From the sensor to the cloud

The journey from the sensor to the cloud involves a series of steps that enable data generated by sensors to be processed, analyzed, and stored in the cloud for further use.

Sensor data collection: The first step is to collect data from sensors. Sensors can be embedded in various devices and objects, such as smart homes, wearables, industrial machines, and vehicles, and can generate a wide range of data, such as temperature, humidity, pressure, and motion.
Data processing: The collected data is then processed and filtered to remove noise, irrelevant data, and duplicates. Data processing can be done at different levels, such as edge processing, which involves processing data on the device or gateway, and cloud processing, which involves processing data in the cloud.
Data storage: The processed data is then stored in a database or data warehouse. Cloud storage can be used to store large volumes of data, enabling easy access, scalability, and security.
Data analysis: The stored data can be analyzed using various techniques, such as statistical analysis, machine learning, and predictive modeling. Data analysis can provide valuable insights and enable decision-making.
Data visualization: The analyzed data can then be presented in a user-friendly format, such as graphs, charts, and dashboards. Data visualization can help users understand the data and make informed decisions.
Data sharing: The final step is to share the data with relevant stakeholders, such as customers, partners, and employees. Data sharing can be done through various channels, such as APIs, mobile apps, and web portals.

Overall, the journey from the sensor to the cloud involves a series of steps that enable data generated by sensors to be processed, analyzed, and stored in the cloud for further use. This journey enables businesses to leverage sensor data to gain insights, optimize operations, and improve customer experiences.

Cloud-based systems

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
Pick-and-place

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.

Cost reduction

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

Hardware

Boards and processors: Arduino, Raspberry Pi, NanoPi, ESP32/8266
CPU Architectures: ARM, MIPS, x86
Hardware design, integration of peripherals and sensors, firmware engineering

Software

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…