Predictive Software for Sustainable Hydro Power Generation

Predictive Software for Sustainable Hydro Power Generation

Moving to sustainable sources of energy to generate electrical power, as Europe has, requires a balancing act. Solar and wind generation provide an imbalance of power since they only operate when proper atmospheric conditions exist—i.e. sunlight or wind. Hydro generation provides a necessary balance, explained Pier-Vittorio Rebba, technology manager power generation for ABB.

But many hydro plants are aging. Management realizes the need to digitalize operations to obtain the best use of Asset Performance Management applications as well as best optimization of plant assets. ABB and its customer Enel Green Power partnered to digitalize operations delivering predictive maintenance solutions that will lower maintenance costs and transform the performance, reliability, and energy efficiency of its hydropower plants throughout Italy.

The three-year contract will enable 33 of Enel Green Power’s hydroelectric plants, comprised of about 100 units, to move from hours-based maintenance to predictive and condition-based maintenance, leveraging the ABB Ability Asset Performance Management solution. With operations in five continents, the Enel Group’s renewable business line, Enel Green Power, is a global leader in the green energy sector, with a managed capacity of more than 43 GW.

“We are privileged to be partnering with Enel Green Power, a digital pioneer, in their move from hours-based to predictive maintenance utilizing ABB Ability technologies for big data, machine learning and advanced analytics,” said Kevin Kosisko, Managing Director, Energy Industries, ABB. “Predictive maintenance and asset performance management must become a key component of plant operators’ strategies to optimize maintenance operations, minimize risk, improve resilience and reduce costs. The results are more competitive electricity rates, in a more sustainable way.”

Collaborating closely since early 2018, the two companies have jointly developed and tested predictive maintenance and advanced solutions (PresAGHO) via a pilot on five Enel plants in Italy and Spain, including Presenzano, a 1,000-megawatt plant near Naples.

The new contract includes digital software solutions and services that will provide analysis of over 190,000 signals and the deployment of about 800 digital asset models, aimed at improving plant operational performance, reducing unplanned failures and enabling more efficient planned maintenance practices through predictive maintenance. The integration is expected to yield savings in fleet maintenance costs and increase plant productivity.

The ABB Ability Collaborative Operations Center for power generation and water will help bring wider benefits of digitalization and engagement, supporting informed decision-making, real-time solutions and cost savings. The center already provides similar digital solutions and advanced applications for more than 700 power plants, water facilities and electric vehicle charging stations globally.

“With personnel retirements resulting in knowledge gaps and more competitive electricity marketplaces, we believe that many power generation customers globally can benefit from this kind of digital transformation around maintenance and operations,” said Mr Kosisko.

84% of industrial companies face gap between IoT and ERP

84% of industrial companies face gap between IoT and ERP

There are two types of people in industry—operations technology and information technology. God forbid if they should actually talk with each other.

Everywhere I go there is talk of overcoming the OT/IT divide. Something just crossed my email stream where there was a survey about whether the departments have merged anywhere. They were shocked, shocked I say, that only about 1 in 10 companies have merged the two departments. I think the purveyors of that survey must have been on Mars for the past bunch of years.

These people just have different jobs to do. Different things they are measured on. Different ways they contribute to the common welfare of the corporation. However, the technologies they use are overlapping at an ever greater pace.

Here is a survey that once again reveals what is seemingly a disconnect between IT and OT. But I think that interfacing to ERP systems is non-trivial. I’m actually amazed and heartened by the progress we’ve made to date.

I’d take a look at this survey and consider how far we have come—and yet, how far we still need to go.

IFS has released a primary research study on how the Internet of Things (IoT) affects readiness for digital transformation in industrial companies.

According to survey of 200 IoT decision makers at industrial companies in North America, only 16 percent of respondents consume IoT data in enterprise resource planning (ERP) software. That means 84 percent of industrial companies face a disconnect between data from connected devices and strategic decision making and operations, limiting the digital transformation potential of IoT.

The study posed questions about companies’ degree of IoT sophistication. The study also explores how well their enterprise resource planning (ERP), enterprise asset management (EAM) or field service management (FSM)software prepares them for digital transformation and to consume IoT data within enterprise software.

Respondents were divided into groups including IoT Leaders and IoT Laggards, depending on how well their enterprise software prepared them to consume IoT data—as well as Digital Transformation Leaders and Digital Transformation Laggards depending on how well their enterprise software prepared them for digital transformation.

The two Leaders groups overlapped, with 88 percent of Digital Transformation Leaders also qualifying as IoT Leaders, suggesting IoT is a technology that underpins the loose concept of digital transformation.
Digital Transformation Leaders made more complete use of IoT data than Digital Transformation Laggards; Leaders are almost three times as likely to use IoT data for corporate business intelligence or to monitor performance against service level agreements.

