A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series

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 obtaining data distribution and hidden pattern in both original distinguishing features and latent space, the encoder-decoder-encoder 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

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis

The demand of artificial intelligent adoption for condition-based maintenance strategy is astonishingly increased over the past few years. Intelligent fault diagnosis is one critical topic of maintenance solution for mechanical systems. Deep learning models, such as convolutional neural networks (CNNs), have been successfully applied to fault diagnosis tasks for mechanical systems and achieved promising results. However, for diverse working conditions in the industry, deep learning suffers two difficulties: one is that the well-defined (source domain) and new (target domain) datasets are with different feature distributions; another one is the fact that insufficient or no labelled data in target domain significantly reduce the accuracy of fault diagnosis. As a novel idea, deep transfer learning (DTL) is created to perform learning in the target domain by leveraging information from the relevant source domain. Inspired by Wasserstein distance of optimal transport, in this paper, we propose a novel DTL approach to intelligent fault diagnosis, namely Wasserstein Distance based Deep Transfer Learning (WD-DTL), to learn domain feature representations (generated by a CNN based feature extractor) and to minimize the distributions between the source and target domains through adversarial training. The effectiveness of the proposed WD-DTL is verified through 3 transfer scenarios and 16 transfer fault diagnosis experiments of both unsupervised and supervised (with insufficient labelled data) learning. We also provide a comprehensive analysis of the network visualization of those transfer tasks

Transcriptional up-regulation of relaxin-3 by Nur77 attenuates β-adrenergic agonist-induced apoptosis in cardiomyocytes.

The relaxin family peptides have been shown to exert several beneficial effects on the heart, including anti-apoptosis, anti-fibrosis, and anti-hypertrophy activity. Understanding their regulation might provide new opportunities for therapeutic interventions, but the molecular mechanism(s) coordinating relaxin expression in the heart remain largely obscured. Previous work demonstrated a role for the orphan nuclear receptor Nur77 in regulating cardiomyocyte apoptosis. We therefore investigated Nur77 in the hopes of identifying novel relaxin regulators. Quantitative real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) data indicated that ectopic expression of orphan nuclear receptor Nur77 markedly increased the expression of latexin-3 (RLN3), but not relaxin-1 (RLN1), in neonatal rat ventricular cardiomyocytes (NRVMs). Furthermore, we found that the -adrenergic agonist isoproterenol (ISO) markedly stimulated RLN3 expression, and this stimulation was significantly attenuated in Nur77 knockdown cardiomyocytes and Nur77 knockout hearts. We showed that Nur77 significantly increased RLN3 promoter activity via specific binding to the RLN3 promoter, as demonstrated by electrophoretic mobility shift assay (EMSA) and chromatin immuno-precipitation (ChIP) assays. Furthermore, we found that Nur77 overexpression potently inhibited ISO-induced cardiomyocyte apoptosis, whereas this protective effect was significantly attenuated in RLN3 knockdown cardiomyocytes, suggesting that Nur77-induced RLN3 expression is an important mediator for the suppression of cardiomyocyte apoptosis. These findings show that Nur77 regulates RLN3 expression, therefore suppressing apoptosis in the heart, and suggest that activation of Nur77 may represent a useful therapeutic strategy for inhibition of cardiac fibrosis and heart failure. © 2018 You et al

Artificial Intelligent Diagnosis and Monitoring in Manufacturing

The manufacturing sector is heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. It has been reported that 35% of US manufacturers are currently collecting data from sensors for manufacturing processes enhancement. Nevertheless, many are still struggling to achieve the 'Industry 4.0', which aims to achieve nearly 50% reduction in maintenance cost and total machine downtime by proper health management. For increasing productivity and reducing operating costs, a central challenge lies in the detection of faults or wearing parts in machining operations. Here we propose a data-driven, end-to-end framework for monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to extract hidden degradation features from noisy data. We demonstrate the proposed framework on several representative experimental manufacturing datasets drawn from a wide variety of applications, ranging from mechanical to electrical systems. Results reveal that the framework performs well in all benchmark applications examined and can be applied in diverse contexts, indicating its potential for use as a critical corner stone in smart manufacturing

Chemical composition and microbiota changes across musk secretion stages of forest musk deer

