Industrial Automation Magazine India

Prescriptive analytics requires a technology and data-driven mindset

Published on : Friday 07-10-2022

Sunil Joshi, Founder & CEO, DGTRA

Condition monitoring is commonly understood to apply to rotating machinery. What other assets in the plant can benefit from condition monitoring?
Condition monitoring is the process of monitoring a particular condition in machinery or equipment (such as vibration, deformations, performance, temperature, etc.), to identify changes that could predict a potential failure. While the application of condition monitoring evolved around focusing mainly on rotary equipment, its philosophy lately has been applied to almost any equipment, object, or machinery that needs maintenance and upkeep. For example, condition monitoring of the health of the structural system that supports a vessel or equipment is equally important, as important as it is monitoring the machine.
 
There are various Condition Monitoring techniques which include:
1. Manual inspections: involves visual inspections, inspection based on human senses (hearing, touch, etc.), which could be very useful for a basic and preliminary response.
 
2. Supervisory Control and Data Acquisition (SCADA) and Building Management Systems (BMS) can also provide critical data related to pressure changes, flow rate changes, changes in levels, etc., which can be used to predict the deterioration of the asset.
 
3. Integrated Digital Twin Platforms which connect to the feed from several sensors, and equipment and demonstrate the same in a real-time virtual environment. Digital twins are equipped with data analytics, rule-based triggers, and AI integration from Predictive Assessment.
 
What is an estimate of the size of the market? How much of this market is accessible to Indian companies?
As per Markets & Markets, the global machine condition monitoring market size is anticipated to grow from USD 2.8 bn in 2022 to USD 4.0 bn by 2027, at a CAGR of 7.8% from 2022 to 2027. However, there will be the matter of digital twin solutions overlapping with this space. The digital twin market is expected to grow from USD 6.9 bn in 2022 to USD 73.5 bn by 2027; it is expected to grow at a CAGR of 60.6% from 2022 to 2027. The rapid adoption of Industry 4.0 across plants, utilities and machinery will increase the overall potential of digital twin-enabled condition monitoring substantially. When it comes to Indian companies, most of them have been focused on the services industry around this, however, the technology and solutions space is occupied by non-India MNC players. We now see a great push within the Indian start-up space to focus on this segment and hopefully, we will see our increasing share in the technology and solutions space as well.
 
Condition monitoring and prescriptive analytics is a business activity different from legacy sales and services activities. Do you think MSMEs and start-ups have an inherent advantage in getting market share?
Condition monitoring and prescriptive analytics requires a technology and data-driven mindset. While large existing players will have to overcome the challenge of managing the transition and change of the overall business processes; MSMEs and start-ups will have great advantages owing to their lean structure, faster decision-making, higher adaptability, and innovation-led creative approach. Digital data driven approach integrated with cloud and mobile technologies, network enhancement like 5G will result in democratisation of the Services & Solutions business built around condition monitoring and prescriptive analysis. Cost has been one of the barriers for several SMEs and mid-size firms for deploying an effective condition monitoring program. Start-ups have great potential to deliver lean, cost effective solutions, with abilities to provide instant gratification to its implementers.
 
Are there real studies done to establish proof of concept using simulation and digital twin techniques? This needs an intensive collaboration between prospective buyers and vendors. Is it happening in India? With what success?
When it comes to digital twins, the traditional buyer/vendor relationship will take a backseat and we will see more participative business models with a long-term partnership mindset taking prominence. The presence of the world’s leading simulation and digital twin OEMs in India are evidence of the potential of digital twins in India however the level of maturity in most cases is still evolving. There are several successful pilots that have been implemented but organisational implementation is progressive in most cases.
 
Is the appetite for such systems bigger at large plant operators like power plants and refineries?
Commoditisation of IoT, Cloud, and Mobile technologies is removing the entry barriers of high implementation cost and hence making systems more adaptable to all sizes. Ticket sizes are mostly the derivative of scale, complexity, criticality, and extent of automation expected. Effective and efficient operations are required across all types of plants and hence the applicability of condition monitoring and digital twin is very much relevant to all types of assets. Prescriptive maintenance is a potential technological response when using artificial intelligence to suggest alternative plans promptly so that decision-makers can reduce the impact on the system in a dynamic state.
 
Can the government play a role in accelerating deployment of such systems? Are there any schemes for promoting better maintenance using data technologies?
Absolutely, the government can use policy instruments to organise interest and motivation to help accelerate the adoption levels. We already see initiatives from institutions like the Defence Research & Development Organisation (DRDO), which has recently initiated the ‘Development of Indigenous Development of Equipment, Sensors, And Software for Condition Monitoring and Machinery Diagnostics Equipment Within The Country’ program. Also, apex bodies like National Power Training Institutes run programs like ‘Training Program on Condition Monitoring and Preventive Maintenance of Hydro Turbine’. Adoption of a data-driven approach is in progress across sectors and there have been several visible initiatives that demonstrate the focus on data-driven predictive solutions.
 
Does engineering education prepare graduates/post-graduates to design, specify, evaluate, and implement such new-age solutions?
We have seen several finishing/certification courses around condition monitoring and digital twin; however there are hardly any full-time professional programs designed specifically to address this market. Our engineering curriculum does cover the fundamentals of several components of condition monitoring, but a holistic approach is missing. A few institutions do offer courses like Master of Technology (Condition Monitoring Protection and Control of Electrical Apparatus) but the number is rare. We have lately seen great interest from several engineering institutions partnering with industry to bring more relevant curricula and we will see this gap getting bridged very fast.
 
Sunil Joshi is the Founder & CEO of DGTRA (a National Award-Winning Start-up). He is passionate about Digital Technologies and how it can bring about a paradigm shift in the way we deliver projects. By Training, he is a Civil Engineer with a post-graduation in Business Management and an RICS-certified BIM Professional. He brings more than 18 Years of experience in servicing the Construction Industry in their Digital Transformation Process catering to global clients across the US, Europe, APAC, India, and ME. He is also a Visiting Professor and a Member of the Board of Studies at MIT College of Management. Mr. Joshi is also one of the Founding Board members & General Secretary at The Confederation of Digital Construction Practitioners India, A National Society for Advancing Digitalisation in India. Mr. Joshi has been working closely with Large Construction houses in India & overseas as their Strategic BIM Advisor and has been instrumental in re-engineering the business processes to adopt Technology.

(The views expressed in interviews are personal, not necessarily of the organisations represented)