VIBRIS: Drumil Joshi has Redefined AI for Renewable Energy

Downtime determines resilience in the renewable energy area, with data accompanying that second factor: very seldom can one claim to know it better than Dr. Joshi, a vibrant Monitoring & Diagnostics Analyst of AI at Southern Power, whose work has been recognized internationally and awarded, and even got feature articles in the press. Recently, Drumil presented co-authorship of the landmark paper, VIBRIS: Vibration Intelligence Bearing Reliability Integrated System, at the Vibration Institute Annual Training Conference of 2025. Also Read: Meta AI Adds UPI Lite, Hindi Support, and Deepika Padukone’s Voice to Ray-Ban Glasses in India

VIBRIS remains far from general predictive maintenance software: Conceptual synergy of secure data engineering, ensemble machine learning, and domain knowledge to countenance a setting for wind turbine monitoring across the globe. The interview will focus on the genesis of VIBRIS and the technical innovations which it brings, as well as the future of AI-led renewable energy. Also Read: A Phone That Thinks And Moves? Honor Robot Phone Has A Camera That Pops Out

Question posed by Techlusive. Drumil, you are quickly, becoming an AI innovator in renewable energy with books, research papers, patents, and media under your belt. At Southern Power, you are handling a renewable portfolio amounting to several hundred million dollars. What inspired the conception of your VIBRIS vision, and where does it stand in the broad context of your management?” Also Read: Vivo Launches OriginOS 6 Globally, Including In India: What’s New, How To Update

Drumil Joshi: Thank you. Our vision for VIBRIS stems from a desire to combine creativity and the highest AI with a purposeful mission. In renewables, any efficiency gain equals more clean energy for India and the world, which drives me each day. I believe in innovation-first leadership philosophy-fixing today’s problems with an eye on tomorrow. VIBRIS aims to disrupt traditional wind turbine maintenance by forecasting problems ahead of time. I took industry-related insight from my past across sectors (finance, healthcare, etc.) and treated problems in renewable energy with a bit of lateral thinking. The end product is a system that employs state-of-the-art machine learning but is also concerned with a philosophy stating, “technology has to be a tool for change on its own.” I do feel honoured by those who consider me as being among the best in the industry, but what really matters is the end; preventing failures, saving money, and making renewable operation smarter. VIBRIS has helped me show my vision, combining technical excellence with a forward-looking purpose. It reflects my leadership style of innovation coupled with strategic and empathetic undercurrents-once again aiming to give AI a purposeful and sustainable meaning. I would hope that as people see VIBRIS taking wind turbine reliability to a whole new level, they would also apprehend the larger vision behind it-a push towards the limits of what can be done at the intersection of AI and clean energy.

Techlusive: In your research paper, you describe an ensemble anomaly detection architecture underpinning VIBRIS. Would you walk us through this kind of machine learning design? Why combine algorithms like the Isolation Forest, Local Outlier Factor (LOF), One-Class SVM, and K-Means clustering?

Drumil Joshi: Absolutely. Under VIBRIS, there has to be an anomaly-detection system that’s sensitive enough to detect and reliable to give early indications of turbulence. No algorithm can ever be perfect in a complex noisy environment. Through the combination of models, you increase the strengths of one while reducing the weakness of the other. Isolation Forest is able to catch extremes; it basically tries to isolate points in the data, which in this case, are those that exhibit severe abnormal behavior. This is opposed to the idea of sudden and quick extremes that define an anomaly, such as a spike in vibration from a potential shaft imbalance. LOF, on the contrary, looks at data points in the context of their neighbors in a manner of abnormality and can detect those subtle anomalies that are often overlooked by global methods. The One-Class Support Vector Machine attempts to distinguish boundaries of “normal” behavior from historical data. Incidentally, it is very accurate when it comes to spotting small deviations from this boundary, which are frequently early signs of wear and related evolving faults. In a strange turn of events, K-Means gets tagged in by clustering data points and, thus, measures how far any new observation is from its nearest cluster center. Should any point land being far away from any normal cluster, this is a signal for anomaly detection. K-Means could pick up on slow changes or drifting patterns from the normal state, which could be signals of slow-forming issues. With all algorithms working in tandem, the system VIBRIS compiles a collective score for anomaly detection, essentially an ensemble view. This multi-algorithm approach drastically cuts down on false alarms, filters transient noise, and spotlights genuine deviations of interest. This practically means that it can decipher almost immediately whether some sudden one-time glitches-only anarchies or something substantial is going on with mechanical shifts. Together the models can separate the loud aberrations from almost whispering hints of problems. In the scenario of wind turbines, late installation is intolerable, but false alarms with every gust would be catastrophic. The ensemble provides both high sensitivity and high specificity-a must for predictive maintenance.

