Decoding Siemens PLC + AI Industrial IoT Solutions, Full Process Technology from Data Collection to Intelligent Analysis

Recently, I participated in a predictive maintenance project for a large steel plant. By combining Siemens S7-1500 PLC with AI technology, we successfully improved the equipment fault prediction accuracy to 93%, saving our clients approximately 2 million yuan in maintenance costs annually. Today, I will share the technical details of this solution.

1. Hardware Configuration and System Architecture

1.1 Hardware Selection

• S7-1516-3 PN/DP CPU (supports OPC UA)
• AI/AQ Module: SM 531/532
• Communication Processor: CP 1543-1
• SIMATIC IPC Edge Device

1.2 System Architecture Configuration

[Field Layer]
Sensors/Actuators ←→ S7-1500 PLC
         ↓
[Edge Layer]
SIMATIC IPC Edge
         ↓
[Cloud Layer]
MindSphere Cloud Platform

2. PLC Data Collection Configuration

2.1 Data Block (DB) Configuration

// Device Status Monitoring DB
DATA_BLOCK "Device_Status"
{ S7_Optimized_Access := 'TRUE' }
VERSION : 0.1
NON_RETAIN
   STRUCT 
      Temp_Value : Real;   // Temperature Value
      Vibr_Value : Real;   // Vibration Value
      Curr_Value : Real;   // Current Value
      Press_Value : Real;  // Pressure Value
   END_STRUCT;
BEGIN
   Temp_Value := 0.0;
   Vibr_Value := 0.0;
   Curr_Value := 0.0;
   Press_Value := 0.0;
END_DATA_BLOCK

2.2 Sampling Cycle Configuration

• Temperature Data: 10s/sample
• Vibration Data: 100ms/sample
• Current Data: 1s/sample
• Pressure Data: 5s/sample

3. OPC UA Server Configuration

3.1 PLC Side OPC UA Configuration

1. Enable OPC UA server functionality in TIA Portal
2. Configure security settings and certificate management
3. Set data access permissions

3.2 Data Node Configuration Example

// OPC UA Node Configuration
{
    "NodeId": "ns=3;s=\"Device_Status\".\"Temp_Value\"",
    "DisplayName": "Temperature",
    "DataType": "Float",
    "AccessLevel": "Read/Write"
}

4. Edge Computing Implementation

4.1 Data Preprocessing Code Example

import numpy as np
from sklearn.preprocessing import StandardScaler

def preprocess_data(raw_data):
    # Outlier handling
    data = remove_outliers(raw_data)
    
    # Standardization
    scaler = StandardScaler()
    normalized_data = scaler.fit_transform(data)
    
    return normalized_data

def remove_outliers(data, threshold=3):
    z_scores = np.abs((data - np.mean(data)) / np.std(data))
    return data[z_scores < threshold]

5. AI Model Deployment

5.1 Predictive Maintenance Model Architecture

class PredictiveMaintenance(nn.Module):
    def __init__(self):
        super(PredictiveMaintenance, self).__init__()
        self.lstm = nn.LSTM(input_size=4, 
                           hidden_size=64, 
                           num_layers=2, 
                           batch_first=True)
        self.fc = nn.Linear(64, 1)
        
    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        predictions = self.fc(lstm_out[:, -1, :])
        return predictions

6. Actual Application Effects and Experience Summary

6.1 System Performance Indicators

• Data Collection Delay: <100ms
• Model Prediction Time: <500ms
• Fault Warning Advance Time: 24-72 hours
• Prediction Accuracy: 93%

6.2 Experience Summary

1. Data collection frequency should be reasonably configured according to equipment characteristics
2. Edge-side data preprocessing is crucial for system real-time performance
3. AI models need to be retrained regularly to adapt to changes in equipment status
4. It is recommended to conduct a system performance evaluation every 3 months

7. Future Prospects

With the popularization of 5G technology and advancements in AI algorithms, the Siemens PLC + AI solution will play a greater role in industrial sites. Readers are encouraged to keep an eye on related technological developments and boldly try innovative solutions in actual projects.

Although this solution requires a large initial investment, its long-term benefits far exceed the costs. If you encounter problems during implementation, feel free to discuss in the comments section.