We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligen...We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligent decision-making tech-niques, which can be used by maintenance professionals who are working with cutting-edge equipment. The systems will provide technical solutions to real-time online maintenance tasks, avoid outages due to equipment failures, and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules, i.e., cyber-physical system, Internet of Things, data mining, and Internet of Services. In the data mining module, fault diagnosis and prediction are realized by deep learning methods. In the case study, the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool, so as to predict the possible failure of the machine tool, and realize the strategy of CM. Through the case study, we discuss the significance of implementing CM for cutting-edge equipment, and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.展开更多
It is difficult to qualitatively evaluate the design effects of product appearance. Electroencephalograph （EEG） and eye-tracking data can serve as reflection of the subcon- scious activities of human beings. The app...It is difficult to qualitatively evaluate the design effects of product appearance. Electroencephalograph （EEG） and eye-tracking data can serve as reflection of the subcon- scious activities of human beings. The application of advanced neuroscience technology in industrial operation management has become a new research hot spot. This study uses EEG equipment and an eye-tracking device to record a subject＇s brain activity and eye-gaze data, and then uses data mining methods to analyze the correlation between the two types of signals. The fuzzy theory is then applied to create a fuzzy comprehensive evaluation model. The neural attributes are used to quantify the factors affected by product appear- ance and evaluation indicators. We use women＇s shirts as research subjects for a case study. The EEG Emotiv device and Tobii mobile eye-tracking glasses are used to record a subject＇s brain activity and eye-gaze data in order to quantify the evaluation factors related to product appearance. This method not only scientifically evaluates the uniqueness of product appearance but also provides an objective reference for improving product appearance design.展开更多
文摘We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligent decision-making tech-niques, which can be used by maintenance professionals who are working with cutting-edge equipment. The systems will provide technical solutions to real-time online maintenance tasks, avoid outages due to equipment failures, and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules, i.e., cyber-physical system, Internet of Things, data mining, and Internet of Services. In the data mining module, fault diagnosis and prediction are realized by deep learning methods. In the case study, the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool, so as to predict the possible failure of the machine tool, and realize the strategy of CM. Through the case study, we discuss the significance of implementing CM for cutting-edge equipment, and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.
文摘It is difficult to qualitatively evaluate the design effects of product appearance. Electroencephalograph （EEG） and eye-tracking data can serve as reflection of the subcon- scious activities of human beings. The application of advanced neuroscience technology in industrial operation management has become a new research hot spot. This study uses EEG equipment and an eye-tracking device to record a subject＇s brain activity and eye-gaze data, and then uses data mining methods to analyze the correlation between the two types of signals. The fuzzy theory is then applied to create a fuzzy comprehensive evaluation model. The neural attributes are used to quantify the factors affected by product appear- ance and evaluation indicators. We use women＇s shirts as research subjects for a case study. The EEG Emotiv device and Tobii mobile eye-tracking glasses are used to record a subject＇s brain activity and eye-gaze data in order to quantify the evaluation factors related to product appearance. This method not only scientifically evaluates the uniqueness of product appearance but also provides an objective reference for improving product appearance design.
