Deep Integration of Siemens PLC and Industrial IoT

Click the blue text to follow us!

[Technical Innovation] Deep Integration of Siemens PLC and Industrial IoT: Data Collection, Cloud Storage, and Remote Monitoring Integrated Solutions

Hello everyone, today we are going to talk about a hot topic: how Siemens PLC deeply integrates with Industrial IoT. This is not just a vague concept, but a practical solution that can enhance production efficiency and reduce costs. We will explore how to create an integrated solution focusing on three core areas: data collection, cloud storage, and remote monitoring.

1

1. Basic Concepts Explained

Let’s clarify a few key concepts:

• Siemens PLC: Like the brain of the factory, responsible for controlling the operation of various devices.

• Industrial IoT (IIoT): Simply put, it means enabling devices in the factory to communicate over the internet.

• Data Collection: This means having the PLC act like a reporter, recording everything that happens on the production line.

• Cloud Storage: Storing collected data in the “cloud”, similar to saving photos in an online drive.

• Remote Monitoring: Knowing what happens in the factory while lying at home, and being able to adjust devices remotely.

Note: Don’t oversimplify Industrial IoT; it’s not just about connecting devices to the internet. Security, real-time performance, and reliability are critical considerations.

2

2. Hardware Architecture

Let’s take a look at what the “skeleton” of this system looks like:

[Sensor/Actuator] <-> [Siemens PLC] <-> [Edge Computing Device] <-> [Internet] <-> [Cloud Server] <-> [User Terminal]

• Sensor/Actuator: The “eyes” and “hands” of the factory.

• Siemens PLC: Responsible for basic control logic and data collection.

• Edge Computing Device: This can be an industrial PC or a smart gateway, responsible for data preprocessing and communication.

• Cloud Server: The place for storing and analyzing data.

• User Terminal: This can be a computer, mobile phone, or tablet used to view data and operate remotely.

Hardware Selection Recommendations:

• PLC: Consider choosing the Siemens S7-1200 or S7-1500 series, with specific models based on I/O point count and performance requirements.

• Edge Device: Consider Siemens’ SIMATIC IPC or open-source hardware like Raspberry Pi.

• Network Devices: Industrial-grade routers and switches, such as Siemens’ SCALANCE series.

3

3. Software Architecture

In terms of software, we need to consider the following aspects:

1. PLC Program: Written using TIA Portal, responsible for basic control logic and data collection.

2. Edge Computing Program: Can be written in Python or Node.js, responsible for data preprocessing and communication.

3. Cloud Application: Can use AWS IoT, Azure IoT, or Alibaba Cloud IoT platform.

4. Visualization Interface: Can develop web applications using Vue.js or React, or develop native mobile apps.

Key Points: Standardization and security of data flow are crucial. It is recommended to use MQTT or OPC UA protocols for communication and implement end-to-end encryption solutions.

4

4. Code Example

Now let’s look at a simple data collection program on the PLC side:

// Siemens S7-1200 SCL Program Example
// Collect temperature and pressure data once per second and store it in the data block
DATA_BLOCK "ProcessData"
BEGIN
   temp :REAL;
   pressure :REAL;
END_DATA_BLOCK
ORGANIZATION_BLOCK "CyclicExecution"
BEGIN
   // Use built-in function to read analog input
   "ProcessData".temp := SCALE_X(AI_1, 0.0, 100.0);
   "ProcessData".pressure := SCALE_X(AI_2, 0.0, 10.0);
   // Send data to edge device (assuming using Modbus TCP)
   #SEND_DATA_TO_EDGE("ProcessData".temp, "ProcessData".pressure);
END_ORGANIZATION_BLOCK
FUNCTION "SEND_DATA_TO_EDGE" :VOID
VAR_INPUT
   temp :REAL;
   pressure :REAL;
END_VAR
BEGIN
   // Logic for sending data using Modbus TCP
   // Specific implementation omitted
END_FUNCTION

This code executes once per scan cycle, collecting temperature and pressure data, and then sending it to the edge device via Modbus TCP.

Note: In actual projects, consider data filtering, calibration, and anomaly handling. Don’t forget to comment on key variables to avoid confusion later.

5

5. Real Application Case

Let me share a project I did at a chemical plant a while ago. This factory has more than a dozen reaction kettles, each requiring strict control of temperature and pressure. Previously, it relied on manual inspections, which were inefficient and prone to errors.

Our solution was:

1. Equip each reaction kettle with an S7-1200 PLC for basic control and data collection.

2. Use an edge server (industrial PC) to aggregate data from all PLCs and perform preliminary analysis.

3. Upload processed data to the Alibaba Cloud IoT platform.

4. Develop a web application and mobile app to allow management to view production status anytime, anywhere.

Results: After implementation, the factory’s product quality stability improved by 15%, energy consumption decreased by 8%, and no more midnight calls to handle emergencies (unless it’s extremely urgent).

6

6. Common Problems and Solutions

1. Data Latency Issues

* Cause: Network congestion or slow cloud service response
* Solution: Optimize network structure, use edge computing for data preprocessing, reduce cloud pressure

1. Security Risks

* Cause: Using insecure communication protocols or failing to update system patches in time
* Solution: Use encrypted communication throughout, regularly update the system, and implement strict access control

1. Poor System Scalability

* Cause: Insufficient initial planning, using a closed system
* Solution: Adopt modular design, use open standard protocols and interfaces

1. Data Inconsistency

* Cause: Unsynchronized clocks between multiple systems
* Solution: Implement NTP time synchronization, consider using time series databases

Lessons Learned: Don’t think about connecting all devices to the IoT at once. Start with a key production line as a pilot, and gradually expand after gaining experience. I learned this the hard way; I rushed in and ended up causing a half-day shutdown of the entire factory, which made the boss very upset.

7

Conclusion

The combination of Siemens PLC and Industrial IoT can indeed bring tangible benefits to factories. However, this is not something that can be achieved overnight; it requires thorough planning and gradual implementation. During the process, special attention must be paid to data security and system reliability.

Here’s a practical suggestion: you can start by purchasing an S7-1200 PLC and a Raspberry Pi to set up a small industrial IoT system at home. Collect temperature, humidity, and appliance switch states, and develop a mobile app for remote control. This way, you can learn while improving your quality of life, which is great!

Remember, what you learn from books is only superficial; true understanding comes from hands-on practice. Only by getting your hands dirty can you truly master these technologies. Keep it up, everyone!

Previous Reviews

01

02