Bringing Artificial Intelligence to Remote Access

A new collaboration between IBM and Black and Veatch will help end-users to more effectively sort through the large volumes of data gathered via remote monitoring technology.

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IBM announced a new collaboration with engineering consultancy firm Black and Veatch to combine IBM’s Maximo Application Suite of software products with Black and Veatch’s experience in the field of real-time data analytics.

Remote access use has boomed over the past few years, as companies have sought to maximize productivity with fewer onsite workers. According to Automation World’s 2021 remote access survey, 67% of respondent companies are currently using some form of the technology.

However, while pulling so much more data out of plants than in the past can provide new opportunities for optimization, it can also introduce unwieldly complexity. As the volume of data grows, software dashboards can become overwhelmed with updates, signals, and alarms that important information winds up being ignored entirely. AI (artificial intelligence) can solve this issue by helping end-users sort through the many incoming datapoints more efficiently.

The collaboration between IBM and Black and Veatch will allow intelligence from Black and Veatch’s monitoring and diagnostic centers to be delivered to end-users via IBM’s Maximo Application Suite, which includes applications for predictive maintenance, worker safety, visual inspection, and an assortment of other tasks performed on the plant floor and in the IT department.

As an example of how AI can assist in optimizing maintenance procedures, consider that much maintenance work currently being carried out is preventative in nature. This means it is performed at pre-determined time intervals or in conjunction with pre-selected events  believed to precede equipment failures. While this method can be effective, it can be inexact, and may lead to either too much or too little maintenance work being done. In addition, the rigid guidelines preventative maintenance schedules employ fail to take into account contextual factors.

By contrast, AI can help operators shift to a predictive model of maintenance that couples large, complex datasets with contextual information such as weather conditions to construct real-time analytics that can greatly increase the efficiency of maintenance procedures. Without AI, the utility of the additional data may go to waste, as its uses could be limited by the narrow expertise of individual operators.

“Organizations in every industry need to figure out how to use the vast amounts of data generated within their own systems,” said Kareem Yusuf, IBM general manager for AI applications and blockchain. “Monitoring insights that combine AI and machine learning technology with deep industry expertise can help organizations make better sense of their data and use it to manage their assets better. IBM and Black and Veatch are collaborating to deliver insights that can be applied to improve the performance of assets and extend their lifespans."

The Maximo Application Suite is a cloud-based platform, which means maintenance crews, plant managers, and other staff can share a unified view of all operational data across multiple sites. According to IBM, having access to this single source of truth is imperative to achieving optimal results.

To further this drive for unified data, IBM is also adding Black and Veatch’s digital twin asset models to its Digital Twin Exchange, an online resource available to IBM customers that allows them to attain information made available by asset providers. Types of data shared on the exchange include: Bills of material, parts lists, AI models, maintenance plans, user manuals, and 3D computer-aided design (CAD) models for digital twin visualization.

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