Sensor technology: Intelligent manufacturing diagnosis relies on various sensors to monitor key parameters in the manufacturing process. These sensors can measure temperature, humidity, pressure, vibration, current, voltage and other data, and transmit them to the data acquisition system for analysis.
Data collection and storage: The collected sensor data needs to be stored and analyzed in real time. Usually, a large amount of data is collected and stored in the cloud or local server for further processing.
Big data analysis: Use big data analysis technologies, such as machine learning and artificial intelligence, to process the collected data. These technologies can identify anomalies, predict equipment failures, optimize production processes, and provide real-time decision support.
Equipment health monitoring: By analyzing sensor data, intelligent manufacturing diagnosis system can monitor the health status of equipment, identify potential failure signs, and take maintenance measures in advance to avoid downtime and production losses.
Quality control: Intelligent manufacturing diagnosis can also be used to monitor product quality. By analyzing the data in the production process, we can find the product defects and take corrective measures to ensure that the products meet the quality standards.
Real-time feedback and decision support: Intelligent manufacturing diagnosis system can provide real-time feedback and decision support to help manufacturing enterprises make rapid adjustments to cope with changing production conditions or demands.
Visual interface: In order to make the production personnel understand and use the diagnosis results easily, an intuitive visual interface is usually designed to display the key data and results.
Generally speaking, intelligent manufacturing diagnosis is a method that integrates sensor technology, big data analysis and real-time feedback, which can help manufacturing enterprises improve production efficiency, reduce costs, improve quality and achieve a more sustainable manufacturing process.