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Smart maintenance for machine tools – A holistic approach 22/04/2021

Smart maintenance for machine tools, from intelligent condition monitoring and failure forecasts to maintenance support for service technicians – Fraunhofer IPK’s exhibit at the recent Hannover Messe 2021, which took place in virtual format, encompassed all these aspects

Wear and tear threatens the smooth operation of machine tools. The total failure of a machine is only the worst-case scenario. Modern production systems work so precisely that even the smallest deviations from good condition, such as imbalances in a spindle, can turn a workpiece into scrap. In addition, production processes are so tightly scheduled that malfunctions and unplanned machine downtimes can prevent on-time delivery and may result in compensation claims.

Fraunhofer IPK is developing a solution for intelligent machine tool monitoring and maintenance which not only detects signs of impending failures during operation, but also interactively supports maintenance. With the help of inexpensive sensor technology and machine learning, even the smallest irregularities are identified before they become serious problems.

Previously available smart maintenance systems only monitor the machine status. Fraunhofer IPK‘s solution goes much further. The system addresses three scopes of tasks:

  • machine monitoring
  • damage detection on machine components, and
  • damage repair.

Cloud-based monitoring utilises sensor data to classify the condition of all components. Parameters such as temperature, vibrations, or energy consumption are recorded and automatically examined. The classification determines whether the data can be considered as „normal“ or if it suggests that intervention is required. Through continuous learning, damage detection becomes increasingly precise over time. A traffic light warning system signals whether a component is working properly, will soon require maintenance, or needs acute attention.

If maintenance is called for, the system interactively and intuitively supports the corresponding service procedures. First, a notification is triggered that a machine requires maintenance. During on-site service, a digital assistant supports the service staff. Necessary information – such as documents and instructions, media or sensor information – is stored in a process model and provided depending on the situation. Technicians can retrieve all information on the affected system via a mobile device. Step-by-step instructions support maintenance of the defective component.

At Hannover Messe 2021, Fraunhofer IPK demonstrated the concept using the example of a ball screw – a central component of machine tools that is used to move workpiece carriers or tools with extreme precision.

http://www.ipk.fraunhofer.de/hm21-en

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Engro stays COVID-safe with WorkSafe Analytics 20/04/2021

CHEMICALS MANUFACTURER, Engro Polymer & Chemicals has deployed EmpiricAI's WorkSafe Analytics solution after a 6-week pilot project. The trial involved using EmpiricAI’s innovative WorkSafe Analytics (WSA) at EPCL's plant with KPIs set to monitor staff mask usage improvement and social distancing requirements around COVID-19 requirements.

WorkSafe Analytics, which utilises Artificial Intelligence (AI) and computer vision, provides EPCL rapid and targeted insights into where the business can improve its already robust occupational health policies to reduce risk and avoid operational interruptions.

Following the 6-week pilot project, which began in November 2020, EPCL recorded a multi-fold improvement in monitoring (spot checks vs computer vision) and witnessed more than 80% reduction in social distancing violations within first 4 weeks of pilot deployment and up to 90% reduction in mask noncompliance towards the end of the pilot.

Jahangir Piracha, CEO at EPCL said: “WorkSafe Analytics has proven to be a very effective tool for EPCL. A useful feature of WSA is that it provides us with high quality pictorial evidence of infringements, which is a very effective method to reinforce COVID guidelines and carry out targeted training to avoid future lapses and to ultimately keep our workforce safe.”

Mahmood Siddique, VP manufacturing at EPCL, added: “We are delighted with the results from deploying the WorkSafe Analytics software. The solution has provided prompt analytics for managing COVID SOPs effectively in the workplace and greater awareness of maintaining social distancing.”

Salman Chaudhary, CEO at EmpiricAI, explains: “EPCL has demonstrated that they place the safety of its staff and speedy return to business for its customers above all else. The AI-powered insights, analytics and recommendations help safeguard their workforce while maintaining maximum productivity.

