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

AEDNet: Adaptive Edge-Deleting Network For Subgraph Matching

Subgraph matching is to find all subgraphs in a data graph that are isomorphic to an existing query graph. Subgraph matching is an NP-hard problem, yet has found its applications in many areas. Many learning-based methods have been proposed for graph matching, whereas few have been designed for subgraph matching. The subgraph matching problem is generally more challenging, mainly due to the different sizes between the two graphs, resulting in considerable large space of solutions. Also the extra edges existing in the data graph connecting to the matched nodes may lead to two matched nodes of two graphs having different adjacency structures and often being identified as distinct objects. Due to the extra edges, the existing learning based methods often fail to generate sufficiently similar node-level embeddings for matched nodes. This study proposes a novel Adaptive Edge-Deleting Network (AEDNet) for subgraph matching. The proposed method is trained in an end-to-end fashion. In AEDNet, a novel sample-wise adaptive edge-deleting mechanism removes extra edges to ensure consistency of adjacency structure of matched nodes, while a unidirectional cross-propagation mechanism ensures consistency of features of matched nodes. We applied the proposed method on six datasets with graph sizes varying from 20 to 2300. Our evaluations on six open datasets demonstrate that the proposed AEDNet outperforms six state-of-the-arts and is much faster than the exact methods on large graphs

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

Investigation of equilibrium and dynamic performance of SrCl2-expanded graphite composite in chemisorption refrigeration system

This work experimentally investigated adsorption equilibrium and reaction kinetics of ammonia adsorption/desorption on the composite of strontium chloride (SrCl2) impregnated into expanded graphite, and also discussed the potential influence of the addition of expanded graphite on the SrCl2-NH3 reaction characteristics. The measured and analysed results can be very useful information to design the system and operating conditions using the similar chemisorption composites. Equilibrium concentration characteristics of ammonia within the studied composite were measured using the heat sources at 90 °C, 100 °C and 110 °C for the decomposition process, where the degree of conversion achieved 50%, 78% and 96% respectively. Therefore, the equilibrium equation reflecting the relationship between temperature, pressure and concentration was developed, and a pseudo-equilibrium zone was found, which should be useful information to setup the system operating condition for the desired global transformation. It was suspected that the addition of expanded graphite altered the reaction equilibrium due to the pore effect and the salt-confinement. The concept of two-stage kinetic model was proposed and kinetic parameters were determined by fitting experimental data. The developed kinetic equations can predict dynamic cyclic performance of a reactive bed in similar geometric structure with reasonable accuracy. Such a chemisorption cycle using the SrCl2-expnaded graphite (mass ratio 2:1) composite can be used for cooling application, and the maximum SCP value can be achieved as high as 656 W/kg at t = 2.5 min, and the COP can be 0.3 after one hour of synthesis process under the condition of Tev = 0 °C, Tcon = 20 °C, Theat = 110 °C

Simulation and Analysis of Indoor Visible Light Propagation Characteristics Based on the Method of SBR/Image

The indoor visible light propagation characteristics are simulated and analyzed using the method of SBR/Image (shooting and bounding ray tracing/Image). A good agreement is achieved between the results simulated and the results given in published literature. So the correctness of the method has been validated. Some propagation parameters are obtained in the simulation, such as the indoor received power distribution, statistical distribution of phase angle of received power, RMS (root mean square) delay spread, direction of arrival, and Doppler shift. The foundation for the wireless network coverage of indoor visible light communication system is provided by the analysis of the above results