One size doesn’t fit all: Digitization designed for industry

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Less than 20% of data generated by industrial companies is actually used. Even less is analyzed. This means up to 80% of data is lost for analytics and decision-making.

Originally published in the December 2021 issue of Power engineering

Heavy industries have been slow to take advantage of the digital revolution, causing these crucial sectors to miss out on the hidden efficiencies and asset protections provided by artificial intelligence and a data-driven world.

One size doesn’t fit all.

ABB executives joined Power Engineering for an exclusive Q&A to talk about the ABB Ability Genix Industrial Analytics and AI Suite, the digital revolution for industry, and how to navigate the energy transition using your own data.

Power Engineering (PE) : How has the digital revolution evolved in recent years for industry?

With the advent of digitalization, vast amounts of real-time sensor and operational data, transaction data, and engineering design data is at our disposal from various sources. Our opportunity is to collect and contextualize this data to develop analytics that use artificial intelligence to help users make better business decisions.

Less than 20% of data generated by industrial companies is actually used. Even less is analyzed. This means up to 80% of data is lost for analytics and decision-making. Unless technology is deeply integrated with operational processes, the path to Industry 4.0 value realization will remain slow.

Rajesh Ramachandran, Chief Digital Officer, ABB Process Automation

PE: And how about the impact of A.I.?

RR: The proliferation of artificial intelligence and machine-learning techniques using sensors, digital data, and remote inputs allows us combine information from a variety of sources, analyze it instantly, and act on the insights derived. With improvements in storage systems, processing speeds and analytical techniques, we are greatly improving analysis and decision making.

We have been using artificial intelligence and machine learning to bring a higher degree of prediction accuracy to optimize operations, processes, and assets. Applying AI to industries that one understands and in which one has significant experience enables safer, smarter, and more sustainable operations.

PE: How has A.I. influenced remote monitoring?

RR: Traditional condition monitoring ascertains the health of an asset based on hard-coded alarms and experienced analysts. This approach however can lead to false alarms or late diagnosis of abnormal behaviors or faults. This approach also looks at signals from a single sensor (maybe two) and fails to provide a holistic picture of asset health.

Data analytics, artificial intelligence and machine-learning methods overcome these gaps and diagnose the health of an asset based on a combination of sensor signals to generate prescriptive advice. Additionally, AI techniques can be used to predict future health and calculate the remaining life of an asset. This can reduce maintenance costs and improve production uptime by allowing users to apply reliability-centered maintenance instead of traditional time-based maintenance that may add unnecessary interactions and induce failures.

AI in the future will supplement traditional condition monitoring by establishing an expert system to provide early warning of potential faults, generate prescriptions to address them, and accurately estimate the remaining life of the asset.

PE: What types of other industries are requesting this technology?

RR: All asset-intensive industries can use this kind of technology, including:

  • Oil & gas
  • Chemicals
  • Refining
  • Metals
  • Pulp & paper
  • Cement
  • Mining
  • Power generation
  • Water
  • Food & Beverage
  • Life Sciences
  • Manufacturing

How can Genix improve performance and extend asset lifetime?

One of the key solutions developed in the Genix Industrial Analytics and AI suite is the Genix APM Suite. “APM” stands for “Asset Performance Management.” Genix APM collects and organizes data from operating assets through information and operational technology such as Enterprise Resource Planning, Computerized Maintenance Management and Enterprise Asset Management systems. 

Next, Genix APM calculates expected performance, models faults, and contextualizes data using past performance, AI, and prescriptive and predictive analytics. Key to this contextualization is domain knowledge from both ABB and end-user subject matter experts.  Leading technology in knowledgeable, experienced hands helps users to decide when and how to maintain assets, improve asset performance, extend asset life, and plan asset replacement.

Gino Hernandez, Head of Global Digital Business for ABB Energy Industries

PE: Conventional power generators are under mounting pressure from corporations and investors, often constraining budgets. Is asset performance and monitoring, within digitization, being appropriately prioritized by asset owners?

