As wireless technology continues to grow at a fast pace, private 4G and 5G networks are quietly rewriting the rules of industrial connectivity. Built for uncompromising reliability, fortified security, and lightning-fast responsiveness, these purpose-built systems are powering the digital backbones of factories, airports, ports, smart cities, and mission-critical operations where failure is not an option. Yet, deploying them has never been straightforward.&nbsp;&nbsp;The absence of clear frameworks for spectrum allocation in many countries has meant that early adopters have had to navigate uncharted regulatory waters. As a result, innovative deployment strategies have emerged, shaped by industry experts who can bridge technical expertise with regulatory insight. Among these leaders is Rahul Bangera, whose work has helped redefine how private wireless networks are planned and delivered amid uncertainty.&nbsp;Rahul’s career reflects steady progression paired with bold innovation. From starting as an RF Engineer, he advanced to senior leadership roles, eventually becoming Director of 5G Solution Architecture and Product Management at NTT DATA. Notably, one of his defining contributions has been the creation of hybrid spectrum models that combine direct licensing, shared spectrum (such as CBRS in the U.S., Ofcom in the UK, and ISED in Canada), and carrier partnerships. This approach allowed organisations to secure reliable spectrum access, lower risk, and speed up deployment timelines. At the same time, the strategist introduced a flexible Network-as-a-Service model. This model enables companies to move from large capital investments to manageable operational costs while still supporting unlimited device scaling.&nbsp;&nbsp;Building on his global deployment experience, the specialist’s edge in private wireless innovation comes from smart spectrum partnerships. In a maze of unclear rules, he helps clients cut through fast, working with carriers and spectrum holders to skip long licensing delays, cut costs, and deploy quickly. His hybrid mix of direct licenses, shared bands like CBRS, and regional deals delivers reliable, scalable connectivity without huge up-front spend.&nbsp;The expert contributed to automating a global automobile manufacturing plant in the US, built Las Vegas’s smart city network that cut wrong-way driving by 90% and saved $1 million a year, and helped turn Brownsville into a wireless hub, avoiding major infrastructure costs. Beyond American borders, the engineer has led industrial connectivity rollouts for manufacturing giants, petrochemical plants, and healthcare facilities in Europe, tailoring advanced mobile solutions to high-performance and low-latency needs. Equally important, spectrum partnerships have expanded to Canada, the UK, Germany, Belgium, Italy, China, India, Mexico, Chile, Australia and Malaysia. Across these projects, his approach has consistently blended technical precision, regulatory adaptability, and a focus on business value.&nbsp;Security and resilience have been key areas of his work. The strategist has supported on-premises network cores, end-to-end encryption, and network slicing capabilities essential for industrial communications. In the future, the private wireless ecosystem will merge with AI and IoT, enabling more agile and adaptive operational models across sectors.&nbsp;According to the expert, “Spectrum is not just a resource; it’s a strategic asset that demands integrated planning to unlock its full potential.” Industry forecasts point toward wider adoption of hybrid spectrum strategies, greater cross-border regulatory harmonisation, and the expansion of managed enterprise-grade networks, developments aligned with the direction set by early innovators in the field.&nbsp;Ultimately, the evolution of private connectivity in the face of regulatory ambiguity has shown that progress in this space relies on a careful balance of technical expertise, regulatory agility, and business planning. Across industries, these systems are moving from experimental pilots to mission-critical infrastructure, enabling secure, scalable, and intelligent communications. The lessons from early deployments will continue to shape how technology, policy, and enterprise needs intersect to create the connected ecosystems of the future.&nbsp;&nbsp;

Absence of clear frameworks for spectrum allocation has meant that early adopters have had to navigate uncharted regulatory waters. As a result, innovative deployment strategies have emerged, shaped by industry experts who can bridge technical expertise with regulatory insight. 