“It is the advanced analytics capability, combined with computer vision, that makes WSA such a powerful management tool. Usability features include KPI dashboard visibility via any authorised PC or tablet, providing notifications and alerts, along with footfall and safety violation as well as heatmaps that identify density of personnel in a specific area.”

Salman concludes: “Our WorkSafe Analytics is rapidly becoming an integral part of a modern health & safety culture - if employees can see that management are committed to health and safety through investment in these solutions, it produces higher levels of motivation and demonstrates concern for health and safety throughout the organisation.

“Ultimately, businesses that invest in WSA achieve lower absenteeism through illness, which also avoids costs for sick pay and overtime cover. In addition, because employees feel safe and secure at work, they are happier, lowering staff turnover and increasing productivity. The return on investment through WorkSafe Analytics is very rapid.”

http://www.empiric.ai
 

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Steady recovery for subcontracting 21/04/2021

THE UK market for subcontract manufacturing continued its recovery from the effects of the Covid-19 pandemic with a 25% increase on the previous quarter.

The latest Contract Manufacturing Index (CMI) stood at 84 for the first quarter of 2021 (Q1) compared to 67 for the final three months of last year. The quarter started well, with January and February far better than any month in the previous quarter, but fell back in March.

The baseline for the index is 100, which represents the average size of the subcontract manufacturing market between 2014 and 2018.

At the end of the first quarter last year, before the pandemic, the CMI stood at 112, so the market is still 25% down.

The CMI is produced by sourcing specialist Qimtek and reflects the total purchasing budget for outsourced manufacturing of companies looking to place business in any given month. This represents a sample of over 4,000 companies who could be placing business that together have a purchasing budget of more than £3.4bn and a supplier base of over 7000 companies with a verified turnover in excess of £25bn.

Within the overall figures for Q1 there were significant changes in processes and markets. Fabrication work made up 57% of the contracts, up by 131% on the previous quarter. Machining, in contrast, fell from representing over half of the market to just over a third.

Industrial Equipment was the strongest sector in the final quarter of 2020 and not only retained the top spot in Q1 but grew by an impressive 90%. The second strongest sector was Furniture, followed by Electronics.

Communication Equipment was in fourth place and Construction staged a strong recovery to take fifth place.

Commenting on the figures, Qimtek owner Karl Wigart said: “In the light of what we are seeing in the wider manufacturing economy, we might have expected the figures to have been a bit stronger. But as we noticed in the previous quarter there are still bottlenecks in material and transport that are resulting in projects taking a longer time than normal to come to fruition. However, we are seeing an increase in the number of projects awarded to our members, and at a higher average price than we have seen for five years.

“When we came out of lockdown last summer there was a strong uptick in the subcontracting market and, as we come out of lockdown again, it would be reasonable to expect a similar boost.”

www.qimtek.co.uk

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Amphibious iFROG robot leaps ahead in ability to inspect and maintain offshore assets 14/04/2021

iFROG, AN amphibious robot capable of working in teams to clean and inspect monopiles above water level and up to 60 metres below (~6 bar), has successfully completed trials at the Offshore Renewable Energy (ORE) Catapult’s National Renewable Energy Centre in Blyth. [1] Based upon an independent analysis conducted by Innovative Technology and Science Ltd.

The multi-robot solution was developed under a three-year project that was funded by Innovate UK and brought together iFROG developer InnoTecUK, ORE Catapult, TWI and Brunel University London. Teams of iFROG robots will be able to clean corrosion and biofouling from monopiles, before inspecting the surfaces and conducting pre-emptive checks of weld integrity.

The technology has the potential to be a game-changer in the offshore wind industry, reducing the safety risks and costs involved in deploying human divers to monopiles, as well as shifting the maintenance approach from reactive to preventative. By upping the frequency and quality of subsea inspections, iFROG can save up to £150,000 per offshore wind turbine per annum [1].

Initial technology demonstrations, which took place at the ORE Catapult dry and wet docks earlier this autumn, saw iFROG navigate a monopile interior using magnetic adhesion and conduct non-destructive testing (NDT).

