August 16, 2018

Artificial Intelligence for Oilfields and Pipelines [Digital Transformation]

Vibration analysis can help companies manage assets for measured success, plan service calls, track failure modes and increase responsiveness to faults for mining, oil and gas, petrochemical, refineries and original equipment manufacturers. However when you couple up vibration analysis with an artificial intelligence platform that can ingest

large amounts of data from systems like OSI PI, Maximo and SAP to couple this data with historical machine failure data, maintenance records, technician data on where the most qualified person is to make the repair and then algorithmically check the spare parts refurbishment inventory is where the magic of artificial intelligence begins to transform how companies start to move from the old inefficient time based maintenance to condition based maintenance.

Safety & Risk mitigation is a big focus for industrial companies as well, especially pipeline companies that have assets spanning across hundreds of thousands of miles through out the U.S. and globally. Artificial Intelligence can process caustic and corrosion values of various types of liquids and materials running through the pipes asking things like:  At what rates do the corrosive materials start to breakdown the pipelines and affect the structural integrity? 

This is key because then artificial intelligence can give key stakeholders insights into where to go inspect next.  Artificial intelligence can give senior level management personnel insights into where to write their next check or allocate financial resources to mitigate their risks and raise shareholder value by keeping as much materials as they can to pump through the pipelines which is how these companies make money at the end of the day.

In the digital oilfield there is still a big gap between having insights that are leading up to failure versus just having production data. There is a huge opportunity to help companies move from condition-based maintenance to time-based maintenance. There are still thousands of well sites being driven to on a daily basis by personnel like Guager’s that literally drive around from well site to well site writing down analog readings from the meters on production data.  Often times it is only when these Guagers go out to the well site is when they discover the well site has a broken sucker rod or failed pump shaft is broken.  This old way of doing business is inefficient, costly and at the end of the day how the old world of oil and gas works. 

So how do we understand events that are leading up to failure of a sucker rod?  First, we need to understand the anatomy of a sucker road.  A sucker rod barrel is a single-piece hollow tube with threads on both ends. The structure of the barrel's materials can be divided into two groups: base materials and the coating or surface treatment layer. The most common base materials are steel, brass, stainless, nickel alloy and low-alloy steel. These base materials abrasion and corrosion resistances are enhanced by plating and other surface treatment processes.

The most common coatings and treatment processes are chrome plating, electroless nickel carbide composite plating, carbonitriding, carburizing and induction case hardening.  Coated or plated barrels have the largest market share because barrels experience the most wear and operate in severe, abrasive and corrosive environments.  The most commonly sold barrel types are Stainless steel, chrome plated, Plain steel, chrome plated, Brass, chrome plated. Brass, nickel carbide coated, plain steel and nickel carbide coated.

As you can see with these various types of sucker rods and various types of materials there is in inherently variation on their strengths and tolerances when it comes to performing in the field.  With artificial intelligence the platform can ingest these values and provide insights into the failure behavior and signals that are leading up to failure.  This can be a valuable tool for pump operators, designers and manufacturers to have insights into reducing total and complete failure of a well sites.

For upstream operations there is still a big opportunity in the market to help companies understand signals that are leading up to failure that can greatly affect their production.  If something like a top drive, cat walk or casing running tool (CRT) fails in some cases there might be a spare waiting on the sideline but what happens if that fails?  To be able to understand signals that are leading up to failure for these types of components of an upstream operation is not only essential to these operators it is also essential to the OEM’s that are supplying these critical components of the oilfield operations.  This is why more and more companies are turning to artificial intelligence to help then build a better product, better understand the performance of their machines, have insights into signals that are leading up to failure and enabling their workforces to Take Action and Not Just Have Insights.

March 12, 2018

Why we love GE Digital- The “Industrial Michelangelo” (3 Reasons)

Last week Krish, Sri and myself got a link to an article which screamed “Why GE didn't make it big” in Inc magazine. We felt it was being insensitive towards a player who had the guts to re-imagine the industrial future when no one could conceive it. ( Agreed execution / economics are important too and in the next iteration it would evolve :)
So we wanted to frame the discussion differently — more positively and highlight what GE has done right instead of focusing on the negative ( understandably so since negative news garner more eye balls for monetization in the publication industry :)
So here are 3 bold things which GE Digital did right

1. GE Digital unchained the industrial world from the shackles of its electro-mechanical past

Digital twins, Remote diagnostics, Data driven prognostic models detecting early warning signals ( before physics driven models). These were gaming changing ways of running operations . This catalyzed the industrial mindsets to think different and be liberated from the chains of traditional mental models which celebrated 
  • Asset ownership over asset access
  • Physics over Statistics 
  • Capex over Opex
Thanks to GE a host of other industrial companies have woken up to the digital possibilities — Hitachi with Vantara, Siemens with it Mindsphere and Honeywell with Uniformance. Always a visionary peer taking a leap of faith propels others into action.

