Uptake says its APM capability for data integrity is a first for the industry, allowing companies to successfully implement predictive maintenance strategies that increase revenues and reduce annual O&M costs by up to 20%.

Companies worldwide are using advanced analytics and machine learning for predictive maintenance strategy optimization as a driving force to reduce operational costs. However, the lack of clean data is a major impediment to achieve those cost savings, says Uptake.

Industrial companies are particularly susceptible to dirty data due to wildly disparate, legacy systems, frequently missing, or miswritten, data, and the prevalence of proprietary systems that have their own data language.

Uptake believes it has solved this problem. “Operational and maintenance data in the industrial, energy, and utilities sector is often inaccurate or missing critical pieces and AI-driven insights are only as good as your data,” said Jay Allardyce, Uptake’s Chief Product Officer. “This Uptake APM capability for data integrity is a first for the industry allowing companies to successfully implement predictive maintenance strategies that increase revenues with better insights and reduce annual O&M costs by up to 20 percent.”

Asset IO, Uptake’s Asset Performance Management (APM) application, includes a new module capability to ingest years of work order data, which comes from existing computerized maintenance management systems (CMMS) and enterprise asset management (EAM) systems. Asset IO leverages artificial intelligence and natural language processing to complete missing data, suggest asset labels, and create an asset category schema where none exists.

The result is a clean dataset for work order cost analysis that can be used to inform critical preventive and predictive maintenance strategies to de-risk operations and reduce annual O&M costs significantly.

“There is a major barrier for industrial plants when it comes to leveraging the value of their assets using current EAM/CMMS software,” said Mike Guilfoyle, Director of Research at ARC Advisory. “These systems are not designed to deal with many of the data challenges common in work orders, such as incorrect or incomplete information, role and location-based variations in word meaning, and the heavy use of slang and shorthand, which can change over time.”

Guilfoyle added: “So much organizational knowledge is locked within these work orders, unable to be leveraged as the key intellectual property it is. By overcoming this barrier, industrial plants can use this knowledge to reduce their O&M expenditures without raising production risk and better execute maintenance strategies aligned with operational excellence goals.”

The Uptake Asset IO application also leverages Uptake’s Asset Strategy Library, which has records of over 58,000 machine failures and over 800 critical asset types and has been trained on over 2.1 billion hours of operational machine data that allows customers to leverage the largest library of asset types, machine failures and preventive maintenance strategies data in the world.

By deploying Asset IO, industries can unlock new operational efficiencies by making proactive maintenance decisions based on predictive insights. Our industrial AI and machine learning engines understand over 10 million different components to help predict and prevent problems before they happen.

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Uptake’s APM software improves productivity and efficiency by leveraging artificial intelligence (AI) to create business value from operational data. Traditional asset management only covers routine maintenance tasks and fails to anticipate and adjust to the ways industry operates its business.

Industrial artificial intelligence provider, Uptake, was named as a 2019 New Energy Pioneer by Bloomberg New Energy Finance (BNEF). The award is in recognition of ground-breaking companies that are fueling the transition to a lower carbon economy and bringing new ideas for business models, technologies, market structures, and commercial opportunities.

“I’m proud of the entire Uptake team and our commitment to our energy, transportation, and industrial customers who use our artificial intelligence platform and applications in the field everyday,” said Uptake CEO Brad Keywell. “Our market leadership has been fortified by our independence from any singular OEM. Further, the impact of our delivered outcomes has been magnified through our ownership of the world’s most comprehensive dataset of equipment failure patterns. Through the Uptake lens, industry productivity looks even better.”

Today’s asset-intensive environments require a new approach with industrial data science generating OEM-agnostic insights, predictions and recommendations for any asset.

With the massive amount of data generated by industrial machines, companies are increasingly searching for simple ways to turn this data into action that improves their bottom line. Using AI and data science, an intelligent industrial platform turns machine data into insights, predictions and recommendations. With insights, people can improve all aspects of industrial performance, make better-informed decisions that impact both top and bottom line financials, and help optimize the overall business.