As wireless technology continues to grow at a fast pace, private 4G and 5G networks are quietly rewriting the rules of industrial connectivity. Built for uncompromising reliability, fortified security, and lightning-fast responsiveness, these purpose-built systems are powering the digital backbones of factories, airports, ports, smart cities, and mission-critical operations where failure is not an option. Yet, deploying them has never been straightforward. The absence of clear frameworks for spectrum allocation in many countries has meant that early adopters have had to navigate uncharted regulatory waters. As a result, innovative deployment strategies have emerged, shaped by industry experts who can bridge technical expertise with regulatory insight. Among these leaders is Rahul Bangera, whose work has helped redefine how private wireless networks are planned and delivered amid uncertainty. Rahul’s career reflects steady progression paired with bold innovation. From starting as an RF Engineer, he advanced to senior leadership roles, eventually becoming Director of 5G Solution Architecture and Product Management at NTT DATA. Notably, one of his defining contributions has been the creation of hybrid spectrum models that combine direct licensing, shared spectrum (such as CBRS in the U.S., Ofcom in the UK, and ISED in Canada), and carrier partnerships. This approach allowed organisations to secure reliable spectrum access, lower risk, and speed up deployment timelines. At the same time, the strategist introduced a flexible Network-as-a-Service model. This model enables companies to move from large capital investments to manageable operational costs while still supporting unlimited device scaling. Building on his global deployment experience, the specialist’s edge in private wireless innovation comes from smart spectrum partnerships. In a maze of unclear rules, he helps clients cut through fast, working with carriers and spectrum holders to skip long licensing delays, cut costs, and deploy quickly. His hybrid mix of direct licenses, shared bands like CBRS, and regional deals delivers reliable, scalable connectivity without huge up-front spend. The expert contributed to automating a global automobile manufacturing plant in the US, built Las Vegas’s smart city network that cut wrong-way driving by 90% and saved $1 million a year, and helped turn Brownsville into a wireless hub, avoiding major infrastructure costs. Beyond American borders, the engineer has led industrial connectivity rollouts for manufacturing giants, petrochemical plants, and healthcare facilities in Europe, tailoring advanced mobile solutions to high-performance and low-latency needs. Equally important, spectrum partnerships have expanded to Canada, the UK, Germany, Belgium, Italy, China, India, Mexico, Chile, Australia and Malaysia. Across these projects, his approach has consistently blended technical precision, regulatory adaptability, and a focus on business value. Security and resilience have been key areas of his work. The strategist has supported on-premises network cores, end-to-end encryption, and network slicing capabilities essential for industrial communications. In the future, the private wireless ecosystem will merge with AI and IoT, enabling more agile and adaptive operational models across sectors. According to the expert, “Spectrum is not just a resource; it’s a strategic asset that demands integrated planning to unlock its full potential.” Industry forecasts point toward wider adoption of hybrid spectrum strategies, greater cross-border regulatory harmonisation, and the expansion of managed enterprise-grade networks, developments aligned with the direction set by early innovators in the field. Ultimately, the evolution of private connectivity in the face of regulatory ambiguity has shown that progress in this space relies on a careful balance of technical expertise, regulatory agility, and business planning. Across industries, these systems are moving from experimental pilots to mission-critical infrastructure, enabling secure, scalable, and intelligent communications. The lessons from early deployments will continue to shape how technology, policy, and enterprise needs intersect to create the connected ecosystems of the future.

Absence of clear frameworks for spectrum allocation has meant that early adopters have had to navigate uncharted regulatory waters. As a result, innovative deployment strategies have emerged, shaped by industry experts who can bridge technical expertise with regulatory insight.&nbsp;

The blueprint for ambiguity: Navigating the spectrum maze with private 5G

