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Equipment maintenance in the IoT era

Equipment maintenance in the IoT era

With the requirements to continually grow, it is important that the production line remains constantly at the optimum level of operation. Using new technologies and equipment maintenance practices, this is now affordable, easy and above all effective.

Analysis of the value of advanced maintenance techniques

Imagine that you installed a new packaging machine on your production line to pack large items, three months ago. For the packaging requirements of your production line, switch between the new machine and the existing  old one. So during these  three months you’ve packed 5,000 items in the new stack. However, the manufacturer proposes to change the bearings every three months or every 15,000 packages. Three months after the purchase of the machine, you have to make a precise (thorough) change of the bearings in order to comply with the manufacturer’s instructions. This is an example of preventive maintenance.

Diagnostic and monitoring system for electromechanical installations using multiple sensors.

Analysis of the value of new maintenance techniques

Now, imagine that the machine you have  installed has been inserted in your maintenance program. With a combination of sensors, you are constantly checking the state of your asset. Having used  the packaging machine for 6 months and having  packed 19,000 items, you receive a notice that you still have 1,000 packages until the next  change of bearings is due. This is an example of predictive maintenance. Prevents damage and alerts you for maintenance work that is adapted to your own use for the equipment in advance before there is a risk of damage.

Preventive Maintenance VS Predictive Maintenance

Industrial sectors are already in accordance  with the concept of preventive maintenance, but predictive maintenance has its own advantages which can enhance  production.

Preventive maintenance work is carried out on the basis of time, events (facts-incidents) or even indications. The age of the equipment as well as the manufacturer’s recommendations are taken into account. In fact, preventive maintenance is a scheduled maintenance. However, as  the example of  the packing machine, this time-based maintenance approach probably it does not represent the actual condition of the equipment and it may  lead to maintenance regardless if this maintenance is necessary according to the condition of the equipment and its components

On the other hand, predictive maintenance is based on actual equipment status instead of time factors. Using a combination of sensors, merging measurements and extracting features is performed. Classification is performed based on algorithmic  predictions  / or experts’ opinion damages can be predicted  before being incurred, so ample time is  given to the company to plan the maintenance. This allows field engineers to fix the damage before it ever occurs.

As is evident in predictive maintenance, advanced techniques and sensors are used, such as:

  • Oscillation sensors (Accelerometers) measure the motion of the camera and detect mechanical errors that are evolving.
  • Current analyzers monitor the status of the electrical components of the system.
  • Temperature sensors.
  • Thermal remote images with mobile devices are used to access and store the temperature and infrared image of the production equipment.
  • Ultrasound sensors are used to detect leaks- inspect mechanical and electrical components.

Extension of Applications

What is the reason for using the preventive meeting since it has such positive results?

However, predictive maintenance offers significant cost savings by reducing downtime and lowering the cost of other parts and spare parts.  Investing in a predictive maintenance system accounts for up to 10 times its cost to the oil and gas industry, according to a Roland Berge study.

One can easily and quickly measure points of interest even with multiple different sensors at the same time. In addition, one can  access the state of the plant from anywhere and make decisions about their maintenance and operation. With the fixed systems and the  Internet of Things (IOT), it is possible   to repeat accurate and complete measurements in inaccessible or remote locations at any time without the need for staff relocation.

When the following are implemented on a software platform that is flexible and safe, the results are unsurpassed to the usual maintenance and tracking practices that  exist to date. It is obvious that predictive maintenance is an attractive worthy  investment that brings excellent  results.

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