IIoT Solutions 

Predictively maintain equipment to extend service life and avoid unexpected downtime

Data is essential for training AI and the cornerstone of transitioning to smart manufacturing；GIT's machine monitoring system is a solution for collecting and analyzing production line equipment data, enabling monitoring of machine efficiency, energy consumption, predictive maintenance scheduling, and data storage for use in heterogeneous systems. 

IIoT Solutions

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Problems and Challenges of IIoT Machine Monitoring System Production Line Equipment

Failure to detect equipment abnormalities early leads to the risk of unexpected downtime


Failure to detect equipment abnormalities early leads to the risk of unexpected downtime

維護仰賴人工經驗 品質不一也有工安隱憂


Maintenance relies on manual experience
Uneven quality also raises concerns about worker safety

缺少機台生產履歷 錯失供應商評比的優勢


Lack of machine production experience
Missing out on the benefits of supplier comparisons 

How GIT Solves the Problems

Quickly analyze monitoring data and propose the best maintenance optimization plan

GIT also helps manufacturers understand the machines, quickly analyze monitoring data, and propose the best maintenance optimization plan.

Predictive maintenance to extend equipment life

The machine is like having an extra butler, actively notifying abnormalities and regularly recommending maintenance plans. Managers can remotely understand the status at any time, extend the service life of the equipment, and maximize the output value.

Data records can serve as the cornerstone of production history

Collecting the history of production status information makes the process control more rigorous, which not only increases customer confidence, but also advances the road to carbon reduction under ESG regulations.

Application

Panel factory exposure machine motor monitoring

Panel factory exposure machine motor monitoring

Provides interactive monitoring data display and visual trend charts for expensive equipment, including vibration time series and frequency domain graphs. 
 
In panel manufacturing, exposure machines are expensive pieces of equipment. A customer experienced a drop in exposure machine yield. After investigation, it was determined that the problem was with motor vibration. Although the replacement time had not yet arrived, the yield was still affected.

To prevent the problem from recurring, GIT installed monitoring equipment in the exposure machine. This equipment records various data, including the motor drive current and the torque sensor within the driver. When the yield began to decline, the data was compared and confirmed to be related to changes in motor-related data.
 
However, detecting anomalies still relies on engineers using methods such as amplitude and Fourier transform to analyze frequency. GIT not only assists with data collection but also uses machine learning to address these issues, reducing the burden on personnel and enabling AI to predict equipment maintenance time, thereby maintaining reliable production yields. 

Automated Machine Monitoring + Health Diagnosis

Automated Machine Monitoring + Health Diagnosis

Real-time information reporting + predictive diagnosis = efficient production
The client, a global electronics design and manufacturing company, needed to monitor the real-time production status and abnormality reports of nearly 100 machines at its China plant.

GIT developed a cloud-based operational intelligence platform based on their needs. The system graphically displays the entire machine status and production capacity overview, allowing for advanced review of individual machine basic information (location, products produced), production statistics, and operating status (normal/abnormal).

In addition to real-time information reporting, predictive equipment monitoring is crucial for maintaining high-quality production lines. GIT installs sensors on machines and collects production logs. This aggregates data from key components such as arms, solenoid valves, nozzles, vacuum generators, and motors into the system for remaining life analysis, predicting appropriate maintenance schedules and avoiding unanticipated downtime.

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