In highly regulated industries, navigating change requires more than just adopting new processes, it demands a strategic approach that ensures compliance, efficiency, and workforce alignment. Organisations operating in sectors such as healthcare, life sciences, and financial services must implement transformation initiatives while balancing regulatory requirements and business objectives. Effective change management plays a crucial role in minimising disruption, enhancing operational resilience, and fostering long-term success.&nbsp;Organisational Change Management (OCM) is a critical component in driving meaningful transformation across industries, ensuring that strategic objectives align with actionable execution. With a career spanning multiple industries, Priyanka Taranekar has led large-scale change initiatives that have reshaped business operations and driven measurable success.&nbsp;&nbsp;She was recognised with prestigious awards such as Consulting Magazine’s Top Consultant Award for Leadership in 2025, celebrating her commitment to delivering enterprise-wide transformation, empowering teams, and driving measurable impact in the healthcare and life sciences sector.&nbsp;Priyanka was also honoured to be featured in “Marquis Who’s Who”, a recognition reserved for individuals who have demonstrated outstanding professional achievement and leadership in their field. This acknowledgment reflects the impact of her work in driving transformation across healthcare and life sciences through strategic innovation and purpose-driven leadership.&nbsp;In the workplace, Priyanka has been consistently recognised for her exceptional contributions—earning the Asia Pacific Region Recognition Award at Towers Watson, multiple Applause Awards at Deloitte, and the prestigious Mogul Award at Slalom for her impact, leadership, and commitment to excellence – over the years.&nbsp;&nbsp;Priyanka has consistently delivered transformational impact through innovative strategies and structured change management methodologies.At the core of her work lies a commitment to aligning business objectives with tailored solutions that foster collaboration and measurable outcomes. Through innovative change management strategies, structured learning programs, and actionable insights, she has empowered organisations to navigate complex challenges and sustain long-term success. By designing frameworks that integrate business strategy with operational execution, she has driven strategic alignment across multiple industries. Her expertise in creating personas, journey maps, and targeted training programs has significantly enhanced workforce engagement, minimised resistance to change, and facilitated the seamless adoption of new systems and processes.&nbsp;Reportedly, one of Priyanka’s most impactful initiatives was leading the learning and development efforts for a regional healthcare provider. The project involved creating an instructional training curriculum, implementing in-app guidance, and rolling out structured training programs to ensure the smooth adoption of a new application. “The initiative resulted in significant adoption rate within the first three months, with impactful increase in task efficiency compared to legacy systems” she stated.&nbsp;&nbsp;Priyanka has also played a vital role in enterprise-wide technology implementations. As a change strategist for a contract research organisation in life sciences, she helped facilitate the implementation of a Workday ERP system. Her approach included readiness assessments, structured change strategies, and customised training programs for HCM that contributed to more efficient workflows and process improvements.&nbsp;Beyond technical implementations, she has helped organisations enhance cross-functional collaboration. Whether redesigning customer loyalty programs for a global pharmaceutical and aesthetics company or developing learning programs for a national title insurance firm, she has supported efforts to align stakeholders and drive program success. These initiatives have helped organisations refine their engagement strategies and significantly optimise program execution.&nbsp;Implementing change in large organisations comes with challenges, including resistance to new systems, fragmented processes, and stakeholder misalignment. She has addressed these challenges by conducting readiness assessments, developing role-specific engagement plans, and implementing structured communication strategies.&nbsp;Her approach has eased team transitions to new systems and boosted adoption rates.&nbsp;As organisations continue to navigate an evolving business landscape, Priyanka highlights the growing role of AI-driven change management tools, digital learning experiences, and agile frameworks that support adaptive transformation. She emphasises the importance of leadership alignment, clear communication, and structured feedback loops to refine change strategies over time.&nbsp;A white paper published by her, "Strategic Change Management in Large-Scale Transformations: Lessons from the Healthcare Industry: Insights into embedding change strategies in complex, multi-stakeholder environments” explores these strategies in depth, providing organisations with guidance on implementing effective change initiatives. Through her experience and expertise, Priyanka remains committed to helping businesses successfully manage change and build a foundation for long-term success.&nbsp;Effective change management is essential for organisations navigating complex and highly regulated industries. By focusing on structured transformation strategies, leadership alignment, and workforce engagement, businesses can overcome challenges and drive meaningful progress. Priyanka’s expertise in designing tailored solutions and implementing impactful change initiatives has helped organisations bridge the gap between strategy and execution. As industries continue to evolve, the role of change management will remain a cornerstone of sustainable growth. Through continued innovation and thought leadership, Priyanka is shaping the future of organisational transformation, equipping businesses with the tools needed to adapt, thrive, and lead in an ever-changing environment.&nbsp;