2. GE Digital and the power of 1 %A

By framing how a 1 % improvement in operational inefficiencies can unlock millions of dollars, they were able to find new levers for cost optimization which were inconceivable before

3. GE Digital tweaked not just AI, but next generation business models

GE did not just innovate on technology but also innovated on business models to deliver their digital services — Asset as a service ( in opex mode) and Pay per outcome were truly game changers.
Closing thoughts …
Pathfinders in uncharted waters are bound to make mistakes and discover things you never envisioned when you started the voyage. It happened to Columbus as he set out in uncharted waters to find India and discovered America in the process :). What if Edison had heeded to journalistic/public pressures at the 500th iteration of light bulb it would hampered the invention of the electric bulb. ( Incidentally Edison said, “I didn’t fail 1,000 times. The light bulb was an invention with 1,000 steps.” :)
So To all the folks at GE Digital — Go where no man has gone before and keep iterating/reinventing. As Michaelangelo said
“In every block of marble I see a statue as plain as though it stood before me, shaped and perfect. I have only to chip away the rough walls that imprison the lovely apparition to reveal it to the other eyes as mine see it.”

GE Digital is truly the Michelangelo for the Industrial World ! 

Others will see what GE Digital saw in time :)

The show must go on !!!

Small Side Note : 
Flutura has also been playing in the same marketplace with our platform Cerebra and have an intimate understanding of whats happening. We compete with GE Predix platform but have also been “brothers” as we both aspire to make industrial companies experience massively transformative outcomes using AI. The “Industrial AI pie” is so large and its not a zero sum game :)

March 11, 2018

Flutura's 6th Anniversary - Cerebra: AI platform for Industrial IoT

Flutura Cerebra AI for IIoT

Flutura completed 6 years!

2017 was a defining year for Flutura since we started our journey in 2012 with lots of achievements to reflect and be proud of.
  1.  We now have one of the Best Ever Customer Base in Industrial IoT - each of them having a potential to become multi-million dollar accounts in the years to come. 
  2. Flutura continues to be seen as a Thought Leader in What We Do globally - Flutura shared its vision on AI in Industrial IoT at Stanford, Flutura will receive the Connected Plants Game Changer Award next week, Investments by Vertex Ventures, Lumis Partners, Hive, and Hitachi who believe in us. 
  3. Cerebra Powering Trans-formative Outcomes - on its journey to become the Most Reliable Industrial Intelligence Platform in the market
But I think the best way to remember 2017 would be to feel privileged to be in the midst of a Potent Team Shaping the Future of Industrial IOT - Pioneering Engineering + AI. 

I am really excited and cannot wait to see the things we would ExperienceLearn and the new opportunities which our customers and partners would Entrust us with, as we move forward through uncharted territories together!  
Thanks to our customers and partners who have believed in us and shaping our journey.

December 27, 2017

Igniting Network Effects in Industrial AI : 2 Real World Examples

70 % of successful consumer startups applied network effects to succeed. For example Apple, Google, Microsoft, Facebook, and Amazon —all flawlessly executed network effects as a strategic lever very effectively to succeed in the hyper-competitive marketplace. 

This is an incredible insight and we at Flutura have been fascinated with cross-pollinating and executing this insight to the Industrial AI sector. So the question we asked ourselves was

“How do we use network effects to achieve massively transformative non-linear outcomes in the Industrial IoT space?”

After 3 years of experimentation and learning, we finally managed to nail the playbook for “Industrial network effect”. We will share 2 industrial examples from the trenches where network effects ignited non-linear outcomes and in the process became a global pioneer in applying network effects for Industrial AI Category. So here they come …

Example-1: A leading manufacturer of Industrial glue

The problem this customer was trying to solve was reduced millions of dollars lost from defective glue quality. In order to do this, they had to process a torrent of high-velocity sensor streams across a battery of plants spread globally in 70+ countries. Flutura developed Industrial AI Nano Apps which surgically predict quality outcomes by processing sensor streams in real time.

How did we achieve industrial network effects here? There we 2 levers which we creatively combined to achieve network effects

Per-App Dimension: Multiple surgical apps to solve ultra-specific factory quality problems were created
Per Factory Line: These apps were deployed in multiple factory lines spread globally across 70+ countries which triggered massive network effects.

