Comprehensive Guide to Data Analysis in Siemens PLC + AI IoT Technology

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

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

• 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 &gt;= #Warning_Threshold;
    
    // Alarm logic
    #Alarm := #AI_Prediction &gt;= #Alarm_Threshold;
END_FUNCTION_BLOCK

5. Practical Case Sharing

In the application within the pharmaceutical company, we achieved the following goals:

• 24-hour advance warning of equipment failures
• Prediction accuracy reached 89%
• Annual maintenance costs reduced by 35%

6. Future Outlook and Recommendations

• It is recommended to start with small-scale pilot projects
• Pay attention to data quality management
• Continuously optimize AI models
• Focus on industrial safety