Priyanka Taranekar has led large-scale change initiatives that have reshaped business operations and driven measurable success. 



In highly regulated industries, navigating change requires more than just adopting new processes, it demands a strategic approach that ensures compliance, efficiency, and workforce alignment. Organisations operating in sectors such as healthcare, life sciences, and financial services must implement transformation initiatives while balancing regulatory requirements and business objectives. Effective change management plays a crucial role in minimising disruption, enhancing operational resilience, and fostering long-term success. Organisational Change Management (OCM) is a critical component in driving meaningful transformation across industries, ensuring that strategic objectives align with actionable execution. With a career spanning multiple industries, Priyanka Taranekar has led large-scale change initiatives that have reshaped business operations and driven measurable success. She was recognised with prestigious awards such as Consulting Magazine’s Top Consultant Award for Leadership in 2025, celebrating her commitment to delivering enterprise-wide transformation, empowering teams, and driving measurable impact in the healthcare and life sciences sector. Priyanka was also honoured to be featured in “Marquis Who’s Who”, a recognition reserved for individuals who have demonstrated outstanding professional achievement and leadership in their field. This acknowledgment reflects the impact of her work in driving transformation across healthcare and life sciences through strategic innovation and purpose-driven leadership. In the workplace, Priyanka has been consistently recognised for her exceptional contributions—earning the Asia Pacific Region Recognition Award at Towers Watson, multiple Applause Awards at Deloitte, and the prestigious Mogul Award at Slalom for her impact, leadership, and commitment to excellence – over the years. Priyanka has consistently delivered transformational impact through innovative strategies and structured change management methodologies.At the core of her work lies a commitment to aligning business objectives with tailored solutions that foster collaboration and measurable outcomes. Through innovative change management strategies, structured learning programs, and actionable insights, she has empowered organisations to navigate complex challenges and sustain long-term success. By designing frameworks that integrate business strategy with operational execution, she has driven strategic alignment across multiple industries. Her expertise in creating personas, journey maps, and targeted training programs has significantly enhanced workforce engagement, minimised resistance to change, and facilitated the seamless adoption of new systems and processes. Reportedly, one of Priyanka’s most impactful initiatives was leading the learning and development efforts for a regional healthcare provider. The project involved creating an instructional training curriculum, implementing in-app guidance, and rolling out structured training programs to ensure the smooth adoption of a new application. “The initiative resulted in significant adoption rate within the first three months, with impactful increase in task efficiency compared to legacy systems” she stated. Priyanka has also played a vital role in enterprise-wide technology implementations. As a change strategist for a contract research organisation in life sciences, she helped facilitate the implementation of a Workday ERP system. Her approach included readiness assessments, structured change strategies, and customised training programs for HCM that contributed to more efficient workflows and process improvements. Beyond technical implementations, she has helped organisations enhance cross-functional collaboration. Whether redesigning customer loyalty programs for a global pharmaceutical and aesthetics company or developing learning programs for a national title insurance firm, she has supported efforts to align stakeholders and drive program success. These initiatives have helped organisations refine their engagement strategies and significantly optimise program execution. Implementing change in large organisations comes with challenges, including resistance to new systems, fragmented processes, and stakeholder misalignment. She has addressed these challenges by conducting readiness assessments, developing role-specific engagement plans, and implementing structured communication strategies. Her approach has eased team transitions to new systems and boosted adoption rates. As organisations continue to navigate an evolving business landscape, Priyanka highlights the growing role of AI-driven change management tools, digital learning experiences, and agile frameworks that support adaptive transformation. She emphasises the importance of leadership alignment, clear communication, and structured feedback loops to refine change strategies over time. A white paper published by her, "Strategic Change Management in Large-Scale Transformations: Lessons from the Healthcare Industry: Insights into embedding change strategies in complex, multi-stakeholder environments” explores these strategies in depth, providing organisations with guidance on implementing effective change initiatives. Through her experience and expertise, Priyanka remains committed to helping businesses successfully manage change and build a foundation for long-term success. Effective change management is essential for organisations navigating complex and highly regulated industries. By focusing on structured transformation strategies, leadership alignment, and workforce engagement, businesses can overcome challenges and drive meaningful progress. Priyanka’s expertise in designing tailored solutions and implementing impactful change initiatives has helped organisations bridge the gap between strategy and execution. As industries continue to evolve, the role of change management will remain a cornerstone of sustainable growth. Through continued innovation and thought leadership, Priyanka is shaping the future of organisational transformation, equipping businesses with the tools needed to adapt, thrive, and lead in an ever-changing environment.

