In the wave of Industry 4.0, the intelligence of equipment has become a key factor in the upgrade of the manufacturing industry. Today, let’s discuss the application of ABB PLC in the field of edge computing and see how to enable production lines to have the ability to “think autonomously”.
What is Edge Computing?
Think of edge computing as a “small brain” on the production site. The traditional method involves sending all data to a remote server for processing, similar to how all decisions must be approved by headquarters, which inevitably affects efficiency. Edge computing allows on-site devices to make preliminary judgments themselves, just like a production line team leader can handle daily issues independently.
Advantages of ABB PLC in Edge Computing
1. Powerful Computing Capability
The latest ABB PM595 series CPU has a clock frequency of 1.3GHz, equivalent to industrial computers from ten years ago, fully capable of real-time data analysis.
2. Open Architecture
It supports C language programming and can directly call machine learning algorithm libraries, no longer limited to traditional PLC instructions.
3. Local Storage
With 8GB of built-in storage, it can save historical data and decision models without the need for constant internet connectivity.
Practical Application Case: Intelligent Warning for Injection Molding Machines
Taking the example of abnormal temperature warnings for injection molding machines, let’s discuss how edge computing can enhance equipment management levels.
Problems with Traditional Solutions
- Single threshold alarms with high false positive rates
- Only addressing issues after faults occur
- Reliance on manual experience for judgment
Edge Computing Solution
awk copy
// Temperature trend analysis program snippet
VAR
TempArray : ARRAY[1..60] OF REAL; // Store 1 minute of temperature data
TrendValue : REAL; // Trend value
END_VAR
// Execute once per second
TempArray[60] := ActualTemp; // Record current temperature
FOR i:=1 TO 59 DO
TempArray[i] := TempArray[i+1]; // Shift data forward
END_FOR
// Calculate temperature change trend
TrendValue := LinearRegression(TempArray);
// Intelligent warning logic
IF (TrendValue > MaxTrend) THEN
PreWarning := TRUE; // Early warning
END_IF
Effectiveness Improvement
- Faults warned 15-20 minutes in advance
- False positive rate reduced by 80%
- Equipment failure rate reduced by 35%
Implementation Key Points
1. Data Collection Optimization
- Sampling period recommended ≤100ms
- Dual collection of key parameters
- Set reasonable data filtering
2. Algorithm Selection
- Initially use simple statistics and regression
- Introduce machine learning models as maturity increases
- Reserve interfaces for algorithm upgrades
3. Communication Configuration
- Reserve sufficient bandwidth
- Set up data caching mechanisms
- Ensure network security
Frequently Asked Questions
Q: Will edge computing affect the PLC main program?
A: As long as task priorities are set reasonably, the impact can be negligible. Set the core control program priority to 0 and the analysis tasks to 6-8.
Q: What if the network goes down?
A: The greatest advantage of edge computing is local decision-making; network disconnection only affects data upload, not on-site control.
Safety Reminders
- Always ensure data backup
- Reserve emergency shutdown mechanisms
- Regularly verify algorithm accuracy
- Important decisions must be confirmed by personnel
Practical advice: Start with a pilot on one production line, beginning with the most problematic equipment, and accumulate experience before expanding. The key is to ensure accurate data collection and clear decision logic; gradual optimization is more practical than trying to achieve everything at once.