Industrial IoT Components Explained (IIoT Components)

How are businesses benefiting from the IIoT today? Here are some examples of current and upcoming IIoT technologies and concepts:

• Digital Twins 

The practice of creating the computer model of an object such as a machine or human organ or a process such as meteorology. Studying the twin's behavior makes it possible to understand and predict their counterpart's behavior in the real world and deal with problems before they occur.

• Electronic Logging Device (ELD) 

Built-in sensors monitor driver speed, driving time, and brake use frequency, help save fuel, improve driver safety and reduce idle resources. If the driver performs a dangerous maneuver or is behind the wheel for too long, he is alerted, and a notification is sent to the sender. This technology can replace the logs that drivers usually had to fill out daily.

Smart Edge is the location where data is generated, analyzed, interpreted, and processed. With the intelligent edge, analysis can be performed faster, and the likelihood of data interception or breach is dramatically reduced.

• Predictive Maintenance 

A system involving a machine or component with sensors that collect and transmit data, analyze it and store it in a database. 

This database then provides benchmarks for events as they occur. As a result, the system eliminates unnecessary maintenance and increases the likelihood of avoiding failure.

• Radio Frequency Identification (RFID) 

It is a system that uses beacons and readers as a more ingenious barcode solution. Readers identify RFID tags using radio waves, which means multiple readers can read the tags in one go and over a longer distance than conventional UPCs allow. As a result, RFID tags make tracking and monitoring their attached.

Before predictive maintenance became a crucial part of Industry 4.0, other types of care prevailed. The first is corrective maintenance, also called corrective or reactive maintenance, which consists of intervening on a machine once a failure occurs. 

Then was adopted preventive care, the objective of which was to change parts before a loss could occur. 

With predictive maintenance, failure is anticipated as soon as warning signs are felt on the machine, making it possible to change parts at the right time and reduce costs by changing positions unnecessarily.

Preventive maintenance is based on estimating the life cycle of the equipment. Actions are carried out there regularly to ensure its long-term maintenance. This type of maintenance is based on the maximum "prevention is better than cure." 

Predictive maintenance uses IoT sensors to analyze the machine, monitor its condition in real-time, and carry out timely repairs.

Predictive maintenance is a source of ROI for companies. According to the company Praxedo, which develops intervention management software for technicians, the savings generated stem from several consequences:

• Reduced breakdowns and, therefore, equipment downtime

• The extension of their lifespan

• Improving the reliability of equipment and therefore optimizing production

• Better management of spare parts stocks

• Reduced maintenance costs.

As per the study published in 2017 by the Vanson-Bourne firm, each unplanned hour of downtime costs an average of $250,000, hence the importance for manufacturers to avoid breakdowns.

Vibration is one of the main parameters for detecting failure or malfunction. Most companies have machines designed to operate without vibration in the industrial sector. Therefore, vibration monitoring makes it possible to quickly identify a machine malfunction and abnormal vibrations on the device announcing a future failure.

Other parameters can also be tracked, such as temperature, machine pressure, or sound. By opting for predictive maintenance, the company can detect a fault in its machine and intervene before the failure occurs.