Priyanka Taranekar has led large-scale change initiatives that have reshaped business operations and driven measurable success.

Priyanka Taranekar leads innovation, redefining organisational change in regulated industries

Drumil Joshi is recognised as an expert in the field of AI for renewable energy. As an M&amp;D Analyst at Southern Power Company, he holds an MS in Data Science and has patents, research papers, and a textbook to his credit. He prefers speaking at top conferences and has appeared on Authority Magazine and several other platforms. In 2025, Drumil Joshi and his team launched the VIBRIS project, one of the foundations for predictive maintenance for wind turbines.The full name of VIBRIS is Vibration Intelligence Bearing Reliability Integrated System next-generation condition monitoring platform. VIBRIS takes the data generated from the turbine sensors, saves it through tokenised pipelines and HEX-encoding, and passes it down to a powerful ensemble of anomaly detection algorithms (Isolation Forest, Local Outlier Factor, One-Class SVM, K-Means) for processing. The results at the turbine end are: early faults detected with precision, fewer false alarms, and transition from reactive turbine maintenance to proactive turbine maintenance.We spoke with Drumil about VIBRIS' origins, the technology behind it, and the bigger picture of AI in sustainable infrastructure.<h2>From idea to impact</h2>Q: What inspired VIBRIS?Drumil Joshi: An ideation from wanting AI for something meaningful. Most wind farms were operating in a reactive manner-maintain turbines after failures had already occurred. I wanted to look at the same thing from a different perspective-ahead of-time problem forecast, and avoid costly outages of turbines and hence unblocked renewable energy generation. I borrowed insights from finance and health care and had to rethink turbine monitoring via AI.For me, this is not about personal accolades; rather, failure prevention and cost minimisation with improved clean-energy reliability would embody the milestones of success. VIBRIS could stand for either very good technical prowess or bright purpose: smarter green operations.<h2>Why Did It Choose the Ensemble AI Approach?</h2>Q: The VIBRIS takes advantage of a four-model ensemble. Why not just one algorithm?Joshi: In a noisy turbine environment, no single model catches everything. Each does its own:Isolation Forest: very good at spotting extreme outliers, such as sudden vibrations in the readings.The Local Outlier Factor (LOF) method tries to detect slight local irregularities.One-Class SVM tries to learn the normal behaviour from history and then raises abnormalities.K-Means Clustering: cluster normal patterns outside the subsets of interest.All four methods are combined in tandem to produce the composite anomaly score, which help to reduce false alarms and at the real time catch real issues. Think of it as an ability to distinguish between an occasional gust of wind and an actual gearbox fault.<h2>Data Security First</h2>Q: Why do you stress the pipeline's security?Joshi: Energy data is sensitive data. Without good security in place, the whole operation would be a failure. Hence, VIBRIS runs on token-based auth, meaning that no raw passwords are ever transmitted but only short-lived tokens. Data is provided stepwise, minimising data exposure.Each sensor reading is HEX encoded and compressed so that no spies ever get a chance to read the stream. Encryption coupled with endpoint obfuscation and multiple-layer validations will be the most heavily secured monitoring pipeline ever for renewables.Clean, trusted data improves the accuracy of the model. It is not just about cybersecurity. Where there is security, the performance shall follow.<h2>Lessons from real deployment</h2>Q: What have you learned from deploying VIBRIS on real turbines?Joshi: AI is not enough; you need domain expertise.Senior vibration analyst Christopher Harrison ensured AI-flagged anomalies were mechanically relevant; his expertise kept us from chasing phantoms.M&amp;D manager Andrew Riley pushed for fleet-wide scalability and integration to SCADA/PI systems; he consistently reminded us that an alert is only good if it culminates in real maintenance.Another lesson learned was correct calibration; if false alarms were generated in excess (say, coming from high winds), the crews merely ignored it; hence, the main thing was the management ofsensitivity and precision. The key lesson from all this was that for a predictive maintenance plan to work well, there has to be a perfect mix of AI and human judgment, trust, and scalability.<h2>Democratising Data in Renewables</h2>Q: Your organisation frequently talks about democratizing data. What does that term mean?Joshi: The idea is to make insights available not only to data scientists but also to engineers, accountants, or even to the lay public if that is at all possible. For VIBRIS, that meant intuitive dashboards and alerts that virtually anyone is capable of acting on.So breaking down those silos is also part of it. Sharing insights securely across teams-from one wind farm project-in fact, between wind farms worldwide-faster learning. Federated learning allows for turbines in different countries to contribute towards an AI model without giving away their own original data. So a failure pattern in a gearbox found in Texas may very well avert its occurrence in Denmark.The more inclusive and accessible the data is to everyone, the faster our innovation becomes.<h2>Beyond Wind: Expanding AI Predictive Maintenance</h2>Q: Can VIBRIS concept be extended to other renewable assets?&nbsp;Joshi: Yes, the approach of secure pipelines + ensemble anomaly detection port well to anywhere where there are rich sensor datasets. BESS and solar PV plants are definitely next on the VIBRIS radar. We can really improve VIBRIS to forecast inverter failures or battery degradation or anything from cell imbalances.Predictive capabilities in AI will adapt themselves instantaneously. Models will be incrementally tuned during their progression into old age, needing human intervention for adjustment only in unusual cases. Through federated learning, whatever is learned from a site will inherently help to enrich systems worldwide.Soon, predictive AI will become a backbone of the renewable industry, interfacing automatically with maintenance systems to facilitate spare part ordering and dispatching technicians and ensuring that downtime rarely happens on a routine basis.<h2>The Large Vision: Intelligent Infrastructure</h2>Q: How do you see AI and clean energy laying the foundation for another level of reality at an even larger scale?Joshi: I see a world where renewable energy is not only green but intelligent. Imagine winds, solar, batteries, and EV chargers all communicating through an AI-powered network to:Balance supply and demand in real time.Detect faults and reroute power immediately.Getting affected by an event or disasters and restoration of energy capabilities, to put it simply, is creating resilient grids.I call these intelligent, resilient energy systems that are mostly low carbon, operating reliably, and adaptive. AI will promote real-time optimisation, be it letting people know a storm's impact on a wind farm or routing solar power to the masses and emergencies of a city.Sustainable AI will be used to optimise storage, grid operations, and climate modelling. If done properly, transparently, and responsibly, it will serve to ensure the fastest possible pace in building a sustainable society-governed by more intelligent approaches and greener means coupled with equity.Key PointsIt carries much more than just technology paramount: Drumil Joshi's VIBRIS project holds the blueprint for how AI can intervene more and more into making renewable energy generation reliable, secure, and collaborative. With increasing investments being poured into renewable assets worldwide, systems like VIBRIS may become infrastructure for infrastructure itself-thinking that it generates power while contemplating it intelligently, resiliently, and sustainably on its own.

We spoke with Drumil about VIBRIS' origins, the technology behind it, and the bigger picture of AI in sustainable infrastructure.