The robot proved its ability to scale the interiors of monopiles diagonally, not just up and down as wheeled robots have done previously. Its amphibious capabilities were tested too, with the robot moving easily between dry and underwater sections of the monopile.

During the final trials, two robots demonstrated how they can work together in a team above and below the waterline. The first robot performed corrosion mapping and water-jet cleaning of the monopile. The second robot inspected weld lines to assess integrity and flag potential defects.

The technology is also expected to find markets beyond the offshore wind industry by targeting the oil and gas sector, ship hull manufacturing and maintenance, military, and other large-structure related industries, both on and off land.

Panagiotis Karfakis, robotics engineer at InnoTecUK commented: “During the first trials, the consortium validated the performance of the onboard non-destructive testing interfaces, which enable a wide range of equipment for asset inspections. iFROG incorporates essential cleaning gear and can remove biofouling and corrosion from off the steel surface at relatively fast rates.

“The robot is also capable of semi-automated navigation which allows the operator to focus on the important part of the inspection rather than the driving, speeding up the overall operation significantly. The final set of trials has demonstrated the full capabilities of this technology and its impact in real-world scenarios, especially in extreme offshore environments.”

Chris Hill, operational performance director at ORE Catapult added: “iFROG is one of a series of highly ambitious robotics projects coming through our innovation programmes and our Operations and Maintenance Centre of Excellence (OMCE). Deploying robotic platforms in teams across offshore wind sites will tighten up inspection and maintenance schedules, reducing the need for human technicians to deploy in potentially hazardous environments.

“Versatile applications like iFROG have the potential for high economic impact in the future. The UK is already a world leader in operations and maintenance for the offshore wind sector, and that market is set to grow rapidly in the coming decades. With applications now being assessed across a variety of other sectors, the technology is set to be a driver of market expansion and job creation in the coming years.”

[1] Based upon an independent analysis conducted by Innovative Technology and Science Ltd.

https://ore.catapult.org.uk/

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Temperature monitoring for vaccine rollouts 12/04/2021

Reputed for its anti-retroviral drug trials, the University of Kwazulu-Natal (UKZN) has been entrusted with the storage and distribution of COVID-19 vaccines in South Africa. Ian Loudon discusses how temperature and humidity monitoring is vital in such applications where small fluctuations could compromise critical assets.

Unfavourable laboratory temperatures threaten the integrity of valuable research materials, which could be costly and time-consuming to fix. As South Africa’s Medical Research Council (MRC) races to immunise its population, it has become clear that remotely monitoring conditions in a facility is the most convenient and cost-effective way to protect assets.

In partnership with CAPRISA, five UKZN research sites are playing an integral role in South Africa’s vaccine rollout. The vaccines must be stored in -20°C freezers as part of the preparation process, before being transferred into 2-8°C fridges prior to being administered. Local regulatory authorities require each facility to be monitored to prevent temperature deviations compromising the efficacy of the vaccines.

Replacing obsolete systems

Monitoring of operating temperatures in dry storage facilities, fridges and nitrogen freezers across UKZN sites was previously carried out manually. Visual inspections were required, with a staff member recording data on a clip board or spreadsheet, placing unnecessary burdens on staff to manage the facilities as well as fulfilling their normal responsibilities. Omniflex’s Data2Deskop service motto “Measure, Manage, Save” attracted the attention of management.

Since manually recorded data can only reflect the conditions at the time of reading and cannot immediately identify problems that arise between recording intervals, these protocols are impractical. Furthermore, temperature fluctuations occurring when a laboratory is unmanned could compromise valuable assets. In a worst-case scenario, research materials may deteriorate to the point they cannot be used by the time storage temperature issues have been identified and addressed.

With nation-wide vaccine distribution being the only way out of the pandemic, losing assets to poor monitoring protocols is simply not an option. Therefore, UKZN and other CAPRISA partners, like the Aurum Institute in Johannesburg, engaged Omniflex to provide a remote temperature and humidity monitoring solutions.

