8,895 research outputs found
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 -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
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