The global surge of the green movement, in alignment with government policies on achieving net-zero emissions, international carbon border taxes, and regulations related to green supply chains, has put Taiwan's predominantly high carbon-emission and energy-intensive industries under the urgent need for adaptation. Most commercially available energy management systems typically only interface with digital meters to record equipment operational energy consumption. Augmented by management systems, customers can intuitively access data on the energy consumption of various devices, aiming to identify and replace high-energy-consuming equipment as part of energy-saving and carbon reduction goals. However, traditional information on machine operation and capacity often relies on manual measurements and data entry, introducing inaccuracies and the risk of human-induced errors, thereby compromising the reliability of information provided by energy management systems.
MiTAC Energy Management System employs vibration measurement to analyze equipment operating conditions, accurately recording processing times to provide authentic and reliable information on energy consumption corresponding to unit capacity. Through AI algorithms cross-referencing vibration frequencies and other key IoT data, the system can meticulously assess various operational time points such as machine adjustment, calibration, material change, and maintenance. This enhances human-machine efficiency, improves output performance, ensures work quality, and reduces resource wastage, effectively achieving ESG sustainable development goals. Leveraging AI deep learning technology, the system can estimate the Energy Baseline (EnB) and Energy Performance Indicators (EnPI), offering effective data for evaluating overall energy-saving and carbon reduction initiatives.
Product Features
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AI Diagnostics and Predictive Technology Utilizes non-intrusive vibration sensors to capture operational vibration frequencies of machinery. MiTAC AI deep learning analysis accurately extracts various key data from vibration characteristics, providing factory managers with precise and effective digital decision-making support. |
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IoT Data Integration and Analysis Integrates additional essential data based on specific equipment needs, beyond just vibration characteristics, for comprehensive AI analysis. This enhances the accuracy of equipment performance analysis, energy consumption data, and the reliability of equipment status assessments. |
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Energy Consumption Monitoring and Management Tracks actual energy consumption of equipment and uses comparative analysis of vibration frequencies and current characteristics to evaluate operational phases such as machine adjustments, mold calibration, material changes, and maintenance. This helps in further assessing personnel performance. By analyzing machine productivity, product quality rates, and equipment energy consumption, it helps determine optimal processing parameters to maximize machine efficiency. |
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ISO 50001 Metrics Data Applies AI deep learning to estimate energy baselines (EnB) and energy performance indicators (EnPI) based on industrial IoT data regarding production processes and equipment energy consumption. Provides effective data for energy-saving solutions and serves as a basis for ESG smart factory certification. |
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Proactive Management System Integrates the current operational status and historical data of multiple processing machines into a single system interface, allowing managers to quickly grasp factory operations and make informed decisions on processing tasks. |
Applications
Manufacturing industry, various metal processing plants, etc.
System Features
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Operation Rate Measurement System
Utilizing non-intrusive vibration sensing units to capture the vibration frequencies during machine operation, coupled with advanced GodTong AI deep learning analysis, this system accurately discerns key data related to factory operations, including equipment availability, production quantity, equipment production efficiency, product yield, and overall equipment efficiency (OEE). Subsequently, it predicts critical timelines such as order fulfillment and inventory reaching alert levels, providing factory managers with precise and effective digital decision-making foundations. -
Energy Consumption Monitoring System
Equipped with vibration sensing devices and digital meters, this system not only compiles the actual energy consumption during equipment operation but, through mutual comparison of vibration frequencies and electrical current characteristics, meticulously assesses the operational time points and durations of various procedures such as machine adjustments, calibrations, material changes, and maintenance. This provides a basis for further evaluating personnel performance. Leveraging data on machine capacity, product yield, and equipment energy consumption, it can further analyze the optimal processing parameters for machine operation, optimizing overall operational efficiency.
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