Search Results - "IEEE transactions on industrial electronics (1982)"

Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data by Guo, Liang, Lei, Yaguo, Xing, Saibo, Yan, Tao, Li, Naipeng

“…The success of intelligent fault diagnosis of machines relies on the following two conditions: 1) labeled data with fault information are available; and 2) the…”
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Wireless Power Transfer-An Overview by Zhang, Zhen, Pang, Hongliang, Georgiadis, Apostolos, Cecati, Carlo

“…Due to limitations of low power density, high cost, heavy weight, etc., the development and application of battery-powered devices are facing with…”
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Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox by Jiang, Guoqian, He, Haibo, Yan, Jun, Xie, Ping

“…This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike…”
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Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach by Chen, Zhenghua, Wu, Min, Zhao, Rui, Guretno, Feri, Yan, Ruqiang, Li, Xiaoli

“…For prognostics and health management of mechanical systems, a core task is to predict the machine remaining useful life (RUL). Currently, deep structures with…”
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Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development by Yuan, Xiaofeng, Li, Lin, Shardt, Yuri A. W., Wang, Yalin, Yang, Chunhua

“…Industrial process data are naturally complex time series with high nonlinearities and dynamics. To model nonlinear dynamic processes, a long short-term memory…”
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Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network by Zhu, Jun, Chen, Nan, Peng, Weiwen

“…Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper,…”
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SDDNet: Real-Time Crack Segmentation by Choi, Wooram, Cha, Young-Jin

“…This article reports the development of a pure deep learning method for segmenting concrete cracks in images. The objectives are to achieve the real-time…”
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Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets by Shi, Qian, Zhang, Hui

“…Safety is one of the key requirements for automated vehicles and fault diagnosis is an effective technique to enhance the vehicle safety. The model-based fault…”
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Multiobjective Design Optimization of an IPMSM for EVs Based on Fuzzy Method and Sequential Taguchi Method by Sun, Xiaodong, Shi, Zhou, Zhu, Jianguo

“…The Taguchi optimization method is an efficient method for motor design optimization. However, it is hard to handle the multiobjective motor optimization…”
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Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture by Yang, Boyuan, Liu, Ruonan, Zio, Enrico

“…Remaining useful life (RUL) prediction has been increasingly considered in many industrial fields for the reliability and safety of their systems. As a data…”
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Two-Dimensional Memristive Hyperchaotic Maps and Application in Secure Communication by Li, Houzhen, Hua, Zhongyun, Bao, Han, Zhu, Lei, Chen, Mo, Bao, Bocheng

“…Continuous memristor has been widely used in chaotic oscillating circuits and neuromorphic computing systems. However, discrete memristor and its coupling…”
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Subdomain Adaptation Transfer Learning Network for Fault Diagnosis of Roller Bearings by Wang, Zhijian, He, Xinxin, Yang, Bin, Li, Naipeng

“…Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in one scene, likely fails in classifying by unlabeled data…”
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A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches by Zhiwei Gao, Cecati, Carlo, Ding, Steven X.

“…With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and…”
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A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions by Huang, Cheng-Geng, Huang, Hong-Zhong, Li, Yan-Feng

“…Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-based prognostic…”
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Recent Advances in Consensus of Multi-Agent Systems: A Brief Survey by Qin, Jiahu, Ma, Qichao, Shi, Yang, Wang, Long

“…In this paper, we mainly review the topics in consensus and coordination of multi-agent systems, which have received a tremendous surge of interest and…”
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Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks by Kiranyaz, Serkan, Gastli, Adel, Ben-Brahim, Lazhar, Al-Emadi, Nasser, Gabbouj, Moncef

“…Automated early detection and identification of switch faults are essential in high-voltage applications. Modular multilevel converter (MMC) is a new and…”
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Time-Varying Formation Tracking for Second-Order Multi-Agent Systems Subjected to Switching Topologies With Application to Quadrotor Formation Flying by Dong, Xiwang, Zhou, Yan, Ren, Zhang, Zhong, Yisheng

“…Time-varying formation tracking analysis and design problems for second-order Multi-Agent systems with switching interaction topologies are studied, where the…”
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Distributed Formation Control of Networked Multi-Agent Systems Using a Dynamic Event-Triggered Communication Mechanism by Ge, Xiaohua, Han, Qing-Long

“…This paper addresses the distributed formation control problem of a networked multi-agent system (MAS) subject to limited communication resources. First, a…”
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Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis by Miao, Yonghao, Zhang, Boyao, Li, Chenhui, Lin, Jing, Zhang, Dayi

“…In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored for the feature extraction of machinery fault. The proposed FMD is…”
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An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data by Lei, Yaguo, Jia, Feng, Lin, Jing, Xing, Saibo, Ding, Steven X.

“…Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and…”
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