Data analysis/abnormal detection of production equipment Field diagnostic device SignAIEdge
Statistical analysis of data from food manufacturing sites. Predictive maintenance and fault prediction are realized with an easily deployable on-site diagnostic device.
Are there any challenges regarding the productivity and maintenance of production and packaging machinery, as well as the utilization of manufacturing data in food manufacturing sites? The on-site diagnostic device SignAIEdge utilizes statistical analysis technology to solve these issues. It enables the detection of "something is different," similar to the intuition and experience of skilled workers (anomaly detection), and allows for improvements in "what is different" through analytics and AI (statistical analysis) (cause analysis). As a result, it achieves failure prediction and anomaly detection, addressing various challenges in food manufacturing sites.
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basic information
- Enables defect cause and failure analysis on the manufacturing floor The on-site diagnostic device can be implemented at a low cost without the need for infrastructure setup, allowing for immediate deployment in manufacturing environments. It easily collects machine equipment data, enabling defect cause analysis and failure analysis on the manufacturing floor. - Supports the alleviation of data analysis personnel shortages The on-site diagnostic device offers functionalities for data analysis and interpretation without requiring specialized knowledge in statistical analysis. It can automatically generate statistical analysis models, eliminating the need for the development of individual applications or software. - Achieves anomaly detection and failure precursor detection, reducing maintenance costs The on-site diagnostic device enables precursor monitoring and predictive maintenance of machinery, allowing for maintenance tailored to the situation. It utilizes data from PLCs and vibration sensors to realize preventive maintenance and failure detection.
Applications/Examples of results
In multi-variety switch production, there are limits to improvement activities and maintenance conducted by skilled workers. By utilizing IoT and analytics AI, it becomes possible to visualize the operating status of production equipment, prevent human errors, and digitalize the know-how of skilled work for technology transfer. As a result, improvements in quality, reduction of quality defects, and reduction of downtime and inspection work hours can be expected. [Application Example: Refrigerated Showcase System] Refrigerated showcases are used for selling fresh food products in convenience stores and supermarkets. When the system fails, it results in significant losses for both the store and consumers, making the early detection and response to failures a major challenge. The showcase undergoes defrosting at regular intervals, and data such as temperature changes periodically, so abnormal diagnosis was applied to the data of the refrigerated showcase system. Specifically, a diagnosis was conducted in response to high-temperature alarms caused by frost, allowing for the detection of abnormal signs approximately three weeks before the occurrence of abnormalities, which previously could only be discovered right before the alarm was triggered.
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Data analysis/abnormal detection of production equipment Field diagnostic device SignAIEdge
Awareness survey on IoT and data utilization in food factories.
Survey on Awareness of IoT and Data Utilization in Foundries
Survey on Awareness of DX in Factories and Plants
Survey on Awareness of DX in Food Factories
Survey on Awareness of DX in the Manufacturing Industry (Steel Industry and Non-Ferrous Metals)
Current Status and Challenges of DX (Digital Transformation) in Production Management in the Food Manufacturing Industry
Company information
Providing solutions for various plants and factories such as food, chemicals, oil and gas pipelines, paper and pulp, and cleaning facilities. Our measurement and control systems have over 40 years of history since the launch of the first distributed control system in 1975. Based on our track record, we propose systems and solutions to support the latest technologies, IoT utilization, and the development of smart factories. - Steam generation heat pumps / waste heat utilization / visualization of thermal energy - Clamp-on steam flow meters / flow meters / flow sensors / thermal balance analysis - Industrial furnaces / electric furnaces / high-frequency induction furnaces / IGBT power supplies - Gas analyzers / gas measurement devices / gas analysis sensors - Predictive maintenance / anomaly detection maintenance / preventive maintenance / maintenance and prevention systems / smart security - Smart factory - MES / DCS / SCADA / edge controllers - EMS / energy management systems / visualization of energy - Smart glasses / remote work support systems - Multivariate analysis / MSPC - Engineering tools