- Prediction and preventive maintenance of equipment failure
based on Big data - Improving the quality of final products and predicting
the results of progress defects and good product data - Prevent accidents by recognizing appropriate parts replacement times
and parts life cycle in advance - Utilization and analysis of real-time or historical data
- Intuitive data monitoring via charts and lists
BIGWATCHER
Prognostic maintenance and product quality data result
prediction solution through deep learning
Prognostic maintenance and product quality data result
prediction solution through deep learning
BIGWATCHER enables users to easily train data using algorithms set by the user based on big data collected from various equipment installed on-site. With the learned data, users are notified in advance about the replacement timing for equipment parts and equipment, inspection timing, and more, facilitating predictive monitoring, predictive maintenance, and preventive maintenance. It can apply real-time big data collected through X-DAS's OPC-UA communication as well as historical big data stored in various databases for analysis and operation. By analyzing data by process, it systematically manages factors influencing the quality of final products, enabling the prediction of good product outcome data.
Understanding of BIGWATCHER Structure
Key Features of BIGWATCHER
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Modeling through Drag & Drop |
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User configuration via No Coding |
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Equipment failure prediction based on data |
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Cost reduction and productivity improvement |
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Provision of predictive analysis and visualization data |
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Reduction in production downtime |
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Scalable high-performance platform |
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Fast decision-making and management support |
BIGWATCHER is a solution optimized for predictive maintenance and
BIGWATCHER is a solution optimized for predictive maintenance and product quality data management optimized for Industrial A.I.
product quality data management optimized for Industrial A.I.
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