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Predictive Maintenance, Maximizing Asset Value

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A Way To Optimization

Imagine a company has just suffered equipment failure. Aside from the costly setback and time-inducing repairs, the cause of the malfunction is largely unknown. It would be a further pain if the problem replicates itself in the future – the company and equipment management team would not have peace of mind.

Wouldn’t it be super if the next time around, there’d be an alert that the system sends to notify proper employees of the malfunction, prompting immediate action? Well, it’s better, but still not the ideal solution. You’d still have to suffer all the inconveniences that comes with inoperable equipment and resulting costs.

Now, what if your system recognizes a change in usual equipment behavior with the help of connected devices and sensors? This change could be an anomaly such as a temperature increase or a shift in an asset location. Your system would notify you of the anomaly detection and suggest you take action or suffer imminent equipment failure. Even a subtle shift in the temperature of a pipeline that is slowly heating up would allow enough data for the system to recognize that something is wrong. This process of data aggregation, analyzation, and visualization would then amount to something that is truly valuable for the enterprise.

Big Data for Smarter Analytics

Predictive maintenance is the early detection and recognition of equipment failure and the deployment of preventive solutions to deal with the problem. Over time, numerous forms of predictive maintenance have taken place to safeguard assets but not until the fourth industrial revolution has this form of preventive care been more efficient. Powered by sensor connectivity, IoT, big data analyzation, and you have the most efficient and modern take on predictive maintenance.

Not only would predictive maintenance save money for the enterprise, but big data analytics would result in potential automation that cuts down time and effort in equipment management. Going back to our overheating pipeline example – if the pipeline showed similar patterns of overheating over multiple instances, our system would not only suggest, but automate our decision making to ensure the appropriate responses are put in place. Both the cases of preventive care and automatized care are of course, made possible by data aggregation and analytics.

The evolution of equipment management started with reactive care, or simply put, reacting after the fact that the equipment is discovered to be faulty or damaged. Proactive care would be a more evolved step, and this would be where preventive maintenance is categorized. There would, of course, be differing gradations of these strategies. Like previously mentioned, automatized care would be a convenience add-on to the already-modernized preventive care.

Data Integration for the Enterprise

Big data analytics is only adopted by 53% of companies in 2017, with undoubtedly less companies using this data to deploy predictive maintenance. For some companies, especially smaller ones, their budget don’t allow for this adoption. However, other businesses are potentially self-encumbered by a trust and familiarity in legacy management systems.

The other ~47% of businesses also hesitate to make the technological leap because of the growing complexity of technology and IoT capabilities. However, the hypothetical ramp to make this leap could propel businesses to new and greater heights of efficient budgeting through preventive care.

If you or your company is a part of this percentage, or if you’d rather invest in streamlined analytics and preventive care, check out ONE Tech’s page – featuring a video, white paper, and demonstration on predictive analytics.

posted Jul 20 by Neeraj N

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+1 vote

The internet of things (IoT) is transforming business across every industry, from healthcare to energy to retail, and the opportunities seem endless. In fact, by the year 2020, it’s estimated that manufacturers will invest $70 billion on IoT solutions, more than double their spending in 2015.

Here at Microsoft, we’re focused on helping our partners and customers in all industries seize IoT opportunities. That is why we’re hosting a new IoT in Action webinar series. Starting in January, we’ll tackle IoT topics from a different industry perspective each month. Our first delivery is all about IoT-enabled predictive maintenance for equipment manufacturers and how artificial intelligence (AI) technology can help you prevent equipment failures, boost product quality, and ultimately, drive profit. Attend the webinar on January 11, 2018 to learn more.

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Benefits of IoT-predictive maintenance

Manufacturers that implement IoT and predictive maintenance garner a number of benefits, depending upon need and application. Benefits may include:

  • Reduced unscheduled downtime: Avoid costly equipment failures and unscheduled down time. Proactively address issues before they become problems that significantly impact operations.
  • Increased quality: Improve products and processes through machine-learning and detect maintenance issues early to increase customer satisfaction.
  • Decreased costs: Lower maintenance costs and extend equipment life.
  • Greater efficiency and output: Increase process efficiency, asset utilization, and production output.

Examples of IoT-enabled predictive maintenance

For a great example of how IoT-enabled predictive maintenance can transform business, let’s look at a steel manufacturer with multiple plants in India. Each plant has multiple arc furnaces that use water cooling panels for temperature control. However, leakages in the panels were causing safety issues as well as production losses. To resolve this issue, the manufacturer worked with Happiest Minds (a Microsoft partner) to build an Azure-based IoT solution that remotely monitors the panels, detects anomalies, and performs root-cause analysis. The implementation of predictive maintenance has prevented failures and production delays throughout the plants while helping ensure employee safety.

In another example, aircraft engine manufacturer Rolls-Royce implemented predictive maintenance on the Azure IoT platform to help their customers reduce costly flight delays caused by engine maintenance issues. Each of their 13,000 engines in operation worldwide has thousands of sensors that monitor engine components and deliver insights around fuel efficiency, engine performance, and operational efficiencies. These insights enable Rolls-Royce to anticipate maintenance needs and avoid costly, unscheduled delays.

Attend the IoT in Action webinar January 11, 2018

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