ITEA is the Eureka Cluster on software innovation
ITEA is the Eureka Cluster on software innovation
30 December 2022 · Source: Erste Yazilim · Download PDF

Rely on PIANiSM, Rely on Your Device

We use many different devices in all areas of our lives. The renewal of some of the devices we use with the developing technology convinces us to move to the upgraded device. However, apart from daily use, it is not possible to frequently upgrade devices that work for a specific task in factories. For this reason, the longevity of such devices with very high financial value by working smoothly provides benefits to the user in every aspect. In order for these devices to have a long life, they should be maintained frequently. But manpower maintenance is no longer sufficient.

The likelihood that any problem will be overlooked poses a great risk. Although it is important to be able to detect existing malfunctions, it is important to detect possible malfunctions before they occur and to take precautions against them as well.

Likewise, enterprises value their projects, capital, and employees. Frequent checks are made to avoid any problems, technical problems, or device malfunctions.

For this reason, predictive maintenance has become a very valuable topic in the industry. Predictive Maintenance is an indispensable part of industrial facilities, which means taking care of the system against problems that may arise as a result of various analyzes.

So why is Predictive Maintenance different from other types of maintenance?

No matter how much maintenance is carried out, the devices used can be damaged quickly due to external factors. These factors can be electrical, mechanical, or environmental. These malfunctions that occur for unexpected reasons also cause system malfunctions.

Predictive maintenance is developed to continuously monitor the health of devices and receive warnings before a malfunction occurs.

Data collection processes that occur through the machine learning system help manufacturers save time and money by detecting a possible malfunction of a device before damage occurs.

Good maintenance technology ensures process reliability; thus, production continues without any interruption.

Predictive Maintenance & Machine Learning

Combining a machine learning system with Predictive Maintenance applications helps to make statistics and predictions for device maintenance easier and more reliable. When such statistics, algorithms, or predictions are not made, a sudden failure might occur, and the intervention takes place after the failure. When caught unprepared for such malfunctions, Corrective Maintenance takes place. This maintenance, which takes place without planning, is the type of maintenance with the highest maintenance cost.

If the fault is noticed late, the maintenance becomes ineffective and causes much greater damage.

Preventive maintenance, which takes place periodically in order to prevent any damage or malfunction, is the maintenance for the predicted damages before the device fails. Preventive maintenance, which plays an important role in device maintenance, is still not sufficient to prevent possible malfunctions.

Predictive maintenance made with the machine learning system offers a more efficient and long-term device life compared to all other maintenance types.

Predictive maintenance under the PIANiSM Project

Within the scope of ITEA, the PIANiSM project, which is carried out in 4 countries with a total number of 16 partner companies, aims to ensure that the valuable devices used in the factories work smoothly and thus prevent possible material and moral damage.

An innovative and industry 4.0-oriented developed system is required by breaking the traditions in production environments. Because innovations for Industry 4.0 allow manufacturers to reduce their costs while increasing the quality of their products. In short, it provides exactly what is desired to companies: much more quality work with much less money.

This system should cover various fields such as data science, machine learning, predictive analytics, simulation, and real-time processing.

The purpose of PIANISM is to provide incomplete analysis techniques and algorithms, thus providing more flexible and applicable solutions for manufacturers.

Since the most important focus of the PIANISM project is the predictive maintenance concept, it is of great importance to have diverse data sets that include different countries and sectors. For this reason, the project has 9 different use-case partners.

Just like how you would take care of something you care about; manufacturers care for their projects and business and want everything to go smoothly. For this reason, PIANISM offers to enhance the capability of the devices of partner companies’ and most importantly aims to prevent potential failures and damages.

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