Learning the tools of the trade

Like the setup provided for Oxford University during its 2020 vaccine trials, plug-in sensors have been placed inside the UKZN fridges and freezers for continuous data recording. These sensors are then networked to the cloud and send out SMS or email alerts in real-time in the event of abnormal temperature variation. For example, if the fridges storing vaccines have ruptured seals, it will likely affect the internal temperature. The relevant personnel will be alerted timeously to ensure appropriate and efficient action is taken.

This technology is more cost-effective and efficient than manual equipment monitoring, and it operates 24 hours a day, 365 days a year. A crucial issue is compliance to FDA 21 CFR Part 11, which suggests any manual recordings could be manipulated. With remote temperature monitoring, data can be collected automatically, free from manipulation, and can capture any errors.

However, the monitoring system alone does not comply with the FDA regulations on Good Laboratory Practice and is integrated into the Standard Operating Procedures of the facility. To be fully compliant, UKZN required a centralised cloud-based system for admin staff to review the archived chronology of operating parameters. Omniflex was able to meet these requirements by installing its Data2Desktop system across the key sites storing vaccines for distribution.

Installed using GSM services, independent of local IT networks, Data2Desktop records a repository of all system data that can be accessed remotely through standard web browsers with designated logins for auditing or post-event analysis. This repository can act as assurance to health officials that the coronavirus vaccine is being stored and distributed correctly. Reporting and alerts are fully automated using emails and SMS to keep management informed 24/7/365.

Ian Loudon  is international sales and marketing manager at Omniflex

https://www.omniflex.com/default.php

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How AI can tackle the engineering skills gap 12/04/2021

THE PROCESS industry workforce is changing. In the UK a recent survey shows that by 2026 19.56% of engineers will have retired or be close to retiring. Andrew Normand explains how AI can play a major role in tackling the resulting skills gap

I was recently talking to a work colleague about how artificial intelligence (AI) is able to continually monitor an entire plant, provide early detection and diagnosis of problems and recommendations to resolve them, and his response was, “we’ve got engineers to do that”.

I replied, “and they’re incredibly valuable, aren’t they?!”

Every plant has a few of these engineers. Those who, if any problems come up, will dig into the data, review what’s been happening, piece all the bits together and diagnose the problem. A lot of them have spent 20, 30, even 40 years understanding the equipment. They’ve seen most problems before and have a gut instinct for how to solve them.

As well as being very valuable, they also tend to be hard to recruit. They often have an eye to retirement and when they do finally exit the industry, a boat-load of experience disappears out of the door.

How, then, do you solve this problem? The general response is to train up younger engineers so they can capture knowledge from the more experienced engineer. Generally, this is difficult, time consuming and has lots of problems associated with it:    

·         Younger engineers don’t want to train to do the same job for the next 20 years anymore

·         Once trained, these young engineers are now valuable and therefore liable to leave for pastures new

·         Time spent developing people is not as directly beneficial to the company. It takes time for the experienced engineer to impart his/her knowledge and time for the inexperienced engineer to take it all in, potentially creating a cost/ resource issue for the company.

There’s a wave of smart, tech savvy engineers who are coming through, eager to learn. What they can’t do is develop 30 years of learning and a gut feel in a few years, so we need to be clear about what skills we need them to develop quickly and what can be done by other means, including artificial intelligence.

AI plant monitoring won’t take away anyone’s job, but it will allow them to focus on the bits that only humans can do – creatively solving problems, strategizing, understanding problems within the wider contexts of plant reliability and efficiency, economics and risk assessment, and weighing up the benefits and trade-offs of making a particular decision.

So, when training up a young engineer, leave the analysis and diagnosis to the AI and concentrate on teaching them the human aspects that escape the capabilities of the technology.

For those who are slightly sceptical of this, consider the advantages of AI plant monitoring applications such as the AI Expert from UptimeAI.