Drumil Joshi is recognised as an expert in the field of AI for renewable energy. As an M&D Analyst at Southern Power Company, he holds an MS in Data Science and has patents, research papers, and a textbook to his credit. He prefers speaking at top conferences and has appeared on Authority Magazine and several other platforms. In 2025, Drumil Joshi and his team launched the VIBRIS project, one of the foundations for predictive maintenance for wind turbines. The full name of VIBRIS is Vibration Intelligence Bearing Reliability Integrated System next-generation condition monitoring platform. VIBRIS takes the data generated from the turbine sensors, saves it through tokenised pipelines and HEX-encoding, and passes it down to a powerful ensemble of anomaly detection algorithms (Isolation Forest, Local Outlier Factor, One-Class SVM, K-Means) for processing. The results at the turbine end are: early faults detected with precision, fewer false alarms, and transition from reactive turbine maintenance to proactive turbine maintenance. We spoke with Drumil about VIBRIS' origins, the technology behind it, and the bigger picture of AI in sustainable infrastructure. From idea to impact Q: What inspired VIBRIS? Drumil Joshi: An ideation from wanting AI for something meaningful. Most wind farms were operating in a reactive manner-maintain turbines after failures had already occurred. I wanted to look at the same thing from a different perspective-ahead of-time problem forecast, and avoid costly outages of turbines and hence unblocked renewable energy generation. I borrowed insights from finance and health care and had to rethink turbine monitoring via AI. For me, this is not about personal accolades; rather, failure prevention and cost minimisation with improved clean-energy reliability would embody the milestones of success. VIBRIS could stand for either very good technical prowess or bright purpose: smarter green operations. Why Did It Choose the Ensemble AI Approach? Q: The VIBRIS takes advantage of a four-model ensemble. Why not just one algorithm? Joshi: In a noisy turbine environment, no single model catches everything. Each does its own: Isolation Forest: very good at spotting extreme outliers, such as sudden vibrations in the readings. The Local Outlier Factor (LOF) method tries to detect slight local irregularities. One-Class SVM tries to learn the normal behaviour from history and then raises abnormalities. K-Means Clustering: cluster normal patterns outside the subsets of interest. All four methods are combined in tandem to produce the composite anomaly score, which help to reduce false alarms and at the real time catch real issues. Think of it as an ability to distinguish between an occasional gust of wind and an actual gearbox fault. Data Security First Q: Why do you stress the pipeline's security? Joshi: Energy data is sensitive data. Without good security in place, the whole operation would be a failure. Hence, VIBRIS runs on token-based auth, meaning that no raw passwords are ever transmitted but only short-lived tokens. Data is provided stepwise, minimising data exposure. Each sensor reading is HEX encoded and compressed so that no spies ever get a chance to read the stream. Encryption coupled with endpoint obfuscation and multiple-layer validations will be the most heavily secured monitoring pipeline ever for renewables. Clean, trusted data improves the accuracy of the model. It is not just about cybersecurity. Where there is security, the performance shall follow. Lessons from real deployment Q: What have you learned from deploying VIBRIS on real turbines? Joshi: AI is not enough; you need domain expertise. Senior vibration analyst Christopher Harrison ensured AI-flagged anomalies were mechanically relevant; his expertise kept us from chasing phantoms. M&D manager Andrew Riley pushed for fleet-wide scalability and integration to SCADA/PI systems; he consistently reminded us that an alert is only good if it culminates in real maintenance. Another lesson learned was correct calibration; if false alarms were generated in excess (say, coming from high winds), the crews merely ignored it; hence, the main thing was the management of sensitivity and precision. The key lesson from all this was that for a predictive maintenance plan to work well, there has to be a perfect mix of AI and human judgment, trust, and scalability. Democratising Data in Renewables Q: Your organisation frequently talks about democratizing data. What does that term mean? Joshi: The idea is to make insights available not only to data scientists but also to engineers, accountants, or even to the lay public if that is at all possible. For VIBRIS, that meant intuitive dashboards and alerts that virtually anyone is capable of acting on. So breaking down those silos is also part of it. Sharing insights securely across teams-from one wind farm project-in fact, between wind farms worldwide-faster learning. Federated learning allows for turbines in different countries to contribute towards an AI model without giving away their own original data. So a failure pattern in a gearbox found in Texas may very well avert its occurrence in Denmark. The more inclusive and accessible the data is to everyone, the faster our innovation becomes. Beyond Wind: Expanding AI Predictive Maintenance Q: Can VIBRIS concept be extended to other renewable assets? Joshi: Yes, the approach of secure pipelines + ensemble anomaly detection port well to anywhere where there are rich sensor datasets. BESS and solar PV plants are definitely next on the VIBRIS radar. We can really improve VIBRIS to forecast inverter failures or battery degradation or anything from cell imbalances. Predictive capabilities in AI will adapt themselves instantaneously. Models will be incrementally tuned during their progression into old age, needing human intervention for adjustment only in unusual cases. Through federated learning, whatever is learned from a site will inherently help to enrich systems worldwide. Soon, predictive AI will become a backbone of the renewable industry, interfacing automatically with maintenance systems to facilitate spare part ordering and dispatching technicians and ensuring that downtime rarely happens on a routine basis. The Large Vision: Intelligent Infrastructure Q: How do you see AI and clean energy laying the foundation for another level of reality at an even larger scale? Joshi: I see a world where renewable energy is not only green but intelligent. Imagine winds, solar, batteries, and EV chargers all communicating through an AI-powered network to: Balance supply and demand in real time. Detect faults and reroute power immediately. Getting affected by an event or disasters and restoration of energy capabilities, to put it simply, is creating resilient grids. I call these intelligent, resilient energy systems that are mostly low carbon, operating reliably, and adaptive. AI will promote real-time optimisation, be it letting people know a storm's impact on a wind farm or routing solar power to the masses and emergencies of a city. Sustainable AI will be used to optimise storage, grid operations, and climate modelling. If done properly, transparently, and responsibly, it will serve to ensure the fastest possible pace in building a sustainable society-governed by more intelligent approaches and greener means coupled with equity. Key Points It carries much more than just technology paramount: Drumil Joshi's VIBRIS project holds the blueprint for how AI can intervene more and more into making renewable energy generation reliable, secure, and collaborative. With increasing investments being poured into renewable assets worldwide, systems like VIBRIS may become infrastructure for infrastructure itself-thinking that it generates power while contemplating it intelligently, resiliently, and sustainably on its own.

