M
Min Xia
Researcher at Lancaster University
Publications - 54
Citations - 2281
Min Xia is an academic researcher from Lancaster University. The author has contributed to research in topics: Computer science & Fault (power engineering). The author has an hindex of 14, co-authored 39 publications receiving 961 citations. Previous affiliations of Min Xia include Northwestern Polytechnical University & University of Science and Technology of China.
Papers
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Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks
TL;DR: In this article, a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery is presented, which incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy.
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Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images
TL;DR: A new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning, which outperforms other cutting edge methods in fault diagnosis of rotor- bearing system.
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Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory
TL;DR: The proposed ELBS method provides optimal scheduling and load balancing for the mixing work robots by using the improved particle swarm optimization algorithm and a multiagent system to achieve the distributed scheduling of manufacturing cluster.
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A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks
TL;DR: An innovative two-stage automated approach to estimate the RUL of bearings using deep neural networks (DNNs) is presented, which has achieved satisfactory prediction performance for a real bearing degradation dataset with different working conditions.
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Multi-scale deep intra-class transfer learning for bearing fault diagnosis
TL;DR: A novel deep transfer learning model called multi-scale deep intra-class adaptation network is constructed, which first uses the modified ResNet-50 to extract low-level features and then constructs a multiple scale feature learner to analyze these low- level features at multiple scales and obtain high-level Features as input for the classifier.