Browsing NTNU Open by Author "Li, Zhe"
Now showing items 1-12 of 12
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A data-driven method based on deep belief networks for backlash error prediction in machining centers
Li, Zhe; Wang, Yi; Wang, Kesheng (Journal article; Peer reviewed, 2017)Backlash error occurs in a machining center may lead to a series of changes in the geometry of the components and subsequently deteriorate the overall performance of the equipment. Due to the uncertainty of mechanical wear ... -
A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment
Li, Zhe; Li, Jingyue; Wang, Yi; Wang, Kesheng (Journal article; Peer reviewed, 2019)Anomaly in mechanical systems may cause equipment to break down with serious safety, environment, and economic impact. Since many mechanical equipment usually operates under tough working environments, which makes them ... -
Ag Nanoparticle-Coated Polystyrene Microspheres for Electromagnetic Interference Shielding Films with Low Metal Content
Cheng, Hua; Liu, Siqi; Wang, Ruiqi; Zhang, Wei; Pan, Rui; Li, Zhe; Gong, Yi; wang, Fangkuo; hu, rui; Ding, Jianjun; Zhang, Xian; Chen, Lin; He, Jianying; Tian, Xingyou (Peer reviewed; Journal article, 2022)The fabrication of thin composite films incorporating metal-based fillers with a delicate structure to achieve high electromagnetic interference shielding effectiveness (EMI SE) at low metal content remains a great challenge. ... -
Deep Learning Driven Approaches for Predictive Maintenance: A Framework of Intelligent Fault Diagnosis and Prognosis in the Industry 4.0 Era
Li, Zhe (Doctoral theses at NTNU;2018:132, Doctoral thesis, 2018) -
Deep transfer learning for failure prediction across failure types
Li, Zhe; Kristoffersen, Eivind; Li, Jingyue (Journal article; Peer reviewed, 2022) -
HDPS-BPSO Based Predictive Maintenance Scheduling for Backlash Error Compensation in a Machining Center
Li, Zhe; Wang, Yi; Wang, Kesheng; Li, Jingyue (LNEE;volume 484, Journal article; Peer reviewed, 2018)This paper presents a novel HDPS-BPSO maintenance scheduling strategy for backlash error compensation in a machining center through binary particle swarm optimization (BPSO) and data-driven regression methods. During the ... -
Industry 4.0 - Potentials for Predictive Maintenance
Li, Zhe; Wang, Kesheng; He, Yafei (Advances in Economics, Business and Management Research;, Chapter, 2016)Abstract: Industry 4.0 represents the coming fourth industrial revolution on the way to combine modern industries with Cyber-Physical Systems, Internet of Things and Internet of Services. In an Industry 4.0 factory, machines ... -
Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario
Li, Zhe; Wang, Yi; Wang, Kesheng (Journal article; Peer reviewed, 2017)Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the ... -
Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs
Wang, Kesheng; Li, Zhe; Braaten, Jørgen; Yu, Quan (Journal article; Peer reviewed, 2015)It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article reports ... -
Knowledge Discovery and Anomaly Identification for Low Correlation Industry Data
Li, Zhe; Li, Jingyue (Peer reviewed; Journal article, 2020)With the development of information technology, industry data is increasingly generated during the manufacturing process. Companies often want to utilize the data they collected for more than the initial purposes. In this ... -
Oppdagelse av Feil i Sensordata for for Ikke-Veiledet Lange Tidsserier
Dahling, Cornelius Grieg (Master thesis, 2019)Å vedlikeholde stort maskineri, er noen av de største kostnadene som fører med til drift av store maskiner. Med den stadig økende populæriteten til Maskinlæring, har vi de siste årene sett stadig nye metoder for å finne ... -
Smart Maintenance in Asset Management – Application with Deep Learning
Rødseth, Harald; Eleftheriadis, Ragnhild; Li, Zhe; Li, Jingyue (Chapter, 2020)With the onset the digitalization and Industry 4.0, the maintenance function and asset management in a company is forming towards Smart Maintenance. An essential application in smart maintenance is to improve the maintenance ...