Digital Transformation Leaders were more likely than Digital Transformation Laggards to be able to access IoT data in applications used beyond the plant floor. They were more than four times as lilkely to have access to IoT data in enterprise asset management software, twice as likely than Digital Transformation Laggards to be able to access IoT data in high-value asset performance management software, and almost twice as likely to be able to be able to use IoT data in ERP.

The data suggests a real need for more IoT-enabled enterprise applications designed to put data from networks of connected devices into the context of the business.

In reviewing the findings, IFS Chief Technology Officer for North America, Rick Veague, commented, “Are your planning and maintenance systems robust enough to make real time decisions using IoT-sourced data? Many are facing the reality of having to answer ‘no.’ ”

“Study data suggest that the most common use case for IoT in these industrial settings is condition-based maintenance. The benefits go beyond operational improvements and maintenance cost avoidance,” said Ralph Rio, Vice President of Enterprise Software at ARC Advisory Group. “It increases uptime that provides additional capacity for increased revenue. It also avoids unplanned downtime that interrupts production schedules causing missed shipment dates and customer satisfaction issues. When married to demand and scheduling systems in ERP, IoT becomes a revenue-enhancement tool improving the top line.”


IoT Plus Predictive Maintenance Equals Business Sense

IoT Plus Predictive Maintenance Equals Business Sense

Dell Predictive Maintenance IoTPredictive maintenance benefits more from implementation of the Internet of Things than perhaps any other function at this early stage of wide-spread adoption.




I have written on this topic several times over the past couple of years.

Predictive Condition-Based Maintenance

IoT Testbed For Condition Monitoring To Predictive Maintenance

Use Of Internet of Things Enhances Preventive Maintenance

10 Myths About Predictive Analytics (SAP)

A foulup at Starbucks, Preventive Maintenance Prevents Production

Cloud Platforms For Internet of Things

Predictive or Condition-Based

The asset management community has not made it easy for us generalists with its terminology and definitions. Searching for predictive maintenance (PdM) often serves up results for condition-based maintenance. I am not going to attempt a final definition, but I found something that made sense on the OSIsoft Website. “PdM defines methods to predict or diagnose problems in a piece of equipment based on trending of test results. These methods use non-intrusive testing techniques to measure and compute equipment performance trends.”

Condition-based maintenance (CBM) is a methodology that combines predictive and preventive maintenance with real-time monitoring. PdM uses CBM systems to detect fault sources well in advance of failure, making maintenance a proactive process. CBM accurately detects the current state of mechanical systems and predicts the systems’ ability to perform without failure.

Business Risk

The Aberdeen Group, Report: Building the Business Case for the Executive, December 2013, found that 40 percent of 149 manufacturing executives identified failure of critical assets as the top risk they face.

How do we mitigate this risk? Predictive maintenance and condition-based maintenance are methodologies that help. One thing that makes these strategies work is data. With sufficient data along with a model of the asset’s condition at operational efficiency, reliability engineers can begin to predict failures before they happen.

Just like your car, productive assets pick the worst time to fail. This unplanned downtime is exceedingly expensive. Using predictive technologies, managers can plan for shutdowns at an appropriate time. The right parts can be on hand, labor lined up, production schedules adjusted, all because everything can be planned.

I’ve been talking with Dell often since October when I attended Dell World and it unveiled its Internet of Things initiative.

The interesting thing about Dell compared to almost everyone else I cover is that they approach the IT/OT convergence issue from the IT side rather than the OT side.

Dell’s first IoT product is something I think we’ll see more of–analytics at the edge combined with gateway technology that can bring disparate sources of data together, massage them, send them off to the cloud for further analytics, storage, and visualization. Dell’s current partners are SAP for predictive maintenance and Statistica for analytics.

Expect to see more of these partnerships evolve. In some cases, such as PTC, we are seeing acquisitions to add IoT capability. On the other hand, larger companies who do not have enough in common overall to merge will forge partnerships to offer complete solutions to customers.

We see some of this through the rise of Industrial Internet and IP organizations.

Collecting, moving, analyzing, and displaying data is becoming a big and important business. Customer executives will come to appreciate the work as their companies gain efficiency–and profits.

Predictive Software for Sustainable Hydro Power Generation

GE, The Digital Thread, The Digital Twin, The Digital Company

UPDATED: Carpenter’s title changed after I wrote this. Also GE Intelligent Platforms is now called GE Digital.

