ArticleSensors (Basel, Switzerland)2019
A Mission Reliability-Driven Manufacturing System Health State Evaluation Method Based on Fusion of Operational Data.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems.Sensors (Basel, Switzerland) · 2020Article
- A Comprehensive Evaluation Method of Sensor Selection for PHM Based on Grey Clustering.Sensors (Basel, Switzerland) · 2020Article
- A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs).Sensors (Basel, Switzerland) · 2019Article
- Risk-Oriented Product Assembly System Health Modeling and Predictive Maintenance Strategy.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The rapid development of complexity and intelligence in manufacturing systems leads to an increase in potential operational risks and therefore requires a more comprehensive system-level health diagnostics approach. Based on the massive multi-source operational data collected by smart sensors, this paper proposes a mission reliability-driven manufacturing system health state evaluation method. Characteristic attributes affecting the mission reliability are monitored and analyzed based on different sensor groups, including the performance state of the manufacturing equipment, the execution state of the production task and the quality state of the manufactured product. The Dempster-Shafer (D-S) evidence theory approach is used to diagnose the health state of the manufacturing system. Results of a case study show that the proposed evaluation method can dynamically and effectively characterize the actual health state of manufacturing systems.
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Registered trials
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