Collecting Data From Different Sources And Their Quick Analysis

How Technology Is Changing The Role Of Overhead Transmission Line Maintenance

Article originally featured in IT Reseller (Polish Version): edited for Vetasi Website.

When we think about what we as technologists can do to support line workers, safety is primary consideration. Lineworkers have an essential, dangerous job. Not only are they responsible for maintaining equipment that is suspended in mid-air, said equipment is capable of producing hundreds of thousands of volts of electricity. A core principle for any ethical employer is the less time your employees have to spend in a potentially dangerous scenario, the better.

Fortunately, technology is helping to accommodate this aim in a number of ways, developments that are helping line workers both be more efficient at their jobs and spend as little time as possible in harm's way. AI can help organizations monitor their assets, maintain them and predict where failures are more likely. In this way, we can shift the role of the line worker away from routine inspections, so more time is devoted to critical repairs while also making it easier to do their jobs more quickly. All this reduces the costs of maintaining large assets like transmission towers and reduces the amount of time that line workers need to spend working directly with dangerous equipment.

Taking advantage of visual inspection and AR

One of the most apparent ways technology can help improve safety is by eliminating the need to send people into transmission towers when there isn't a problem that needs to be fixed. Visual inspection has improved rapidly in only the last few years in the ease of use, accuracy, and speed with which organizations can train and develop their own AI models. Already today, visual inspection AI can analyze image data gathered by cameras pointed at assets or drones and help detect problems, reducing the need to send line workers into the field for a routine inspection.

If directly work on the equipment becomes necessary, for example, for repair, artificial intelligence can help labourers get their jobs done faster, reducing the time spent in the dangerous area. Line workers can take pictures of a problem with their smartphone, which a visual inspection model can then analyze for flaws like cracked insulators or issues with the conductor and connected structures. Artificial intelligence used together with class systems EAM such as IBM Maximo® Application Suite, supporting maintenance works, is also able to detect failures hardware and system comparing data entrance with a service history of hundreds what is more, it can also suggest potential solutions. Proper selection of tools from the IBM family Maximo Application Suit such as IBM Maximo Visual Inspection will allow full support for the entire control process visual from the acquisition and marking of data, through "training" the model to its implementation and production use. By using a simple smartphone, line operators can even take advantage of augmented reality for further assisted guidance, all of which leads to faster and more accurate resolution of problems when addressing a fix. In the near future, during instances where the line worker needs both hands, these capabilities could be integrated with technology embedded in hardhats or into safety rated smart glasses.

"It is vital to collect data from a variety of sources and to analyze it quickly. We can forecast and discover anomalies using this information through a procedure called predictive maintenance" explains Jarosław Łukasiewicz, CEO of Vetasi.

Better results due to maintenance predictive

Today it's fairly common to use AI models trained in anomaly detection and can alert technicians to unusual temperatures, energy consumption and more. But this barely scratches the surface of AI's potential in this field. Today models are smart enough to put an anomaly into a broader context and assess an asset's health holistically. For example, in the IBM Maximo® Ap plication Suite system, we can track the history of a specific device from the moment of its installation to its decommissioning, and using artificial intelligence, fully estimate its technical condition at a given moment. Thanks to this we know if a given component makes problems as it needs review or repair anyway, or because there might there be a more serious issue. Most recently, AI has also proven that it can help organizations achieve an even deeper layer of analysis which we call predictive forecasting.

Predictive forecasting uses AI to analyze how often a given piece of equipment typically breaks down or how it might deteriorate over time, taking into account factors like how often the equipment is used and the environment where it is stored. By looking at a transmission tower, its history, current health, and its peers' performance, we can make predictions about what repairs might be needed not only in the next few months, but the next several years and is automatically scheduled in the system IBM Maximo® Application Suite. Data stored in EAM systems over longer periods may also form the basis for analyzing "root cause analysis" or even help identify associate defective assets to a 3rd party provider. All these additional insights help utilities companies work with the vendors capable of offering the assets and services of the highest quality, further reducing outages and the costs of maintenance. 

"Modern EAM systems enable businesses to move beyond planned maintenance to condition-based action, which uses machine learning and data analytics to predict the likelihood of future failures and thereby reduce asset failures and associated costs" explains Jarosław Łukasiewicz.

Vetasi is an international consulting firm that specializes in the implementation of IBM's Maximo solution. Jarosław Łukasiewicz continues, "Maximo Predict, which is integrated into the IBM Maximo® Application Suite, identifies trends in asset data, usage, and the environment and associates them with known issues, assisting reliability engineers and maintenance managers in forecasting failures and sharing data and scores."

All of these developments can work in tandem to improve line worker safety. By making assets easier to maintain and repair, we not only make it easier for them to do their jobs quickly and safely, but we make the equipment they have to maintain safer to use and operate as well. This is the right recipe for keeping the workforce strong and our networks humming.

Vetasi Poland, as part of a global group Vetasi, was the winner as Best Sales Partner in 2020. Category: Industry 4.0 - the most innovative implementation. Vetasi is IBM's exclusive European implementation partner IBM MAXIMO - the undisputed leader of the EAM market of class solutions.

It is worth adding that following the spirit of the times, Maximo Applications Suite can be launched in the modern container environment Red Hat Open shift on IBM Cloud. Significantly increases it's quick launch capabilities and scaling the platform, at the same time freeing the customer from the need to manage infrastructure and the environment container which are delivered in the form of a website managed by IBM - says Marcin Klabiński, IBM Cloud Sales Director in Poland and at Baltic countries.

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Collecting Data From Different Sources And Their Quick Analysis