Intelligent Sensors for Real-Time Decision-Making
Abstract
:1. Introduction
- On industrial architectures and intelligent sensor technologies to achieve the digital twin and support decision-making, relying on the simultaneous acquisition of real-time data from the cloud and edge devices in a cloud-fog-edge architecture.
- An implementation to integrate business data with intelligent sensors for automatic identification, to estimate the duration of tasks and subtasks in real-time, supporting dynamic scheduling in personalized production environments.
2. Architectures of Intelligent Sensors for Decision-Making
2.1. Applications and Use Case
2.1.1. Predictive Models
2.1.2. Dynamic Scheduling
2.2. Automatic Identification
2.2.1. Optical System Identification
2.2.2. Radio System Identification
2.2.3. Real-Time Location Systems
3. Strategy for Integrating Intelligent Sensors
3.1. Methodology
3.2. Connections
3.2.1. PLC Platform
3.2.2. PC Platform
3.2.3. Full Architecture
4. Implementation and Analyses
- RFID reader (read analyst);
- Proximity sensor (trigger Barcode reader);
- Barcode reader (read sample);
- Sample with barcode (test sample);
- RFID tag for user identification;
- Material preparation;
- Local control (PLC / ET);
- HMI (user-sample management);
- Stack light (station status).
- PC with GUI (sample reception and user-sample management);
- Barcode reader (read sample);
- Sample with barcode (test sample);
- RFID tag for user identification;
- RFID reader (read analyst).
- Analyze analyst profiles: helps to analyze the workflow differences between analysts, depending on their different levels of experience and evaluate if there are problems with the training or advantages to having dedicated people to specific tasks;
- Include failure rates: determine how many times particular tasks fail until the analyst moves to the next. This allows the scheduling to include average failure rates;
- Analyze equipment competencies: why in some equipment analysts take more or less time, and in which products;
- Analyze resource availability: currently there is no way to measure the Overall Equipment Effectiveness (OEE) or other Key Performance Indicators (KPIs). This will allow providing information on whether new equipment is necessary, improving resource management;
- Improve Business Process Mapping: the adoption of automatic identification is helping to map and standardize more granular business operations;
- How to react to delays and save time in the passing of work between shifts.
5. Discussion
5.1. Automatic Identification Sensors
5.2. Intelligent Sensors Architectures
6. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Before | After |
---|---|
|
|
Task Times (min) | Actual System | Proposed System | Improvement | Reduction |
---|---|---|---|---|
System Preparation | 139 | 93 | 46 | 33% |
System Suitability | 159 | 102 | 57 | 36% |
Sample Preparation | 143 | 117 | 26 | 18% |
Analytical Run | 221 | 217 | 4 | 2% |
Total | 662 | 529 | 133 | 20% |
Optical System | RFID System | |
---|---|---|
Advantage |
|
|
Disadvantage |
|
|
Measure | PLC Platform | PC Platform |
---|---|---|
Hierarchical | + | − |
Complexity | + | − |
Interoperability | − | + |
Real-Time | − | + |
Security | + | − |
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Coito, T.; Firme, B.; Martins, M.S.E.; Vieira, S.M.; Figueiredo, J.; Sousa, J.M.C. Intelligent Sensors for Real-Time Decision-Making. Automation 2021, 2, 62-82. https://doi.org/10.3390/automation2020004
Coito T, Firme B, Martins MSE, Vieira SM, Figueiredo J, Sousa JMC. Intelligent Sensors for Real-Time Decision-Making. Automation. 2021; 2(2):62-82. https://doi.org/10.3390/automation2020004
Chicago/Turabian StyleCoito, Tiago, Bernardo Firme, Miguel S. E. Martins, Susana M. Vieira, João Figueiredo, and João M. C. Sousa. 2021. "Intelligent Sensors for Real-Time Decision-Making" Automation 2, no. 2: 62-82. https://doi.org/10.3390/automation2020004