conditions for cell-free extracts of VECs and L-cysteine were optimized to detect H2S, which was measured using monobromobimane (MBB). EGCG and theaflavin were applied to VECs at 10 µM, and H2S and NO were measured using the MBB method and a NO-specific probe, diaminofluorescein-2 diacetate, respectively. The effects on the functions of VECs were evaluated based on wound healing and cell viability. Theaflavin significantly stimulated H2S production in VECs by 1.51-fold compared to the control, but EGCG showed no effect. In contrast, EGCG and theaflavin increased NO production (1.63 and 2.16-fold), wound healing (1.30- and 1.75-fold), and cell viability (85.0 and 90.0%). The potency of theaflavin was found to be higher than that of EGCG. By inhibiting cystathionine-γ-lyase activity with 100 µM of DL-propargylglycine (PPG) treatment, all cell responses were suppressed in both EGCG and theaflavin treatments, and the reduction rate in the case of theaflavin treatment was higher than that of the control and EGCG treatments in all responses. These results indicate that the protective effect of VECs is dependent on NO production, and that both EGCG and theaflavin have therapeutic potential in VECs. Theaflavin has a relatively higher therapeutic potential than EGCG by increasing H2S production, thereby affecting NO production and biological activity.

Abstract:The barrier toll is a crucial part of a freeway.The bottleneck of a freeway about safety and throughput is usually the fan-in area.In this paper, I put forward the merging model of barrier tolls (MBT) based on cellular automaton(CA) taking various factors which influence the merging of vehicles into consideration.