·         it’s based on the domain knowledge of world-class engineers

·         it’s constantly looking at data 24-7, 365 days a year, performing analysis in more detail than a human ever could and does it almost instantaneously. It spots issues months before anyone else would ever notice it

·         it never tires or retires, taking knowledge out the door

·         It’s always available, even at 3am, identifying an issue before your operators do

·         it’s multi-discipline – process, mechanical, electrical, instrument and control, all rolled into one

·         it learns not just from its own experience, but also systems on similar plants located around the world

·         it never forgets what it has seen and is not unduly biased by memories of the last or most traumatic failure

·         it will articulate multiple failure mechanisms and provide detailed quantification on the most likely.

Humans can’t do this, so let’s focus on what they can do but AI can’t:

·         AI operates only in a narrow context. Engineers can see the broader context: economics, strategy, market, health and safety impacts, risk judgement

·         AI can’t be creative, whereas the engineer can solve the problem in a bespoke way

·         AI can’t influence and organise, whereas an engineer can unite people behind a solution

Human skills are important and shouldn’t be wasted on the things that AI can do vastly better when applied correctly.

Focusing on these kinds of skills enables you to develop employees so they’re useful on a much wider scale. Without worrying about the analytics/diagnosis, everyone can focus on the human-dependant aspects of the work and the transfer of skills from one person to another.

This, surely, is a better way to help plug skills gaps within the engineering sector, which is widely recognised to be suffering from a long-term skills shortage.

This approach removes the need for retiring engineers to pass on 30-40 years of knowledge to younger engineers who are unlikely to stay as fixed in the industry and allows training to be focused on human skills in a wider context – benefiting both the engineers and the companies that employ them.

In short, using AI can free up time for engineers to find real-world, practical solutions to problems, make these engineers so much more valuable to an organisation, save the organisation time and money, and provide a solution to the long-standing engineering skills crisis.

Case study

The challenge

A premier power generating company with six units was using preventive maintenance, digital control systems (DCS) alarms, and condition monitoring to identify operational issues. However, the operations team ran into an unexpected generator failure, which led to 45 days of unplanned downtime, causing a loss of US $750,000.

Despite reviewing the operational data with the manufacturer and an independent third party, the engineering team could not work out how this happened without any warnings and how they could possibly predict such failures in the future. The challenge was to find a solution to prevent this from happening again.

The process

The UptimeAI Plant Expert was used in a pilot test. To analyse the generator failure, historical operating data from the generator was loaded into the UptimeAI application.

This application is different to traditional solutions, which rely on manual thresholds to identify impending problems. Manual thresholds for generator signals can be set close to trip limits; hence, the reason why they did not catch this failure. On the other hand, if thresholds are set too low, this can cause hundreds of false alarms. Further individual signal limits do not help explain the impact of other parameters such as load on the generator performance. This approach also requires significant upkeep of these rules.

Unlike manual threshold-based solutions, UptimeAI uniquely combines AI and subject matter knowledge to mimic experts by continuously learning without manual rules and explaining complex issues to help mitigate them. While deep-learning techniques analyse good versus bad behaviour considering all of the operating parameters, built-in fingerprints with failure modes, recommendations and fault-tree analysis enable the AI solution to diagnose and explain the issues.

Results

UptimeAI Plant Expert diagnosed the problem in 30 minutes, identifying six sensor alerts due to missing data, out of range and flatline.

Using an anomaly score as an aggregated metric for the health of the system, UptimeAI would have generated an alert ten months ahead of the failure and a second high-priority alert four months ahead of the failure.

It would have taken several months for an engineer to detect the same fault – or perhaps it would not have been detected at all. This time could have been better spent by the engineer on

·         understanding what this means in the wider context of operating a power generator in the real world

·         finding creative solutions to the problem

·         getting everyone in the engineering team bought into the solution

Benefits

UptimeAI made it easier to predict the impending problem, understand it, and act within minutes without needing to follow any manual rules. Once the issue was detected and understood, the customer realised that maintenance of the equipment could have been scheduled during turnaround or the team could have planned an outage to fix the equipment. As a result, the generator failure could have been avoided, saving US $750,000.