Techlusive: The development of VIBRIS was clearly a team effort, with everyone working alongside such top-notch experts. What influence did Christopher Harrison and Riley Andrew in this project?

Drumil Joshi: I am happy you asked; in fact, innovation looks like a team activity. I was indeed lucky to have excellent collaborators. Christopher Harrison, a reliable analyst for over 13-plus years in work with rotating equipment, is a Category IV vibration analyst (one of the highest level). This indicates that Chris has a clear understanding of the health state of machinery. Chris applied pragmatic knowledge in a really unmeasurable application: the real understanding behind those vibration signals from particles in turbines. So whenever the models would mark some anomaly using AI, Chris would mechanically interpret it-if the anomaly was either due to the sensor or a legitimate cause for concern. This ensured that VIBRIS is not detecting anomalies inside failure modes that exist only in theoretical models but are true to the real world. In between raw data and operation, Chris put in lots of insight; that is, what those alterations in raw data mean in terms of changes in machine behavior. Hence, he adjusted the sensitivity of the system so that it picks out issues that really matter while weeding out useless noise.

In contrast, we are offered Andrew Riley’s vision from the high strategy point of view. Because of his background as a Performance M&D Manager with over 55 power plants, including 15 wind sites, his remit covers more than 13,000 MW of generation. Practically speaking, he is a seasoned veteran in predictive analytics for the utility sector. The client kept challenging us on scalability and operational impact, which would have been completely dismissed if it were not for him, turning VIBRIS into a one-off tool for one turbine rather than a tool meant to be integrated across a large turbine fleet and other equipment types. He emphasized integration with the PI data server (real-time SCADA data ingestion) and ensuring that outputs of VIBRIS could be acted upon by maintenance teams in the field. His direction kept the whole process focused on the business value, in other words: detection of an anomaly should somehow translate into a maintenance action or into cost saving. Furthermore, Andrew has that knack for bridging academia and industry, so he pushed the organization to do things rigorously, but package them in a way that engineers can handle.

Techlusive: Many compare predictive systems to IBM Maximo or GE Predix. Where does VIBRIS really stand in relation to such giants?

Drumil Joshi: Those platforms are fantastic broad solutions but VIBRIS is deliberately specialized. It’s like a Swiss Army knife versus a scalpel. We really dug into turbine vibration intelligence by building an ensemble tailored to the nuances of renewable machinery. That focus plus an injunction on secure data handling separates us. In fact, VIBRIS can complement Maximo or Predix by bringing in sharper vibration-specific intelligence onto their broader platforms.

Techlusive: Vibration analysis goes way back. One would ask, what is new here?

Drumil Joshi: What is really new is the marriage of traditions with intelligence. Vibration analysis has always been one of the most important techniques, being reactive in the beginning and currently terribly manual. VIBRIS takes that germ and puts AI, secure pipelines, ensemble, and other analytics over it. The platform, sound from the platform’s chair, does not just detect anomalies-there is a precise and foresightful interpretation of the anomalies. It is tradition reborn into intelligence.

Techlusive: Stretched from a bird’s eye perspective: Where would you next view the VIBRIS methodology going outside turbines?

Drumil Joshi: The VIBRIS philosophy, namely data security, ensemble AI, and anomaly scoring, does not confine itself to any industry’s boundaries. Maybe battery energy storage, solar inverters, or some industrial robotics. Wherever machines generate signals, safeguarding that intelligence ensures reliability. The idea is to take this approach into renewables so that assets everywhere become more predictable, more secure, and more sustainable.

Drumil Joshi: The future is adaptive and collaborative AI. Systems shall learn continuously with the aging of the turbines and baselines will essentially be adjusted in real time. With the second approach of federated learning, wind farms across the globe shall contribute to one overarching AI model without ever sharing raw data. Thus, in some secret fashion, turbines in Texas get educated with the insights that turbines in Denmark have gathered. Ultimately, AI will work towards making renewables not just cleaner, but ultra-reliable and cost-efficient, and VIBRIS represents a hint into that future.

Conclusion

A lot emerges from a conversation with Drumil Joshi: VIBRIS stands far away from being an ordinary research project it’s more or less a vision about how AI might build the next-generation renewable energy sector. Drumil with his team, by secure data practices, ensemble machine learning, and domain expertise, are redefining condition monitoring so much that new global standards are being set. Just because of the intellect of such leaders, one can see a future for renewable energy where green will be synonymous with intelligent resilience.