The winners from more than 185 applicants were assessed against three criteria:

• Potential to scale the opportunity and have global impact
• Level of innovation of the technology or business model and the novelty it brings to the market
• Momentum by showcasing substantive progress in the form of strong commercial partnerships, the distribution channels in place and sales growth

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Wind developers can increase wind-farm operational productivity by quickly noting and addressing underperforming turbines. AI-based software with predictive models can help prevent component failures before they occur, saving unnecessary asset downtime and lost revenue. (Image: Greenbyte)

Increasingly, predictive analytic software takes much of the work and worry out of deciphering the massive amounts of data generated by wind turbines. The goal is to link real-time data to proactive decision-making without drowning in “an unmanageable ocean of data,” according to independent market research firm, New Energy Update. Ultimately, wind operators are trying to reduce their reliance on OEMs for operations analytics and take control of their data.

“Predictive analytics helps owners establish a single source of independent truth across the organization,” explains Ryan Blitstein, VP of Renewable Energy at Uptake, an artificial intelligence (AI) and internet of things (IoT) software provider.

Data delivered directly into the wind flow can predict a problem, sending alerts that allow proactive investigation and repairs. This “fix it before it breaks” strategy is preventing more extensive repairs down the road, thereby saving money and downtime.

Uptake’s asset performance management system, Uptake APM, delivers actionable insights directly into the wind flow, says Blitstein. “So, if the data science engines predict that something is going to happen, it gets to the right person at the right time before something goes wrong.”

Uptake recently released an updated version of Uptake APM. The software is based on the Asset Strategy Library, a comprehensive database of content on how industrial equipment fails. This data, with its extensive industrial machine data experience, helps wind companies predict more failures with greater accuracy, according to Uptake.

Last year, the AI company worked with Iowa-based MidAmerican Energy Company on a blade inspection and repair project.

“We heard over and over again that software companies just don’t ‘get’ wind. So, we put a lot of time configuring our asset analytic products for the particular way wind sites work,” says Blitstein. Within two days of deploying the software at a pilot site, the Uptake data science model predicted a main bearing failure, preventing gearbox failure and saving MidAmerican $250,000.

“We’re allowing companies to be more proactive than reactive,” says Blitstein, leading to a more self-directed approach to managing operations and maintenance.

Likewise, Predict, a new addition to Greenbyte‘s Energy Cloud, a renewable data management system, detects small deviations so operators can avoid secondary failures, which could lead to more downtime and costly repairs.

Greenbyte’s Predict software mimics how the brain works. An artificial neural networks mathematical model learns the behavior of the system, stores it as experience information and then uses it to perform the task it is assigned. (Image: Greenbyte)

The Swedish renewable energy company is piloting Predict with several customers and plans to launch availability early in 2019. In its year-long pilot, Greenbyte found that its system can predict faults as much as two to nine months in advance.

Predict sees alarms as they happen instead of waiting for alerts from the vendor, according to Dr. Pramod Bangalore, Greenbyte’s Head of Research. In one example, Predict was able to alert an operator to a fault in the rotating union, which will be replaced. The system also saw the same pattern within the same wind farm and alerted the customer to a second, identical alarm.

While Predict uses data from SCADA (supervisory control and data acquisition) systems, it sees faults that SCADA data might miss, says Bangalore. In one case study, a maintenance error caused temperatures to rise during peak performance times. Predict alerted the customer to the problem, which was then corrected.

“The customer told us that these kinds of faults are difficult to detect from SCADA statuses,” he said. SCADA typically takes a long time to recognize a problem during high-power events. In addition, Predict can see faults in electric and hydraulic components, an advantage over vibration-based condition-monitoring systems, he notes.

Predict’s monitor portfolio includes a rating that identifies how certain the system is about a problem in a component. High certainty means follow-up is needed and low certainty means an alarm may have been triggered by changing conditions, and that no real threat exists.

These types of predictive analytics save wind-farm operators the headache of managing data themselves.

“Operators don’t have time to analyze data themselves,” says Bangalore. Predict was a response to a customer’s request to more effectively manage critical operational data.

Predictive analytics solve current problems operators face dealing with multiple, often incompatible data sets, including SCADA data and vibration data. In addition, wind companies often manage turbines from multiple manufacturers, which may operate differently across a wind farm. Having a single view of all turbines eliminates the complexity of integrating point solutions.

“It’s about having the power and the knowledge to take greater ownership to drive changes on the ground,” adds Blitstein.

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