Following the pilot, the customer is now deploying UptimeAI’s predictive maintenance solution for all of the six units.

Andrew Normand is UptimeAI partnership lead for Encora Energy

https://www.encora.energy/

 

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Wireless battery-operated condition monitoring device 12/04/2021

TREON, A leading technology company making intelligent wirelessly connected edge devices for massive IoT solutions of scale, has released the new Industrial Node 6.

Treon Industrial Node 6 is a wireless battery-operated condition monitoring device that measures tri-axial vibration and surface temperature of rotating equipment, such as pumps, motors, and compressors, with a frequency range up to 6.3kHz.

Treon Industrial Node 6 is the newest addition to the Treon product portfolio. The new 6khz sensor operates in a wireless mesh network, making it easy and cost-efficient to deploy on a large scale or to retrofit existing equipment with it.

"The existing 1khz Treon Industrial Node, measuring up to 1khz has been very successful and continues to do so. It is used by many companies globally, including some of the industry-leading giants like Sulzer and ifm", says Joni Korppi, CEO of Treon. "We observed and listened to the market needs for a higher-resolution vibration data sensor that can be reliably deployed on a massive scale, and we developed our new Treon Industrial Node 6, with a frequency range up to 6.3KHz and sampling rate over 26kHz".

With all the benefits of its existing 1khz predecessor, such as cost-efficiency, scalability, and wide compatibility with cloud platforms used by customers, Treon Industrial Node 6 enables root cause analysis by measuring high-resolution data over a wide frequency range to enable highly effective predictive maintenance.

It also enables the usage of existing analytics tools, as it can provide a high frequency, high resolution waveform, in addition to pre-calculated KPIs. Another one of the main benefits of the Treon Industrial Node 6 is the flexible configurability. Its advanced data and signal processing capabilities enable it to meet any customer need.

Treon has developed the Industrial Node 6 in close cooperation with SymphonyIndustrialAI, a global leader in predictive maintenance. With the decision of strong entry into the area of remote asset monitoring, SIAI has chosen to build their wireless condition monitoring offering on Treon Industrial Node 6 and Treon Gateway.

“We are at an inflection point in wireless condition monitoring. Treon has helped us to achieve high resolution that meets our customers’ expectation for wide-scale deployment, which for the first time can offer reliable prediction and prescription of assets”, says Dominic Gallello, CEO of Symphony Industrial AI.

http://www.treon.fi

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Powering up the UK net zero supply chain 31/03/2021

UK COMPANIES across multiple sectors are set to benefit from a £28.5m investment into cutting edge equipment to help them achieve the UK’s net zero ambitions. The investment, from the driving the electric revolution challenge at UK Research and Innovation (UKRI), will provide a critical financial boost to nine facilities located across the UK.

The £28.5 million investment in new equipment, which will be operational later this year, builds on existing capability and fills gaps in the UK’s current capability. The investment will enable a competitive electrification supply chain to be built across sectors, including industrial, transport and energy.

The new equipment consists of:

- a high power integrated electrical propulsion and powertrain accelerator at the Power Networks Demonstration Centre, University of Strathclyde

- assembly lines for power electronics and electrical machines at the North East Innovation Centre, Sunderland

- a high frequency coil manufacturing and magnetic test facility at the University of Nottingham

- a power electronics reliability and failure analysis facility and an electrical machines winding centre of excellence at the University of Warwick

- a wide band gap power electronics component industrial pilot line at Swansea University

- a production line for recycled sintered magnets at the University of Birmingham

- a prototype facility for ceramic and copper elements and subassemblies for integrated modules at the Compound Semiconductor Applications Catapult in Newport.

Led by Newcastle University, this investment will play a vital role in bringing together a UK-wide network of over 30 academic, research and technology organisations. The network gives businesses the opportunity to develop manufacturing process technologies; industrialise the processes needed for power electronics electric machines and drives (PEMD) scale up; and reduce risk by sharing expertise, technical advice and facilities.