GH: Asset optimization and health continues to be a high priority in all industrial operations. Digitalization and sustainability interests have accelerated business strategies and corporate initiatives toward better asset utilization. This leads to better cost control in the short term, and delivers strategic advantages over the long term as maintenance schedules are tuned, faults predicted, and issues mitigated before they become problems. Economic conditions are no longer the same as when users originally purchased their assets.  The focus is now on maintaining existing assets in order to retain cash. Maintenance is evolving from reactive or preventive to condition-based, with the next steps being prescriptive maintenance strategies using advanced predictive techniques.

Demand volatility causes end users to operate in a way that was not planned for; therefore, better understanding and control of asset performance is more important than ever. This approach also helps reinforce sustainability by retaining existing assets instead of buying new ones.

PE: What have you heard from customers who have implemented the Genix platform?

A large customer from the power sector is using Genix APM for predictive analytics. Genix APM combines data science and physical modeling to all production assets regardless of manufacturer. The solution helps customers make more informed decisions for maintaining and operating their assets, providing warning of critical failures and health diagnostics of assets while providing performance and efficiency KPIs.

Another large customer that is operating data centers around the world uses Genix to reduce energy usage in cooling systems, which consumes the most electrical power in a data center next to the IT equipment. Optimizing cooling efficiency in turn improves power usage effectiveness and reduces the carbon footprint, with an estimated reduction of 1,100 tons of carbon in the atmosphere for every 10MW of IT load utilized. This is equivalent to saving approximately 18,000 trees. With the ability to glean AI-generated insights, this customer can effectively capture, contextualize, track, and analyze data generated by various systems in the data center, and better facilitate dynamic cooling optimization to reduce costs.

A large mining customer can extract and contextualize data from their operational, information and engineering systems using Genix. With Genix, the user applies advanced analytics to track important KPIs, find root causes wherever there are deviations, and perform predictive and prescriptive analytics.

Tariq Farooqui, Group Vice President and Head of Digital Portfolio Management, PA Digital

PE: What about industrial customers who have to adhere to strict sustainability goals or regulations?

Sustainability goals require accurate measurement of the environmental impact of a particular process, site, or company. At its most simple level, digitalization enables aggregation and visualization of a range of measurement data coming from potentially disparate systems, and converts that data into relevant units such as CO2 output, KWH consumed, Megaliters of water treated, etc.  Live sustainability data allows companies to immediately understand how they are performing against specific KPIs and to take informed action where required. A more intelligent system connects sustainability data with process data to predict future outputs, and can even automatically tune processes to reduce environmental impact.

Many regulatory-driven measurements are required in industry, including chimney stack emissions and water effluent discharge. Measurements are often taken continuously, and require the data to be securely stored for many years. These systems are often complex and require regular maintenance to ensure that accurate and reliable data is collected. Digitalization enables remote management of these systems, thus reducing downtime and easing efforts to operate and manage them. This in turn allows plants to focus on process performance to eliminate emissions, as well as to reduce energy and water consumption.

Dave Lincoln, Head of Global Digital Business for ABB Measurement & Analytics

A large industrial plant could potentially have many continuous emission monitoring systems. These complex systems require skilled onsite personnel to ensure that they are operating accurately with the very high availability requirements demanded. Routine inspection and measurement validation of each system consumes many hours per month, which may not directly benefit the company or the environment in terms of emissions reduction. Digitalization is creating new opportunities to reduce and even remove this manual inspection effort.

Remote monitoring of emission monitoring systems using Genix Datalyzer automates data collection and enables full remote management. Diagnostic data is used to identify potential failure mechanisms before they occur, reducing risk of downtime. Additionally, Datalyzer provides a validation report (known as QAL3 in Europe) of the measurement accuracy that can be given to the local regulatory organization to provide evidence that the system is performing correctly. These features reduce many hours of work each month. This solution provides data-driven insights that are shifting business models toward CEMS-as-a-Service and outcome-based agreements.

