From the sensor to the cloud
Cloud-based systems make it easy to monitor machines located anywhere. Sensor and cloud are the key words in the IoT enviornment. With them, 24/7 monitoring makes it easy to learn operational profiles.
The cloud can support all industrial markets through sensor:
- Machine tools
- Assembly machines
- Handling systems
Retrofitting machines with sensors and a “gateway device” is the key to building a low-cost predictive maintenance system that is safe and simple. The gateway device aggregates data from a variety of sensors, handles local communication processing, and provides an Internet link to the cloud.
Connect your analog sensors to the Cloud
Recently, data has become one of the most valuable assets a manufacturer can have. Improved insights into factory workflows can offer new insights, streamline existing processes, and help companies develop better forecasting techniques.
As a result, many manufacturers are turning to the Internet of Things to gather more information on machine performance. However, the capital required for a new network-ready device may be too much for some businesses.
The average age of industrial equipment in Europe has steadily increased over the past few decades.
Retrofits provide a solution to this problem. They can also address the growing technology gap between IoT adopters and manufacturers who cannot afford new IoT-ready machinery. Therefore, through the IoT it is possible:
- Permanent monitoring promptly detects anomalies and wear of machine parts and can send alert messages with advance warnings.
- Permanent monitoring of structural noise, vibrations, load monitoring / torque and flux measurement.
- Expensive replacement parts only need to be ordered when parts in use show signs of wear and when failure is imminent.
First, let’s check the capabilities of your existing machines: do they produce signals? Can existing signals be used or do sensors need to be updated? Is there already an integrated system that could make a retrofit superfluous.
- Lower investment costs compared to the replacement of machines or entire plants
- Reduction of personnel training costs since the existing machines are already known
- Additional time savings through partial renewal instead of full replacement
- Boards and processors: Arduino, Raspberry Pi, NanoPi, ESP32/8266
- CPU Architectures: ARM, MIPS, x86
Hardware design, integration of peripherals and sensors, firmware engineering
- Wireless: WiFi, ZigBee, LoRa, BT5.0/BLE, NFC, RFID
- Protocols: STOMP, AMQP, MQTT, ZeroMQ, RabbitMQ, WS/WSs, push notifications, custom protocols design
- Industrial networking: Modbus, GOOSE, CIP, MRP, PROFIBUS, PROFINET, CAN, Industrial Ethernet, ARCNET, BACNet
- IoT gateways: connectivity software – development of drivers and firmware libraries, edge computing components: preprocessing and data normalization.
Embedded Linux experience: Yocto, Debian, Ubuntu (aarch64)
Vibrations in the cloud
In this concrete example we see how an inexpensive accelerometer can easily control movements and vibrations (for example by checking the health of machinery). The same movements can be viewed in real time (in this case in an open-source platform, Thingsboard) and possibly saved in the cloud.
With similar sensors it is possible to detect temperature, humidity, pressure, air quality, concentration of gas or substances, light intensity…
Using machine learning (TinyML) and the IoT, we want to ensure that shipments are handled with extreme care. In the video below, a sensor measures the vibrations inside a box usually used in shipments. In a second video we will see how it is possible to distinguish the vibrations that are harmful to the contents of the box from the vibrations that normally accompany a shipment.