Recently, I encountered an interesting challenge in a production line renovation project at a large pharmaceutical company: how to use real-time data collected from the Siemens S7-1500 PLC, combined with AI technology, to predict equipment failures. This project made me deeply appreciate the powerful potential of combining PLC and AI technologies. Today, let me take you deep into this field.
1. Hardware Configuration and Data Collection Basics
Hardware Selection
-
• S7-1500 CPU (recommended 1517-3 PN/DP or higher) -
• CP 1545-1 communication processor -
• Relevant I/O modules and communication cards
TIA Portal Configuration Steps
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1. Create an OPC UA server -
2. Configure data blocks (DB) for data storage -
3. Set sampling cycles and trigger conditions
// Data collection DB example
DATA_BLOCK "Process_Data"
{ S7_Optimized_Access := 'TRUE' }
VERSION : 0.1
NON_RETAIN
STRUCT
Temperature : Real; // Temperature value
Pressure : Real; // Pressure value
Speed : Real; // Speed
Vibration : Real; // Vibration value
TimeStamp : DTL; // Timestamp
END_STRUCT;
BEGIN
Temperature := 0.0;
Pressure := 0.0;
Speed := 0.0;
Vibration := 0.0;
END_DATA_BLOCK
2. Data Preprocessing and Transmission
OPC UA Server Configuration
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• Enable the built-in OPC UA server of the S7-1500 -
• Configure access rights and security settings -
• Set data subscription parameters
Python Data Collection Program
from opcua import Client
import pandas as pd
import time
def collect_data():
client = Client("opc.tcp://192.168.0.1:4840")
try:
client.connect()
node = client.get_node("ns=3;s=\"Process_Data\".Temperature")
value = node.get_value()
return value
finally:
client.disconnect()
3. AI Model Development and Application
Data Preprocessing
-
• Outlier handling -
• Data normalization -
• Feature engineering
Prediction Model Development
from sklearn.ensemble import RandomForestRegressor
import numpy as np
def train_model(X, y):
model = RandomForestRegressor(n_estimators=100)
model.fit(X, y)
return model
def predict_failure(model, current_data):
prediction = model.predict(current_data)
return prediction
4. Real-time Monitoring System Integration
PLC Program Implementation
// Alarm trigger program block
FUNCTION_BLOCK "Alarm_Trigger"
VAR_INPUT
AI_Prediction : Real; // AI prediction value
Warning_Threshold : Real; // Warning threshold
Alarm_Threshold : Real; // Alarm threshold
END_VAR
VAR_OUTPUT
Warning : Bool; // Warning signal
Alarm : Bool; // Alarm signal
END_VAR
BEGIN
// Warning logic
#Warning := #AI_Prediction >= #Warning_Threshold;
// Alarm logic
#Alarm := #AI_Prediction >= #Alarm_Threshold;
END_FUNCTION_BLOCK
5. Practical Case Sharing
In the application within the pharmaceutical company, we achieved the following goals:
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• 24-hour advance warning of equipment failures -
• Prediction accuracy reached 89% -
• Annual maintenance costs reduced by 35%
6. Future Outlook and Recommendations
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• It is recommended to start with small-scale pilot projects -
• Pay attention to data quality management -
• Continuously optimize AI models -
• Focus on industrial safety