A Review of Optimal Sensor Deployment to Diagnose Manufacturing Systems

He, Kang; Jia, Minping; Liu, Conghu

doi:10.1109/access.2018.2834556

Cited by 5 publications

(7 citation statements)

References 95 publications

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“…The layer’s general components installation is critical to the monitoring and diagnosis reliability of manufacturing systems. 27 Then, the main function of the Agent is data acquisition, which is customized according to the information source communication and control format. The information source communication in this study is the PLC information format.…”

Section: Iiot-embedded Ffp Architecturementioning

confidence: 99%

Industrial Internet of Things on integrated preventive maintenance and enterprise-resource-planning systems: A case study of fastener forming manufacturing processes

Hsu

Wang³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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The Industrial Internet of Things (IIoT) has evolved industrial operations to be more efficient and reliable. The fastener forming process (FFP) used to rely on the judgments of in-process inspectors and experienced operators. Since no critical information was transmitted between machines, the machining status during production was unable to reveal. Now, with sensors installed in the machines of FFP, manufacturing data can be collected, analyzed, and responded to the machines. This IIoT-embedded FFP can not only carry out real-time data, pre-processing, and feature engineering but also enable optimally selecting classification algorithms for preventive maintenance. Consequently, the FFP establishes machine-health indicators for the production site and the heading process recognizes the abnormal situations between punches. The operator-delayed response leads to a great amount of loss that can be avoided. Besides, with the integration of enterprise resource planning (ERP) and manufacturing execution system (MES), this IIoT-embedded FFP converts receiving customer orders into work orders and coordinates operations and sales within enterprises. This system integration catches the status of various production indicators and further advances accurate materials preparation and inventory costs control. Overall, this IIoT-embedded FFP system becomes no necessity for increasing inventory and reserving backlog funds to cope with the impact of a vast number of scrap products. Finally, other types of machining processes or systems can adopt this systematic approach in the future.

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Section: Iiot-embedded Ffp Architecturementioning

confidence: 99%

Industrial Internet of Things on integrated preventive maintenance and enterprise-resource-planning systems: A case study of fastener forming manufacturing processes

Hsu

Wang³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…Numerous studies have been carried out and have yielded several encouraging findings. As highlighted by He et al [ 14 ], key information regarding the best sensor placement for diagnosing a production system has been addressed in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the recent research aims to propose approaches that take into account the many objectives involved in sensor arrangement as well as the heterogeneity of sensor properties. Different implementing plans are required for sensor placement and have their own unique characteristics for each different application scenario [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry

Pellicer

Tungekar

Carpitella

2023

Sensors

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Industry 4.0 technologies offer manufacturing companies numerous tools to enhance their core processes, including monitoring and control. To optimize efficiency, it is crucial to effectively install monitoring sensors. This paper proposes a Multi-Criteria Decision-Making (MCDM) approach as a practical solution to the sensor placement problem in the food industry, having been applied to wine bottling line equipment at a real Italian winery. The approach helps decision-makers when discriminating within a set of alternatives based on multiple criteria. By evaluating the interconnections within the different equipment, the ideal locations of sensors are suggested, with the goal of improving the process’s performance. The results indicated that the system of electric pumps, corker, conveyor, and capper had the most influence on the other equipment which are then recommended for sensor control. Monitoring this equipment will result in the early discovery of failures, potentially also involving other dependant equipment, contributing to enhance the level of performance for the whole bottling line.

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“…The mere use of historical data-based approaches may negatively impact the time-critical aspects of monitoring [ 19 ], and result in resource wastage and other difficulties, like increased processing failure, poor product quality, and operational disruptions [ 20 , 21 , 22 , 23 ]. Sensors and signal processing tools have been employed to, respectively, read/collect machine data and analyze it for real-time monitoring of manufacturing systems [ 24 , 25 , 26 ]. Real-time fault detection systems have been successfully employed in the literature [ 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework

Cheng

Pourhejazy

Hung

et al. 2021

Sensors

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Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.

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A Review of Optimal Sensor Deployment to Diagnose Manufacturing Systems

Cited by 5 publications

References 95 publications

Industrial Internet of Things on integrated preventive maintenance and enterprise-resource-planning systems: A case study of fastener forming manufacturing processes

Industrial Internet of Things on integrated preventive maintenance and enterprise-resource-planning systems: A case study of fastener forming manufacturing processes

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry

Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework

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