Example-2: A leading Oil and Gas OEM Manufacturer in Houston

The problem the customer was trying to solve reduce the operational cost of visiting the site by enabling remote digital diagnostics/prognostics using Cerebra. How did we achieve network effects here? We again activated 2 dimensions to trigger network effects

Per Asset dimension: In keeping with the customers roll out program we created a flexible per asset pricing model where cash outflow for customer is tied to their equipment rollout plans

Per-App dimension: Again we created a catalog of Industrial AI apps which surgically diagnose/predict imminent high-value failure modes for nodal assets in the upstream extraction process.
Both of the above examples illustrate the successful application of network effects to achieve non-linear outcomes in the industrial sector.

Closing Thoughts

Platform-based digital business models + Network effects are 2 powerful constructs which innovators used to disrupt traditional models in the consumer-facing industries of Retail, Banking, Telecom etc. 

As Marc Andreessen famously said

“There will be certain points of time when everything collides together and reaches critical mass around a new concept or a new thing that ends up being hugely relevant to a high percentage of people or businesses. ” 
Flutura is blessed to be at the forefront of executing platform-based business models and network effects for the non-traditional world of Oil and Gas, Manufacturing and Engineering/Energy sectors. This is a massive inflection point and we are just getting started.


October 30, 2017

Transforming 6 Operations across 15 countries for a $10 Billion Process Chemicals Giant [Use Cases]

A lot has been talked about the hype around #Industrial IoT, viable business case, a juicy ROI for IoT platforms. I wanted to share this very proud moment where #Flutura's Cerebra deployment which started as a small app back in 2015 is now the central nervous system driving plant operations worldwide for a Fortune 500 process chemical major.
The customer's global operations and quality teams called for a checkpoint review last week to assess the impact of Cerebra on its operations and looking at new avenues to leverage it for its operations.
We never imagined the number of ways Cerebra could impact day to day operations as it is today. Here are a few areas where Cerebra has triggered concrete actions to transform operations.
1.  Cerebra drives product quality benchmarking - Before Cerebra, every product manufactured had its own quality measurements and was difficult to assess, benchmark and compare the quality across manufacturing batches, across production lines, and across factories to take corrective actions. Cerebra introduced a scientific way to measure product quality in a homogenous manner enabling measurement of performance at all levels. At a single instance, the product quality can be assessed in a simple and intuitive way. The entire plant management team now reviews operations through Cerebra. Old, paper-based review processes have been discontinued.
2. Cerebra learns complex interactions - Before Cerebra it was impossible to attribute causal factors for suboptimal batches due to the multitude and complex nature of data points available. Cerebra is now constantly learning the ever-changing complex interactions and inter-relationships between human, machine, environment, processes, and outcomes throughout the manufacturing process and helping operations and quality teams to intervene at the right time.
3. Cerebra is “THE” root cause analysis system - Before Cerebra, root cause analysis was manual, highly time-consuming and many a time impossible to conduct due to the sheer complexity of the process. Cerebra is now able to isolate the needle in the haystack with ease; one of the manufacturing plants was able to isolate an issue related to the design of the blades used in the industrial mixer to be the cause for variance in viscosity of the manufactured product. The production schedule was changed in order to circumvent this constraint
4. Cerebra pinpoints critical manufacturing step changes - Before Cerebra, macro deviations or step change in operations were difficult to detect. Cerebra is now helping detect macro shifts in processes; a manufacturing plant was able to detect a change in product quality due to a change they made to a supplier of a critical raw material several months before. Cerebra now helps review Supplier performance.
5. Cerebra detects unforeseen patterns - Before Cerebra, repetitive & seasonal patterns were difficult to detect. Cerebra is now helping unearth unforeseen changes to environment leading to undesirable impact; Cerebra detected a peculiar pattern related to variance in quality from a particular shift, on the investigation, the training process was relooked into and operators are now being trained on the new process.
6. Cerebra optimizes Standard Operating Procedures - Before Cerebra, process effectiveness and continuous improvement to the standard operating procedure were subjective and reactive. Cerebra is now helping monitor compliance to standard operating procedures, the effectiveness of standard operating procedures and helping question status quo; Cerebra helped narrow down more-than-a-decade-old quality threshold established for a specific product's BOM and in turn, helped change the product development formulation.
Cerebra IIoT intelligence platform was initially deployed to gain basic visibility into product quality deviations on the manufacturing line; has now
1. Transformed Production Planning
2. Transformed Machine Resource Planning
3. Transformed Supplier Performance Assessment
4. Transformed Product Development
5. Transformed Manufacturing Process Optimization, and 
6. Transformed Operator Training.
Many plans and blueprinting are underway to leverage Cerebra in multiple areas; next being Supply Chain Optimization. 
Cerebra is serving as THE operating system on which Man-Machine interactions are being orchestrated. It has become the Central Nervous System for plant operations.
Can't wait to see a day when Cerebra supports autonomous operations.
It was an amazing moment to see the team rejoice at the impact they had made to our customer's business. Great job team. Look forward to many more transformations

