Make industrial maintenance predictable

In the era of Industry 4.0, we seek to empower our customers with a tool that allows them to optimize the maintenance process of their assets. Our priority is to increase productivity by reducing downtime with predictive maintenance.

Sensor Edge installed in a motor

Smart sensors

The sensor provides information on the status of the equipment. There are two types of sensors with different applications.

Edge

NX

Edge sensor
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Sensor Edge

Made for industry
Made with high-quality technical plastics and an aluminium base, which makes the sensor resistant to the harshest conditions.

Communication
IO-Link - This solution allows integration with any industrial system that uses the IO-Link communication protocol, so no additional device is needed.

Multi Sensor
Consists of sensors that measure vibration, magnetic field, acoustic emission and temperature, whose data correlation allows for better accuracy in detecting problems in equipment operation.

On-Device Machine Learning
Our sensor is equipped with state-of-the-art anomaly detection algorithms directly on the device. This means it can identify changes in the equipment's operating pattern in real time, preventing potential problems and increasing the equipment's lifespan.

Time and frequency domain analysis
The sensor performs signal processing in the time and frequency domain in real time. This data is readily available via the IO-Link protocol, providing important information on the status of the equipment. It also allows limits to be set in accordance with the ISO 20816-3 standard, ensuring that the machine operates within acceptable vibration levels.

Sensor NX
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Sensor NX

Made for industry
Made with high-quality technical plastics and an aluminium base, which makes the sensor resistant to the harshest conditions.

Communication
WiFi and Bluetooth.

Multi Sensor
Consists of sensors that measure vibration, magnetic field, acoustic emission and temperature, the correlation of which allows for better accuracy in detecting problems in the operation of equipment.

Battery
The system is powered by a lithium battery, which allows it to last for more than 2 years (depending on the data acquisition interval and environmental conditions).

Operation detection
Data acquisition is carried out when the equipment is running, ensuring that each sample is only taken after the equipment is in operation.

An intuitive platform

An easy-to-access and easy-to-use platform that allows you to check the status of equipment in real time, making maintenance easier.

NodeHub dashboard

Warns you when a problem occurs

The predictive maintenance platform offers an effective solution for identifying equipment faults quickly and efficiently. With a simple interface, it makes it easy to check for faults, enabling the maintenance team to take immediate action. In addition, access to the platform is quick and convenient and can be done from any device with a web browser.

An important feature of the platform is the ability to schedule periodic reports, allowing parameters to be configured according to the company's specific needs. This ensures that relevant information is delivered on a consistent and scheduled basis, facilitating decision-making and maintenance planning.

In addition, the platform sends notifications by different means, such as SMS, email, Slack or Telegram. This allows for effective and instant communication, ensuring that the maintenance team is quickly informed of any anomalies or problems detected in the equipment. 

OPC icon

OPC UA -This is an industrial communication protocol that can be used from machine to machine (M2M) regardless of the manufacturer or platform on which it is used.

mqtt icon

MQTT - This is an efficient communication protocol in environments with low bandwidth and high latency, widely used in the Internet of Things (IoT).

Rest Api icon

Rest API - Allows communication to be established between the platform and an external service using the HTTP protocol.

Easy integration

The technology developed by NodeHub facilitates integration with SCADA (Supervisory Control and Data Acquisition) systems and MES (Manufacturing Execution System) industrial management software, thus allowing customers to adopt our solution more easily.

Machine Learning

Machine Learning is a branch of artificial intelligence (AI) that allows systems to learn and improve from experience, without being explicitly programmed. Our system uses two types of Machine Learning algorithms - Supervised learning and Unsupervised learning.

With Supervised Learning, our system is trained to classify possible problems using a diverse set of sensor data. This means that it learns from identified data so that it can recognise the problem before it occurs.

We also use Unsupervised Learning to detect anomalies. This approach allows the sensor to learn during normal operations without the need for identified data.

Industries

These are some examples of industries that can greatly benefit from the installation of these sensors.

These are some examples of industries that can greatly benefit from the installation of these sensors.

Biomass Production

In biomass production, pressing equipment, conveyors and crushers play a crucial role. By predicting when maintenance is required, the downtime of this equipment can be minimised, ensuring consistent production and reducing overall operating costs. Predictive maintenance uses sensors and data analysis to monitor the condition of equipment in real time, helping to identify potential problems before they lead to costly breakdowns.

Paper Industry

Predictive maintenance is a major advance for the paper industry. The precision and efficiency of papermaking equipment is extremely important. By using sensors to monitor vibration and temperature, anomalies in machine performance can be detected, enabling swift and timely intervention, avoiding costly downtime and guaranteeing high-quality paper production.

Food and Beverage Industry

The food and beverage industry relies heavily on processing and packaging equipment. Predictive maintenance can be useful in this sector by monitoring important parameters such as motor performance, conveyor belt wear and so on. By doing so, possible problems can be dealt with before they worsen, ensuring food safety, regulatory compliance and minimising the risk of production delays.

Pellet mill

Biomass Production

In biomass production, pressing equipment, conveyors and crushers play a crucial role. By predicting when maintenance is required, the downtime of this equipment can be minimised, ensuring consistent production and reducing overall operating costs. Predictive maintenance uses sensors and data analysis to monitor the condition of equipment in real time, helping to identify potential problems before they lead to costly breakdowns.

Paper machine

Paper Industry

Predictive maintenance is a major advance for the paper industry. The precision and efficiency of papermaking equipment is extremely important. By using sensors to monitor vibration and temperature, anomalies in machine performance can be detected, enabling swift and timely intervention, avoiding costly downtime and guaranteeing high-quality paper production.

food industry manufacture

Food and Beverage Industry

The food and beverage industry relies heavily on processing and packaging equipment. Predictive maintenance can be useful in this sector by monitoring important parameters such as motor performance, conveyor belt wear and so on. By doing so, possible problems can be dealt with before they worsen, ensuring food safety, regulatory compliance and minimising the risk of production delays.

What kind of equipment can be monitored?

NodeHub's predictive maintenance systems are designed to monitor and predict faults in rotating equipment, including motors, fans, compressors, pumps, belt drives and gearboxes, among others.

Reduction of unexpected downtime

Maintenance based on equipment condition

Cost reduction

Increased productivity

For more information on predictive maintenance

You can make an appointment, without any obligation, where we will explain our process for implementing predictive maintenance systems in a little more detail. We will then understand whether we are the right partner for your project.