We spoke with Drumil about VIBRIS' origins, the technology behind it, and the bigger picture of AI in sustainable infrastructure.

AI-driven renewable energy: Interview with Drumil Joshi on VIBRIS

Changes in the manufacturing and industrial world today are redefining traditional quality audits, which used to be perceived as reactive checklists. As the world becomes increasingly digitalised and goes through the Industry 4.0 revolution, organisations are currently striving to transform the audit system into a proactive data-based practice that can be able to forewarn and correct quality lapses at an early stage. Numerous industries realize that preventing defects before occurrence is a way of safeguarding brand image and reducing wastes and expenses. Analytically driven, machine learning and real-time data-powered predictive quality auditing are also making growing contributions currently to operational success.&nbsp;Krishna Chaitanya Mamidala, whose extensive experience in this field has not only made quality a back-end compliance activity, but a change to front-end performance, resilience, and innovation, is a forefront of such transformation. This is the same trend that is being experienced across the various industries, with companies seeking to integrate predictive tools into quality systems to enhance decision-making.&nbsp;The quality transformation leader, Krishna, has been reportedly leading a series of initiatives that have replaced former audits with futuristic and digital-driven audits. His input on the expert bench includes leading the way to the conversion of manuals to an integrated digital QMS where real-time tracking is possible, and audit preparation has reduced by 40%.&nbsp;Furthermore, he teamed up with data scientists to develop predictive dashboards which prevented 30 percent repeat non-conformity in one year. The thought of blending quality skills with data science is attaining fame across the planet. It assists manufacturers to react to trends before they turn into issues. One of the most substantial projects that Krishna led was the introduction of the real-time monitoring and control system on the quality with the help of the IoT-enabled device. &nbsp;&nbsp;Through this initiative; customer complaints were reduced by 50% and first-pass yield jumped by 82% to 91%. His work has also been influential in his organisation. His monitoring of the predictive KPIs with leadership dashboards is said to drive quality dialogues to the executive level. The transformation of quality audits into dynamic continuously operating systems has allowed spotting the problems before they occurred and has decreased the rework and scrap by 25%. The digital audit predictive risk scoring tool has eliminated 60% of emergency inspections, and increased supplier part quality by 15%. This is a dynamic initiative in the quality assurance of supply chains.&nbsp;Moreover, the digital audit leadership of the professional has proven successful in various prominent projects. The concept of digital twins is catching on in manufacturing as a method of predicting production risk and enhancing processes. Among the major projects, there was the development of a digital quality twin, which copies production risks and eliminates the occupancy of more than 300 audit hours per year. He was also the person that led the design of a centralized audit dashboard to monitor trends, root cause, and status of corrective action that enables leadership to make decisions in a timely manner. Such initiatives have reduced cost of rework and scrap of rework in the organisation by nearly 750,000 annually, and this illustrates the beneficial role of predictive audits in enhancing the bottom line.&nbsp;In the industry, change reluctance continues to be one of the impeding factors to switch to complete digital systems after starting with manual work. The specialist demonstrated the benefits of predictive auditing with the help of awareness programs and pilot projects, and received support from leaders and different teams. Data challenges were resolved through cooperating with IT to develop one data lake, making data consistency reach 95%. It resulted in providing solid insights. Remarkably, he also remedied gaps in skills of audit teams by applying individualised training plans, which led to an adoption rate of over eighty percent proficiency in half a year.&nbsp;Besides this work, he has contributed the cross-industry benchmarking activities that have helped in developing an evolution maturity model in predictive audit. Other industries activities that are being benchmarked are helping in the formulation of best practices as far as predictive quality is concerned. His work is also strengthened with membership in industry consortiums as well as industry thought leadership forums.&nbsp;When asked about the broader consequences of his volume, Krishna was eager to point out that audits had to become more intelligence-oriented rather than inspection-oriented. This represents an increasing desire on the part of the quality community that audits would be used as learning opportunities, rather than being seen as mere compliance audits. He explained that it is no longer the case that quality audits are seen as being simply a compliance exercise, but one of getting ahead of the curve, learning and preventing systemic risk. To this, he appended the fact that people make the digital quality journey in that; tools make the change, but it is the engaged and data-literate workforce that maintains it. Other new trends that he brought up included AI-powered risk scoring, audit robots through robotic process automation (RPA) and the use of digital twins to simulate audit scenarios virtually before putting them into production, something he believes is already in our future.&nbsp;In conclusion, quality audits are evolving to become less of an after-the-fact check procedure to becoming a smart, proactive system that aids in preventing issues before they arise. Firms are able to identify dangers early enough, eliminate wastages, and be efficient in the manner of their operations, using real-time information and superior tools. Future quality will be determined by continual monitoring, speed of taking necessary steps and the ability of teams to use data to make sustainable changes.