October 5, 2017

Cerebra Feature Series #2 - Discovering Unknown Unknowns [Use cases]

"I have lots of data and I don't know what to do with it"
"We collect a lot of data but not sure whether it is accurate or has any value which can be unlocked"
"We have conducted a lot of experiments and proof of concepts but very few of them have resulted in big operational or financial impacts"
These are the most common experiences we hear in our conversations with customers. These are definitely not trivial questions. We call it the Data-to-$-Chasm.The questions themselves not only relate to unidentified problems to solve but also includes unidentified answers to complex questions which needs macro and micro synthesis of large volumes of data.
Now, does this exercise of finding problems to solve, or finding answers to questions which needs large breadth of data worth the effort ? What if these experiments don't yield a BIG return on effort or investment. The short answer is "YES this is worth the effort"
We have been working on solving this problem for sometime now and has resulted in a core product suite within Cerebra called Pre-Diagnostics to aid engineers' maieutic enquiry process.
In the engineering world, first principles based interrogation techniques are still widely adopted, however when Physics + Chemistry + Ambient condition + Operating Procedures + Human behavior is involved, currently first principles based modeling is complex and sometimes unattainable. You never know there might be a day when the "real world" can be expressed with a universal equation !
For the time being let us stick to Pre-Diagnostics, These are some of the "Never before seen insights" our customers discovered using Cerebra Pre-Diagnostics, which enabled operational process changes and modifications to their industrial equipment ,
  1. A leading industrial glove manufacturer discovered unusual changes to sensor behavior affecting product quality with Cerebra's Swarm Algorithm. Pollen pollution in the shop floor was ascertained as the root cause for this issue and extensive changes were done to set right the ambient operating conditions for the plant. Continuous monitoring of ambient conditions has now been institutionalized
  2. A process chemicals manufacturer discovered deviations to the composition of the end product while it was manufactured with a particular industrial mixer. This was a decade old problem which had gone unnoticed. On further investigation it was ascertained that the type of blade used in the industrial mixer was leading to deviations in viscosity of the product. The machine resource allocation process has now been changed to add constraints related to product - machine mix
  3. A gas processing plant had frequent failures on its valves resulting in periodic unplanned downtimes. The discovery process lead to surfacing insights related to non synchronized feedback loop from the control systems to the valves leading to friction. The plant is replacing its control systems architecture to prevent issues
  4. A leading mining company discovered failure patterns on their milling machines when certain load thresholds were breached consistently over a period of time. New alarms were designed and put in place to detect progressive failure paths
Not long ago people could imagine only white swans, because white swans were all they had ever seen. And so people predicted that every next swan they would see would be white. The discovery of black swans shattered this prediction. The black swan is a metaphor for the uselessness of predictions that are based on earlier experiences, in the presence of unknown unknowns -Taleb,Nassim
Cerebra Pre-Diagnostics helps in identifying problem statements worth solving and use cases worth operationalizing in a short time
Watch out for the next feature in Cerebra Feature Series.

October 3, 2017

Cerebra Feature Series #1 - Digital Twin in 48 Hrs [ Case Study ]

Flutura's team achieved the improbable when they enabled a massive oil field equipment used in early production stages of crude oil extraction to be connected in 48hrs and turned on Cerebra's diagnostics engine to look at operational insights and anomalies of this critical equipment from anywhere in the world.Prior to this, a lot of manual monitoring of the flowrates and other vital signals used to happen.
The machine by itself is a complex integration of Desander, Separation Units, Outlets, Valves and other machine units. The machine is subjected to high pressure of up-to 9000 psi which is the norm in any Oil field equipment. Data from close to 500 sensor tags across the machine units are streamed every second. Cerebra ingests this data with very high reliability and broadcasts critical signals to key stakeholders in real time.
Cerebra's universal machine model helps model any machinery and equipment with ease.
Watch out for the next feature in Cerebra Feature Series.