Maintenance Decision-Making Support for Textile Machines: A Knowledge-Based Approach Using Fuzzy Logic and Vibration Monitoring

Baban, Mirjana; Băban, Călin Florin; Suteu, Marius Darius

doi:10.1109/access.2019.2923791

Cited by 14 publications

(8 citation statements)

References 56 publications

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“…Their numerical simulation using MATLAB confirms the effectiveness and security of their data sharing scheme in IoV. Second, Buaban et al [37] (baseline scheme 2) proposed a conditionbased decision-making model that used several fuzzy logic and context-sensitive schemes to identify the legitimacy of the communicating nodes. The decision method chosen to identify the legitimate nodes was TOPSIS that validated the results using MATLAB.…”

Section: B Baseline Schemementioning

confidence: 79%

Trusted Orchestration for Smart Decision-Making in Internet of Vehicles

et al. 2020

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Decision-making is of critical significance in Internet-of-Vehicles (IoV), where vehicles need to quickly make decisions in real-time when sharing or transferring the information. In addition, it is necessary to identify the significant factors of an entity while measuring its legitimacy or to record the real-time data generated by it. Traditional automated schemes in IoV are confronted by the issues related to real-time processing and the manner they respond, such as traffic congestion information, fastest route selection, and road accidental information. The exchange of accurate information among vehicles is critical, but the decision-making for IoV has still not been fully investigated in the literature. Further, the involvement of malicious devices in the network may disgrace the network performance by consuming network resources. In this paper, we propose a hybrid decision-making scheme in vehicular informatics for data transferring and processing through VIKOR and analytic hierarchy process (AHP) methods. The proposed model is scrutinized and verified rigorously through several sensing and decision-making metrics against a conventional solution. The simulation results depict that the proposed model leads to 93 percent competence in terms of decision-making, identification of legitimate sensors, and data alteration process when sharing the information through various sensors in IoV.

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Section: B Baseline Schemementioning

confidence: 79%

Trusted Orchestration for Smart Decision-Making in Internet of Vehicles

et al. 2020

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“…Moreover, a mathematical representation of such relationship is not available in the literature, as far as we know. Considering its capacity to deal with the complex aspects of uncertainty and vagueness that are commonly linked with human intelligence, a fuzzy logic approach was developed to predict the impact of the five OI determinants on the perception of universities as a source for OI activities of firms, as follows (Baban et al, 2019):…”

Section: The Fuzzy Logic-based Analysismentioning

confidence: 99%

Investigating Determinants of Industry–University Collaboration in an Open Innovation Context: Comparative Evidence from an Exploratory Study

Băban

Baban

Rangone

2021

Science, Technology and Society

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In an open innovation (OI) paradigm, universities are considered as important sources of external scientific knowledge for industry, and comparative study of such collaboration can result in more effective and efficient employment of OI. Within this framework, this study explores how the determinants of collaboration between industry and universities in an open context of innovation are addressed by firms within industrial areas. For this purpose, a conceptual framework of industry–university determinants in an open context of innovation is developed from the related literature. Taking into consideration the determinants integrated into the framework, this study compares motives, barriers, channels of knowledge transfer, benefits and drawbacks of such collaboration in two Italian and Romanian industrial areas. Comparative differences in each OI determinant between the firms from the two Italian and Romanian industrial areas are analysed. The associations among the study determinants are also investigated based on correlation matrices among the five determinants in both Italian and Romanian firms. An artificial intelligence approach based on fuzzy logic was developed to predict the impact of the study determinants on the perception of universities as a source for OI activities of firms.

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“…The results show that cloud computing solutions offer convenient tools for users to analyze their equipment data and develop maintenance strategies. Recently, PdM has been employed for maintaining textile manufacturing equipment, including sewing machines [33], textile machines [34], and maintenance scheduling [35]. Existing studies used fuzzy logic [35], statistical methods [36], and data mining to predict textile equipment failure.…”

Section: Yearmentioning

confidence: 99%

“…Recently, PdM has been employed for maintaining textile manufacturing equipment, including sewing machines [33], textile machines [34], and maintenance scheduling [35]. Existing studies used fuzzy logic [35], statistical methods [36], and data mining to predict textile equipment failure. Some studies have indicated that industry 4.0 can assistant enterprises understand their maintenance and production activities [37].…”

Section: Yearmentioning

confidence: 99%

Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

2021

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Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.

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Maintenance Decision-Making Support for Textile Machines: A Knowledge-Based Approach Using Fuzzy Logic and Vibration Monitoring

Cited by 14 publications

References 56 publications

Trusted Orchestration for Smart Decision-Making in Internet of Vehicles

Trusted Orchestration for Smart Decision-Making in Internet of Vehicles

Investigating Determinants of Industry–University Collaboration in an Open Innovation Context: Comparative Evidence from an Exploratory Study

Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

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