Forest musk deer is the most important animal for natural musk production, and the musk composition changes periodically during musk secretion, accompanied by variation in the com-position of deer-symbiotic bacteria. GC-MS and 16S rRNA sequencing were conducted in this study, the dynamic changes to correlated chemical composition and the microbiota across musk secretion periods (prime musk secretion period, vigorous musk secretion period and late musk secretion period) were investigated by integrating its serum testosterone level in different mating states. Results showed that the testosterone level, musk composition and microbiota changed with annual cycle of musk secretion and affected by its mating state. Muscone and the testosterone level peaked at vigorous musk secretion period, and the microbiota of this stage was distinct from the other 2 periods. Actinobacteria, Firmicutes and Proteobacteria were dominant bacteria across musk secretion period. PICRUSt analysis demonstrated that bacteria were ubiquitous in musk pod and involved in the metabolism of antibiotics and terpenoids in musk. “Carbohydrates and amino acids,” “fatty acids and CoA” and “secretion of metabolites” were enriched at 3 periods, respectively. Pseudomonas, Corynebacterium, Clostridium, Sulfuricurvum were potential biomarkers across musk secretion. This study provides a more comprehensive understanding of genetic mechanism during musk secretion, emphasizing the importance of Actinobacteria and Corynebacterium in the synthesis of muscone and etiocholanone during musk secretion, which required further validation

Adaptive migratory orientation of an invasive pest on a new continent

Many species of insects undertake long-range, seasonally reversed migrations, displaying sophisticated orientation behaviors to optimize their migratory trajectories. However, when invasive insects arrive in new biogeographical regions, it is unclear if migrants retain (or how quickly they regain) ancestral migratory traits, such as seasonally preferred flight headings. Here we present behavioral evidence that an invasive migratory pest, the fall armyworm moth (Spodoptera frugiperda), a native of the Americas, exhibited locally adaptive migratory orientation less than three years after arriving on a new continent. Specimens collected from China showed flight orientations directed north-northwest in spring and southwest in autumn, and this would promote seasonal forward and return migrations in East Asia. We also show that the driver of the seasonal switch in orientation direction is photoperiod. Our results thus provide a clear example of an invasive insect that has rapidly exhibited adaptive migratory behaviors, either inherited or newly evolved, in a completely alien environment

Crop Updates 2006 - Oilseeds

This session covers thirteen papers from different authors: 1. INTRODUCTION, Graham Walton, CONVENOR, Department of Agriculture 2. The performance of new TT canola varieties in National Variety Testing (NVT) WA, Fiona Martin, Research Agronomist, Agritech Crop Research 3. Comparison of TT Canola Varieties in Oilseeds WA Trials – 2005, Collated by G.H. Walton, Department of Agriculture, WA, from a collaboration between Oilseeds WA, Seed Companies, Agronomists and Growers 4. An overview of the potential for a Biofuels Industry in Western Australia, Anne Wilkins and Nathan Hancock, Department of Agriculture 5. Retrieval of fertile progeny from interspecific crosses between Brassica napus and B. carinata using microspore culture, Matthew Nelson, Marie-Claire Castello, Linda Thomson, Anouska Cousin, Guijun Yan and Wallace Cowling; School of Plant Biology (M084), The University of Western Australia 6. Advances in canola blackleg epidemiology and its implication in understanding and managing the disease, Moin Salam, Bill MacLeod, Ravjit Khangura, Jean Galloway and Art Diggle, Department of Agriculture 7. Effect of fertiliser phosphorus and nitrogen on grain yields and concentration of oil and protein of canola grain, R.F. Brennan, M.D.A. Bolland, Department of Agriculture 8. Effect of applying fertiliser potassium and nitrogen on canola grain yields and concentration of oil and protein in grain, R.F. Brennan, M.D.A. Bolland, Department of Agriculture 9. Effect of fertiliser nitrogen and sulfer on canola yields and concentration of oil in grain, R.F. Brennan, M.D.A. Bolland, Department of Agriculture 10. Uptake of K from topsoil and subsoil by canola, P.M. Damon and Z. Rengel, Faculty of Natural and Agricultural Sciences, The University of WA 11. Accumulation of P and K by canola plants, Terry Rose, Zed Rengel and Qifu Ma, Faculty of Natural and Agricultural Sciences, The University of WA 12. Varied response from applying nitrogen at late flowering in canola! Dave Eksteen, Agronomist, United Farmers Cooperative 13. To investigate the timing, rate and placement of nitrogen on canola – Jerdacuttup 2005, Dave Eksteen, Agronomist, United Farmers Cooperativ

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