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Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

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Advances in Manufacturing 2015年第2期3卷 97-104页

作者： Ke-Sheng Wang Zhe Li JФrgen Braaten Quan Yu Knowledge Discovery Laboratory Department of Production and Quality EngineeringNorwegian University of Science and Technology Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University

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 re- ports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks （ANNs）. The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.

关键词： Backlash error Artificial neural network(ANN) machine centers Predictive maintenance

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Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers： Industry 4.0 scenario

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Advances in Manufacturing 2017年第4期5卷 377-387页

作者： Zhe Li Yi Wang Ke-Sheng Wang Department of Mechanical and Industrial Engineering Norwegian University of Science and TechnologyTrondheim Norway School of Business Plymouth University Plymouth UK

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 accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules： sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.

关键词： Data mining (DM) machine centers Predictive maintenance Industry 4.0

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