In the era of Industry 4.0, predictive maintenance of equipment has become an important development direction for the manufacturing industry. I remember when I implemented an equipment management project at a large steel plant last year, the client proposed a seemingly impossible requirement: to use the existing S7-1500 PLC to implement a simple AI algorithm for real-time monitoring and early warning of the health status of the rolling mill bearings. This project allowed me to deeply explore the SCL programming capabilities of the S7-1500, and today I will share this interesting technical implementation solution.
1. System Configuration and Preparation
Hardware Requirements
-
• CPU: S7-1516F-3 PN/DP (recommended firmware version V2.8 or higher) -
• AI Module: SM 531 (16-bit high-precision analog input) -
• Memory Card: At least 32MB
Software Configuration
// Define data structure in DB
DATA_BLOCK "VibrationDB"
{ S7_Optimized_Access := 'TRUE' }
VERSION : 0.1
NON_RETAIN
STRUCT
RawData : ARRAY[0..999] of REAL; // Raw vibration data
FFTResult : ARRAY[0..499] of REAL; // FFT results
HealthScore : REAL; // Health score
AlarmThreshold : REAL := 85.0; // Alarm threshold
END_STRUCT;
END_DATA_BLOCK2. Core Algorithm Implementation
Data Preprocessing
FUNCTION "ProcessVibrationData" : VOID
VAR_INPUT
RawInput : REAL;
END_VAR
VAR_TEMP
i : INT;
END_VAR
BEGIN
// Data shifting
FOR i := 998 TO 0 BY -1 DO
"VibrationDB".RawData[i+1] := "VibrationDB".RawData[i];
END_FOR;
// Insert new data
"VibrationDB".RawData[0] := RawInput;
END_FUNCTION3. Implementation of AI Model
To implement a lightweight machine learning algorithm in the S7-1500, we adopted a simplified anomaly detection model:
FUNCTION "CalculateHealthScore" : VOID
VAR_TEMP
meanValue : REAL;
stdDev : REAL;
i : INT;
sumSquares : REAL;
END_VAR
BEGIN
// Calculate mean
meanValue := 0.0;
FOR i := 0 TO 999 DO
meanValue := meanValue + "VibrationDB".RawData[i];
END_FOR;
meanValue := meanValue / 1000.0;
// Calculate standard deviation
sumSquares := 0.0;
FOR i := 0 TO 999 DO
sumSquares := sumSquares + POWER("VibrationDB".RawData[i] - meanValue, 2);
END_FOR;
stdDev := SQRT(sumSquares / 999.0);
// Calculate health score
"VibrationDB".HealthScore := 100.0 - (stdDev * 10.0);
END_FUNCTION4. Practical Application Tips
Performance Optimization
-
1. Use a circular buffer to manage data, avoiding frequent array shifting operations -
2. Execute compute-intensive tasks in lower priority program cycles -
3. Adjust the sampling rate appropriately to find a balance between accuracy and performance
Alarm Management
FUNCTION "CheckAlarms" : VOID
VAR_INPUT
HealthScore : REAL;
Threshold : REAL;
END_VAR
VAR_OUTPUT
AlarmStatus : BOOL;
END_VAR
BEGIN
IF HealthScore < Threshold THEN
AlarmStatus := TRUE;
// Trigger alarm handling logic
ELSE
AlarmStatus := FALSE;
END_IF;
END_FUNCTION5. Real Application Cases
In the project at the steel plant, we applied this system to monitor the main bearings of the rolling mill:
-
1. Acquisition frequency: 100Hz -
2. Feature extraction: Calculate health score every 10 minutes -
3. Warning threshold: 85 points (can be adjusted based on actual operating data)
Three months after the system was put into use, it successfully predicted and prevented two bearing failures, saving the client about 500,000 yuan in maintenance costs.
6. Future Outlook
With the continuous improvement of the computing capabilities of the S7-1500 series PLCs, we can expect:
-
1. Implementation of more complex machine learning algorithms -
2. Deep integration with cloud platforms for distributed AI computing -
3. Support for more predictive maintenance scenarios
Finally, a reminder: when implementing similar projects, it is recommended to conduct sufficient data collection and validation to ensure the accuracy of the AI model. Also, do not forget to set reasonable backup plans to ensure system reliability.