PE: How does Genix allow ABB to maximize data from existing clients?

Rajesh Ramachandran, Chief Digital Officer, ABB Process Automation: ABB Ability™ Genix is an enterprise-grade, open architecture-based platform and suite, which harnesses the power of industrial analytics and artificial intelligence to transform cross-functional data into actionable insights. Data used extends across diverse systems: operational, information, and engineering technology. We designed ABB Ability™ Genix with scalability in mind to have a platform that extends from the asset level through asset ecosystems at plants and enterprises, addressing the needs of multiple stakeholders. The basic value proposition is the collection, collation, and contextualization of large amounts of asset data.

Genix offers a key advantage: many of the assets used by ABB customers in their operations come from ABB, such as analyzers, control systems, drives, generators, motors, PLCs, robots, switchgear, and more. ABB is positioned, then, to know how to extract data from these assets, to understand what the data indicates, and to identify opportunities to improve the performance of these assets.

Gino Hernandez, Head of Global Digital Business for ABB Energy Industries:

Every industry has a unique way of operating assets depending on the desired outcome.  For example, a feed pump serving a distillation column is a very different application from a pump circulating cooling water in a power plant.  The digital model for these pumps may be similar, and even display similar information in a dashboard. However, because the application of the model is so different, they require current, accurate, application-specific use cases and models for precise use.  Genix APM can be configured for specific industries, including power generation. Existing uses of Genix APM include diagnosing discrepancies across gas turbine compressors, revealing a leakage problem in an HRSG, and early detection of unbalance in a hydro turbine.

PE: What is the biggest pain point that you hear from customers who are making the digital transformation?

Tariq Farooqui, Head of Sales, ABB Process Automation Digital: Some of the bigger pain points that customers are facing in their digital transformation journey include:

  • Inability to leverage the vast amount of data generated in the organization since the data is in silos; that is, different operational, information and engineering technology systems, and not in a form that can be used for further analysis and decision-making. In fact, many customers see that nearly 80% of their data is not usable.
  • Inability to find dependencies between data points from different sources. Even where the data is available, it is not contextualized.
  • Inability to develop AI models and deploy them at scale. Customers are able to deploy small AI pilots, but are not able to scale in a full production deployment.
  • Inability to develop and deploy analytics applications rapidly with lower time to value.
  • Inability to find suppliers that can bring data, domain and analytics expertise together. Suppliers who are good in AI and analytics often are not able to bring the domain understanding to apply the analytics in the right industrial context. Also, vendors are not able to solve end-to-end requirements.

PE: How does digitization fit into cybersecurity and risk management planning?

Robert Putman, Global Manager, Cyber Security Services: To harness digitalization’s full capability and benefits, one must have confidence that previously unconnected systems won’t add risk to the organization’s overall goals. The advantages of digitalization are improved productivity, less environmental impact, and accelerated innovation. The risk of digitalization and system interconnectivity is low when managed correctly using available security controls and strategies, making the decision of digitalization and its benefits simple.

Take Colonial Pipeline, for example. Colonial Pipeline embraced digitalization by connecting the customer’s meters used to bill the customer for the product received. Under normal circumstances, this approach increased Colonial’s billing quality while reducing labor costs. The implementation focused on the benefits while the associated risk was not prioritized. With relatively minor modifications, benefits could have been realized without exposing the operations to the outcome of the ransomware attack.     

While risk assessments are a common practice in Information Technology, they are often overlooked in Operational Technology. OT systems share many of the same characteristics of IT systems, yet have more Health, Safety and Environmental risk. One must make sure that a compromised IT network will not propagate into the OT network. In the unlikely event of a cyber incident, one must have a documented and tested incident response plan ready to ensure correct and speedy production recovery.

Robert Putman, Global Manager, Cyber Security Services

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