The transformation of quality audits into dynamic continuously operating systems has allowed spotting the problems before they occurred and has decreased the rework and scrap by 25%. 

Changes in the manufacturing and industrial world today are redefining traditional quality audits, which used to be perceived as reactive checklists. As the world becomes increasingly digitalised and goes through the Industry 4.0 revolution, organisations are currently striving to transform the audit system into a proactive data-based practice that can be able to forewarn and correct quality lapses at an early stage. Numerous industries realize that preventing defects before occurrence is a way of safeguarding brand image and reducing wastes and expenses. Analytically driven, machine learning and real-time data-powered predictive quality auditing are also making growing contributions currently to operational success. Krishna Chaitanya Mamidala, whose extensive experience in this field has not only made quality a back-end compliance activity, but a change to front-end performance, resilience, and innovation, is a forefront of such transformation. This is the same trend that is being experienced across the various industries, with companies seeking to integrate predictive tools into quality systems to enhance decision-making. The quality transformation leader, Krishna, has been reportedly leading a series of initiatives that have replaced former audits with futuristic and digital-driven audits. His input on the expert bench includes leading the way to the conversion of manuals to an integrated digital QMS where real-time tracking is possible, and audit preparation has reduced by 40%. Furthermore, he teamed up with data scientists to develop predictive dashboards which prevented 30 percent repeat non-conformity in one year. The thought of blending quality skills with data science is attaining fame across the planet. It assists manufacturers to react to trends before they turn into issues. One of the most substantial projects that Krishna led was the introduction of the real-time monitoring and control system on the quality with the help of the IoT-enabled device. Through this initiative; customer complaints were reduced by 50% and first-pass yield jumped by 82% to 91%. His work has also been influential in his organisation. His monitoring of the predictive KPIs with leadership dashboards is said to drive quality dialogues to the executive level. The transformation of quality audits into dynamic continuously operating systems has allowed spotting the problems before they occurred and has decreased the rework and scrap by 25%. The digital audit predictive risk scoring tool has eliminated 60% of emergency inspections, and increased supplier part quality by 15%. This is a dynamic initiative in the quality assurance of supply chains. Moreover, the digital audit leadership of the professional has proven successful in various prominent projects. The concept of digital twins is catching on in manufacturing as a method of predicting production risk and enhancing processes. Among the major projects, there was the development of a digital quality twin, which copies production risks and eliminates the occupancy of more than 300 audit hours per year. He was also the person that led the design of a centralized audit dashboard to monitor trends, root cause, and status of corrective action that enables leadership to make decisions in a timely manner. Such initiatives have reduced cost of rework and scrap of rework in the organisation by nearly 750,000 annually, and this illustrates the beneficial role of predictive audits in enhancing the bottom line. In the industry, change reluctance continues to be one of the impeding factors to switch to complete digital systems after starting with manual work. The specialist demonstrated the benefits of predictive auditing with the help of awareness programs and pilot projects, and received support from leaders and different teams. Data challenges were resolved through cooperating with IT to develop one data lake, making data consistency reach 95%. It resulted in providing solid insights. Remarkably, he also remedied gaps in skills of audit teams by applying individualised training plans, which led to an adoption rate of over eighty percent proficiency in half a year. Besides this work, he has contributed the cross-industry benchmarking activities that have helped in developing an evolution maturity model in predictive audit. Other industries activities that are being benchmarked are helping in the formulation of best practices as far as predictive quality is concerned. His work is also strengthened with membership in industry consortiums as well as industry thought leadership forums. When asked about the broader consequences of his volume, Krishna was eager to point out that audits had to become more intelligence-oriented rather than inspection-oriented. This represents an increasing desire on the part of the quality community that audits would be used as learning opportunities, rather than being seen as mere compliance audits. He explained that it is no longer the case that quality audits are seen as being simply a compliance exercise, but one of getting ahead of the curve, learning and preventing systemic risk. To this, he appended the fact that people make the digital quality journey in that; tools make the change, but it is the engaged and data-literate workforce that maintains it. Other new trends that he brought up included AI-powered risk scoring, audit robots through robotic process automation (RPA) and the use of digital twins to simulate audit scenarios virtually before putting them into production, something he believes is already in our future. In conclusion, quality audits are evolving to become less of an after-the-fact check procedure to becoming a smart, proactive system that aids in preventing issues before they arise. Firms are able to identify dangers early enough, eliminate wastages, and be efficient in the manner of their operations, using real-time information and superior tools. Future quality will be determined by continual monitoring, speed of taking necessary steps and the ability of teams to use data to make sustainable changes.

The transformation of quality audits into dynamic continuously operating systems has allowed spotting the problems before they occurred and has decreased the rework and scrap by 25%.&nbsp;

Quality audits reimagined: Moving from reactive checks to predictive prevention insights

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Zee News Rajasthan

Editor

Priyanka Gupta

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