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Predictive Maintenance in IoT and Big Data Era

Thanks to the developing industrial IoT and artificial intelligence technologies, it has become much easier than before to monitor the conditions of machinery and equipment in real time and to detect when something starts to go wrong. Although predictive maintenance has been used at different maturity levels in many facilities from past to present, its access has become easier and the efficiency of prediction has increased significantly with new technologies. Thanks to the wireless sensor capabilities that come with IoT technologies, installation costs and periods have been significantly reduced and the data that can be gathered has gained to a great variety. With the help of the big data infrastructures brought by IoT technologies, it became possible to analyze large data sets from dozens of sensors installed on machines. Another important issue is the advance of artificial intelligence technologies that enable machines to learn on their own and make necessary implications for the rules. Studies on machine learning and deep learning have enabled the development of many models that can effectively solve different types of problems. With the development of cloud and edge-based computers with high processing power that can run these algorithms simultaneously, there is no longer a reason to not to invest in predictive maintenance.

We can examine the global situation, after examining why today is the right time for investing in predictive maintenance. In the "Predictive Maintenance 4.0" report published by PwC in 2017, the 4 levels of predictive maintenance are listed as follows:

Level 1: Visual Control, periodic physical checks are made by the maintenance specialist, decisions are taken with the experience and observations of the maintenance specialist.

Level 2: Instrumental Control, assumptions are made by combining the experience of the maintenance specialist and the data obtained from the sensors during periodic controls.

Level 3: Real-Time Equipment Condition Monitoring, real-time sensor data is collected from machinery and equipment, decisions are made according to pre-entered rules and threshold values.

Level 4: Predictive Maintenance with Big Data Analytics, a decision is made based on sensor data collected from machines and equipment supported by machine learning and predictive analytics techniques.

According to these technology levels, the distribution of 280 manufacturing facilities located in Europe is as follows. Considering that the predictive maintenance market has grown by 25% annually, it will be understood that manufacturing facilities in the age of digitalization are rapidly beginning to use these technologies in order to reach a more competitive position.

The opportunities offered by this technology have the potential to make the production facilities much more efficient by increasing the efficiency of the equipment in the manufacturing facilities. We recommend that manufacturers who want to take part in global competition should invest in this technology, starting with their most critical equipment. Yet, the return on investment in equipment that is critical for the facility will be maximum. Critical machinery which create a production bottleneck in many facilities and determine the capacity of the facility are ideal for starting. If a data project is to be performed for the first time at the facility, the skills and enthusiasm of the team will play an important and critical role in the success of the project. From time to time, we observe that some projects based on very successful technologies may fail due to teams, who do not believe in data or analysis methods, or are not competent enough. Therefore, we recommend that a company aiming to make such an investment should first create a team with high motivation for innovation, with team members who are interested in data science and experienced in maintenance operations. After all these preparations are made, you can be sure that you will see how possible problems are prevented with predictive maintenance and how failures are solved without causing major damages and long interruptions in production. According to McKinsey's study in 2015, companies investing in predictive maintenance save 10% to 40% in maintenance costs, 10% to 20% reduction in wastage due to defective manufacturing and new development opportunities between 10% and 50% with analysis of the data obtained.

Advanced Trio Mobil IoT Platform provides; the easiest way to establish the infrastructure you need in predictive maintenance, powerful sensor options and big data experience. You can always contact us to get more detailed information about the values that predictive maintenance will add to your business.


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