GE now bills itself as the “digital industrial” company. It has realized the benefits of technologies such as the Watchdog Agent developed by the Center for Intelligent Maintenance Systems for monitoring and prognostics and the Industrial Internet of Things within its own manufacturing processes—especially aircraft engines.
Evidently it now all starts with the “digital thread.” To understand what was meant by this term, I was chatted with Rich Carpenter, Chief of Strategy Technology Strategist for GE Intelligent Platforms Digital.

I asked if this was essentially just a marketing term. “The digital thread is a way to describe a concept,” he told me. “People have become good at “leaning” out the manufacturing process. Now we are leaning out the entire new product introduction cycle. They are optimizing to the end of the path from design to engineering. Closing that loop and carrying forward to manufacturing.”

Companies have accumulated big data infrastructures, so they are also leaning out interactions between digital silos by managing the data flows. This enables remote diagnostics.

Carpenter also mentioned a process I’m beginning to hear around the industry. First you connect things—people, sensors, machines. Then you collect and analyze the data you get from the process. Finally given all this, you can begin to optimize the process.

Official word

Here is a definition from GE, “While the Industrial Internet may be unchartered territory to some manufacturers, early adopters are starting to understand the benefits of the ‘Digital Thread – a web of data created the second they initiated their Industrial Internet journey. The digital thread is the result of several advanced manufacturing initiatives from the past decade, creating a seamless flow of data between systems that were previously isolated.

“This data is essentially the manufacturing health record, which includes data from everything to operator logs to weather patterns, and can be added to as needed. For example, you could compile the digital threads across multiple plants to get a full understanding of the efficiency and health of particular processes and product lines. This record provides data context and correlations between downtimes and outside factors, allowing operators to be proactive in their maintenance strategies.”


I especially appreciate the term “manufacturing health record.” That’s a term Jay Lee at the IMS Center used often in the first phase of prognostics and the Watchdog Agent—a consortium that GE played an active part in.

Digital twin

We’ve heard of cyber-physical systems, and then Industry 4.0 which is a digital manufacturing model based upon it. Now we have a new term, “digital twin” which Carpenter says is a new way to describe a real world physical asset. Then, trying to optimize it, we’ll create a digital representation—a model based on statistics or physics. We run the model, then apply successes of the simulation in the real asset. Then feedback the information.

News release predictive analytics

GE held a conference in September that I could not attend. So, I talked with Rich Carpenter and some marketing people and obtained these press releases. These technologies and applications reveal where GE is heading as a Digital Industrial Company—and where it can take its customers, as well.

GE’s predictive analytics solution, SmartSignal, will be available as part of GE Digital’s Asset Performance Management (APM) solutions on the Predix platform, the purpose-built cloud platform for industry. SmartSignal powered by Predix will deliver anomaly detection with early warning capabilities that is SaaS-based and therefore at a lower cost and at a higher speed, making it accessible to a broader range of distributed equipment.

“Until now, advanced equipment monitoring and predictive anomaly detection capabilities have only been available to enterprises with significant resources, both in terms of machinery expertise and capital,” said Jeremiah Stone, General Manager, Industrial Data Intelligence Solutions for GE Digital. “Because of this, insight gained through predictive analytics has been limited to high value assets due to these cost and knowledge barriers.”

Companies see condition-based maintenance as a means to cut existing operations & maintenance costs. With SmartSignal powered by Predix, they will be able to capitalize on cloud and Big Data platforms to drive more efficient and productive operations.

“There is an unmet need in the industry for a cloud platform that supports the unique requirements of industrial data and operations,” said Harel Kodesh, Chief Technology Officer and Vice President & GM of Predix. “GE Predix is the first cloud platform to meet these demanding requirements. By leveraging GE’s deep domain expertise in information technology and operational technology, Predix provides a modern cloud architecture that is optimized for operational services like asset connectivity, managing and analyzing machine data, and industrial-grade security and regulatory compliance.”

Today, SmartSignal technology provides early warning detection for more than 15,000 critical assets in customer operations. According to May Millies, Manager of Power Generation Services, Salt River Project, “SmartSignal has us listening to the right data and using that data to impact our work operations.” Salt River Project provides reliable, reasonably priced electricity and water to more than two million people in Central Arizona. Integrating data to improve visibility into operations was a key to maintaining their standing with customers. “Now that we have realized the incredible performance of the software and how strong and robust it is, we are improving asset utilization across the enterprise.”

Brilliant manufacturing

In a second announcement, GE announced the next version of its Brilliant Manufacturing Suite. Field-tested and optimized within GE’s own factories, the suite maximizes manufacturing production performance through advanced real-time analytics to enable all manufacturers to realize GE’s Brilliant Factory vision.

