Founder & CEO
Came into existence in 2017, Nanoprecise Sci Corp is a Canadian futuristic tech-company with its APAC head office in Bangalore. Within a short span of time, Nanoprecise has carved its niche in the next-gen industrial technology business by coupling Artificial Intelligence and IoT with physics, material science, and data analytics to diagnose issues with physical assets in various industries. The company is well-known in the field of Condition Monitoring for its precision in diagnosing issues with industrial machinery and predicting the remaining time to failure so that proper corrective actions can be taken at an appropriate time. NASA iTech had recently named NanoPrecise as of the ‘Top 10 finalists for NASA’s iTech Cycle II forum’. CIO Insider is delighted to engage in an exclusive conversation with the company’s founder & CEO, Sunil Vedula.
In conversation with Sunil Vedula, Founder & CEO, Nanoprecise Sci Corp
People have accepted IIoT and other data driven technologies as a necessity for business continuity. The IIoT market is expected to grow from $77.3 billion in 2020 to $110.6 billion by 2025, at a CAGR of 7.4 percent. Where is Nanoprecise positioned in the current IIoT industry against the opportunities that you foresee?
At Nanoprecise, for the past four years, we have been adding value to our customers with our IoT+AI based end-to-end solutions in the field of condition monitoring. As per a recent research report on Condition Monitoring by Frost & Sullivan, globally, the total market for Machine Condition Monitoring is on course to hit $2.7 billion by 2025. This market comprises four major segments: Vibration Condition Monitoring, Thermography, Lubricating Oil Analysis and Affordable Wireless Monitoring.
Affordable Wireless Monitoring, which can also be called as IIoT enabled Condition Monitoring, has the highest CAGR projection despite hosting only 1 percent of the total Condition Monitoring market. In fact, the IIoT-enabled Condition Monitoring has the potential to disrupt as much as 75 percent of the existing condition monitoring market over the next few years. Thus, the TAM for IIoT-powered condition monitoring by 2025 would be $2 billion, which is still just 1.5 percent of the IIoT market that you mentioned.
With our unique six-in-one IIoT sensor and software based on unique & patented algorithms, which have been vetted by reputed companies such as Sensata, Delek, Chevron, Petronas, Tata Steel etc, Nanoprecise is well positioned to grow at a CAGR of at least 100 percent in the next four-to-five years and occupy at least 2-3 percent of the global market share.
Tell us about the solution that your company offers. What are the outcomes and underlying frameworks that we are talking about?
Our unique solution comprises an IIoT based six-in-one sensor called Machine Doctor and AI powered software platform called RotationLF. Our sensor feels vibration, listens to the machine sound, and measures its temperature, speed in revolutions per minute, humidity of the environment & magnetic field near the motor - everything with just one sensor. Just like the way a doctor uses a stethoscope to observe human pulse reading and diagnoses the issue, our Machine Doctor uses six-sense technology and sends data to our software platform RotationLF.
Subsequently, resembling an expert doctor, who uses years of professional experience to diagnose the problem and advise the patients about their health status, healthcare options and time frames of the treatment, our RotationLF platform detects the anomaly, diagnoses the fault and predicts the remaining time-to-failure. This highly evolved predictive maintenance system brings a whole new way of how machines have been maintained and monitored historically.
This was also our largest installation at a single facility, and with more than 1000 sensors deployed it is also the largest IoT sensor installation for predictive maintenance purposes in India till date
Our predictive maintenance system revolves around a five-fold value proposition. It (i) avoids unplanned downtime (ii) reduces over or under maintenance (iii) extends equipment life (iv) decreases the external vendor costs, and (v) decreases the cost of manual labor required for offline monitoring.
We not only are considerate about the overall lifecycle cost of implementing such an application for the user, but also identify the features that can easily bring down the lifecycle cost of PdM solution implementation for the user both in terms of time and money. Additionally, for any PdM solution, the false negative rate is of paramount importance. The solution that has the highest ratio of specificity, which is also known as (1-False Negative) to the cost per sensor per month is the most optimal solution. I also call this PdM Solution Value Metric. As far as we have seen in the market, our PdM solution’s Value Metric is the highest in the industry, which is further corroborated by the fact that we have competed with many startups and well-established brands during some of our customer pilot projects and we have proven this ratio to be the highest.
What is the most challenging deployment that you have done and ended up with a Wow from the customer? Could you tell us the story?
One of our most challenging deployments was actually in India – at one of the largest steel plants in the world. This was also our largest installation at a single facility, and with more than 1000 sensors deployed it is also the largest IoT sensor installation for predictive maintenance purposes in India till date.
Well, as far as the challenges were concerned we had a mountain to climb. The first hurdle was, however, fetching the details of around 250 machines deployed across the entire facility. Getting the customer to provide us with all the details of all these 250 machines was a challenge in itself. But after a few discussions, they recognized the vitality of being informed about the machines while installing sensors, and invested a lot of effort in getting us the details. The next hurdle was the need to ensure strong, seamless internet connectivity for all the 1000 sensors deployed across the 250 machines, covering an area of one square kilometer.
It was quite appreciable the way the customer and their IT networking vendor coordinated with us to finish this milestone in just two months. I, in fact, have never seen such a coordinated team effort within three different entities. The next challenge was delicately drilling and tapping holes in all these 250 machines to ensure that the sensors are properly stud-mounted. The easier way to fix the sensors is mounting them on machines with a magnetic base, but since our customer had raised a concern that these sensors could be stolen if done so, we hired technicians who could do this drilling and tapping work even though we had to take a drilling work permit on multiple occasions.
Finally, ensuring that all the sensors sent the data according to the set parameters and making sure that the false negative & positive rates were kept to minimum were the biggest challenge. This is where our constantly evolving analytics and customized approach towards customer’s operations helped us achieve almost zero false negative rate and low false positive rate.
As a result of this installation, the customer consistently added more numbers in terms of applications in their other businesses. On the other hand, being successful on such a large scale reflected on our performance and scalability in front of other customers in India and globally, which eventually brought us more customers.
What are the opportunities that you foresee? What is Nanoprecise’s future roadmap?
Thanks to our latest investor and partner Sensata Technologies, the opportunities are in abundance. We are looking at multiple applications in Wind Turbine, Solar Farm, Mobility, Discrete Manufacturing etc. As per our product roadmap, we are currently trialing our LTE based Energy Harvesting sensors and within this year we will launch them on a wide scale. Also, we are now absolutely ready to add value to users with the application of our algorithms on top of the data from their sensors.
Truly, as APJ Abdul Kalam said, “Dream is not that which you see while sleeping, it is something that does not let you sleep.” In our case, it's our vision that is our dream that does not let us rest.