Four regional industrialisation centres will coordinate and build on the UK’s national capability to deliver long-term sustainable growth on the road to net zero.

https://www.der-ic.org.uk/

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Construction begins on National Robotarium 31/03/2021

CONSTRUCTION HAS started on a world-leading research facility for robotics and artificial intelligence, the largest and most advanced of its type in the UK.

Based at Heriot-Watt University’s Edinburgh campus, the National Robotarium is supported by £21m from the UK Government, and £1.4m from the Scottish Government as part of the Edinburgh and South East Scotland City Region Deal.

Expected to open in Spring 2022, it will create innovative solutions to global challenges using cutting-edge research, product design and industry collaboration. Bringing together academics and global companies, the facility will provide a catalyst for entrepreneurship and is expected to deliver sustainable economic benefit to Edinburgh, the UK and beyond. Key areas of research application will include hazardous environments, offshore energy, manufacturing, construction, healthcare, human-robot interaction, assisted living and agritech.

Professor Helen Hastie and Professor Yvan Petillot are joint academic leads of the National Robotarium.

Professor Hastie said: “As a world-leading facility that will promote entrepreneurship and drive forward early-stage product development, the National Robotarium will play a significant role in supporting the UK’s economic recovery from the COVID-19 pandemic.  

“By drawing upon the world-class talent of the staff at Heriot-Watt and our collaborative partner, the University of Edinburgh, alongside students at the Centre for Doctoral Training in Robotics and Autonomous Systems, the National Robotarium will form a centre of excellence for fundamental research and knowledge exchange to address real-world challenges and industry needs.”

Professor Petillot added: “The cutting-edge resources provided by the new facility combined with the expertise of our researchers will put us in a highly competitive position to elevate the UK onto the global stage in robotics and AI technologies."

www.hw.ac.uk/uk/research/the-national-robotarium.htm#

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New methodology reduces reliability spending 19/03/2021

LEADING PROVIDER of data-driven reliability and integrity programmes, Pinnacle, has introduced an improved approach to reliability modeling called Quantitative Reliability Optimization (QRO). This new approach streamlines current reliability methodologies into a single, comprehensive analysis, empowering leaders in processing industries to make smarter decisions at a lower cost, reports the company.

“QRO methodology will be instrumental in taking reliability performance to the next level,” said Ryan Sitton, founder and CEO of Pinnacle. “Throughout the development of QRO, we’ve worked with top performers from a variety of processing industries to test the foundation of QRO and have already seen the significant impact that this approach has on equipping facilities to make better reliability decisions. As we continue to validate QRO methodology, we invite other industry leaders to participate by working with us to improve reliability across the world.”

QRO links every relevant reliability data point to the facility level and provides for advanced simulation of reliability investment and associated reliability performance. Because everything is related in one powerful model, a reliability or operational leader can leverage the approach to optimise all reliability and maintenance investments to hit specified future reliability and process safety targets.

“On average, facilities waste between 10% and 30% of their maintenance spend on activities that do not directly impact the reliability of their facility,” said Jeff Krimmel, director of market and data analysis for Pinnacle. “As facilities continue to heavily scrutinise their annual spending, QRO is the exact methodology they need to confidently identify and extract areas of wasted budget.”

Successfully deploying QRO will allow companies to make near real-time decisions by understanding the reliability implications of changes in process, economics, operations or maintenance. In addition, QRO will provide common reliability language between field workers, engineers and corporate executives, which help facilities better align and increase bottom-line value.  

“So much has changed in the industry since we developed the API RP 581 approach in the early 1990s,” said Lynne Kaley, vice president of research and development. “It’s imperative reliability models advance along with technology to increase bottom-line value for processing industries. QRO can leverage a seemingly endless supply of data already coming into process facility systems and perform the more detailed analyses needed to attain insights and make better reliability decisions and associated plans.”

https://pinnaclereliability.com/learn/white-papers/quantitative-reliability-optimization-qro/

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