“Today’s demands on manufacturers are driving an unprecedented rate of change, innovation and agility,” said Jennifer Bennett, General Manager for GE Digital’s Manufacturing Software initiatives. “Manufacturers are challenged to decide what to build, how to build it, where and when to build it, and how to efficiently maintain it. We believe that the key to optimizing the full product life cycle from design to service is through analytics of data that has been traditionally locked inside corporate silos.”

GE’s Brilliant Manufacturing Suite allows customers to begin to realize their own vision of a Brilliant Factory. Integrating and aggregating data from design to service and leveraging analytics to support optimal decision-making allows manufacturers to drive improvements in end-to-end production. Analyzing data in context and providing the right information at the right time allows for better decision support throughout the manufacturing process. Data-driven analytics encompassing machines, material, people and process will transform the factories of today into Brilliant Factories.

GE’s next generation Brilliant Manufacturing Suite includes:

  • OEE Performance Analyzer – available for early access today, it transforms real-time machine data into actionable production efficiency metrics so that Plant Managers can reduce unplanned downtime, maximize yield and increase equipment utilization.
  • Production Execution Supervisor – digitizes orders, process steps, instructions and documentation with information pulled directly from ERP and PLM systems. Factories are able to ship higher quality products and deliver new product introductions faster by getting the right information in the right hands to focus on the highest priority manufacturing tasks.
  • Production Quality Analyzer – real-time identification of quality data boundaries that catch non-conforming events before they occur. Quality engineers can analyze this information to identify patterns and trends that enable factories to ship higher quality products faster.
  • Product Genealogy Manager – builds a record of all personnel, equipment, raw materials, sub-assemblies and tools used to produce finished goods. Service personnel can respond to customer and regulatory inquiries with confidence, knowing who, what, when, where and how for an individual shipment.
Predictive Software for Sustainable Hydro Power Generation

Use of Internet of Things Enhances Preventive Maintenance

The various parts of the Industrial Internet of Things ecosystem—smart devices, networks, databases, cloud, mobile HMI—really so help manufacturing and production operations, maintenance, and engineering perform better.

Fluke has made strategic acquisitions over the past several years that enhances its technology portfolio. It has brought together many of these technologies to make the IIoT useful. In the case of this new product, enhancing preventive maintenance.

Unplanned downtime due to equipment failure can cost manufacturers up to three percent of their revenue, according the U.S. Federal Energy Management Program. Manual methods of tracking equipment health to predict failures are time consuming and prone to errors and incomplete data, while existing computerized maintenance management systems can be costly and complex and often require significant IT resources to implement.

Fluke Connect Assets changes the way equipment maintenance is documented, reported, and managed.

Fluke Connect Assets is a cloud-based wireless system of software and test tools that gives maintenance managers a comprehensive view of all critical equipment — including baseline, historical, and current test tool measurement data, current status, and past inspection data — enabling them to set up and sustain a preventive maintenance (PM) or condition-based maintenance (CBM) system easily with minimal investment.

It features wireless one-step measurement transfer from more than 30 Fluke Connect wireless test tools, eliminating manual recording of measurements so maintenance managers can be more confident that the equipment history is accurate.

The system’s features allow maintenance managers to analyze multiple types of predictive data (for example, electrical, vibration, infrared images) all in one program, side by side, in a visual format that enables easy scanning. In fact, it’s the first software that allows you to compare multiple measurement types in one system, making it easy to see correlations and spot problems. This intuitive display of multiple measurements enhances the productive use of data and the ability to identify a problem, since each measurement type tests a different aspect of equipment health and together they present a more complete picture.

Key features of Fluke Connect Assets include:

  • Asset Health dashboard — is a hierarchy based overview of the status of all assets over time, with drill-downs to the individual asset record for more details. This permits managers to identify anomalies or correlations across equipment.
  • Asset Analysis – is a record for each piece of equipment that is the single source for all of its maintenance information. Managers and technicians can trend and compare thermal, electrical and vibration data over time for each piece of equipment in order to make optimal repair and replacement decisions.
  • Asset Status dashboard — allows managers to quickly scan the most recent status updates for key assets so they can better monitor team and equipment activity.

Entire maintenance teams can capture and share data via their smartphones regardless of their location using AutoRecord measurements to automatically record measurements from Fluke Connect wireless test tools, upload the data to Fluke Cloud storage and then assign it to a specific asset for sharing and analysis. Technicians can collaborate with their colleagues to discuss problems while sharing data and images in real time with ShareLive video calls, speeding up problem solving, decision-making and approvals.

With the Fluke Connect Assets system, maintenance managers and technicians generate more reliable data to make informed decisions about equipment health, reducing unexpected equipment downtime, improving costs, and enhancing the efficiency of their teams.

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