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Reducing Machine Failure and Downtime with Pattern Recognition and AI

BillMannel

 

Machine failure is often unavoidable during certain phases of the equipment lifecycle, but techniques like artificial intelligence and pattern recognition are helping to pinpoint potential breakdowns earlier – often before they even occur.

In the highly competitive and fast-paced world of industrial manufacturing, there is intense pressure to continuously improve operational efficiency, reduce costs, and drive productivity. Progressive manufacturers are accomplishing this by leveraging accelerated analytics to increase their understanding of their factories and production processes, using information from sensors, operations, and historical logs to gain insight into everything from the supply chain to equipment health. Machine failure is often unavoidable during certain phases of the equipment lifecycle, but GPU-accelerated analytics techniques baPredictiveMBlog.jpgsed on artificial intelligence (AI) and pattern recognition are helping to pinpoint potential breakdowns earlier – often before they even occur.

A recent Strategic Technology Trends report from Gartner named “Applied AI and Advanced Machine Learning” as the #1 trend for 2017. This is a good indicator that these techniques, which were once thought to be beyond the technical expertise and capital budget of the typical manufacturer, are now becoming more accessible and easier to deploy thanks to robust NVIDIA graphics processing units (GPUs) and high performance computing (HPC). These technologies combined with Internet of Things (IoT) expansion are helping manufacturers collect and analyze vast volumes of data, and quickly convert it into actionable insight that can help them proactively maintain equipment, predict and prevent failures, and reduce costly downtime.

IMPROVING PREDICTIVE MAINTENANCE

Recent research from McKinsey estimates that predictive maintenance has the potential to save global businesses up to $630 billion per year by 2025. Predictive maintenance is proven to help manufacturers improve their operations and reduce costs in a variety of ways, by driving lower maintenance expenditures, fewer accidents, less energy usage, and fewer unplanned outages. To capture a real-time view into the status of equipment components and functions, manufacturers are applying predictive techniques to data collected from sensors, contextual information about the equipment, and historical information to predict when a failure might occur. 

With machine learning, a specialized AI technique that uses trained algorithms to help computers to learn and reason using large datasets, manufacturers are leveraging anomaly detection to identify issues with equipment. These techniques quickly isolate signs of equipment deterioration that can trigger preventative maintenance actions before a failure occurs. For example, when AI determines from sensor data that a failure is imminent on a particular piece of equipment, operators can start planning an appropriate time to take it offline, and internal systems can automatically check for necessary spare parts in advance of repairs. Timely maintenance activities can lead to a reduction in operation costs and keep unplanned downtime to a minimum.

REDUCING COSTLY DOWNTIME

Most manufacturers must produce a high volume of goods in order to turn a profit and keep costs low. However, unplanned downtime in production can translate directly to dollars lost. In fact, it has been estimated that unplanned downtime can cost automotive manufacturers up to $22,000 per minute. In addition to predictive maintenance, manufacturers can quickly identify the root case of an impending equipment failure, so they can prioritize troubleshooting and complete repairs in a way that won’t hinder or slow down production activities. This also enables users to correct issues where possible to eliminate future occurrences.

NVIDIA GPUs are designed to accelerate and enable AI applications and help manufacturers more quickly analyze large datasets in order to gain greater insight into all aspects of their operations. When combined with HPC manufacturing solutions from HPE, manufacturers can gain the compute power necessary to handle the requirements of AI with the highest levels of performance and efficiency.

As manufacturers increasingly incorporate advanced analytics and techniques like AI and pattern recognition into their daily operations, they are dramatically reducing the incidence of catastrophic and costly equipment failure and enhancing predictive maintenance capabilities. To find out more about how savvy manufacturers are using AI and HPC to streamline operations and improve profitability, please follow me on Twitter at @Bill_Mannel. And for up-to-the-minute news, customer stories, and updates on the effect of HPC and AI on a variety of industries, please check out @HPE_HPC and @NvidiaAI.


Bill Mannel
VP & GM - HPC & AI Segment Solutions

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About the Author

BillMannel

As the Vice President and General Manager of HPC and AI Segment Solutions in the Data Center Infrastructure Group, I lead worldwide business and portfolio strategy and execution for the fastest growing market segments in HPE’s Data Center Infrastructure Group which includes the recent SGI acquisition and the HPE Apollo portfolio.