Technologies That Improve Predictive Maintenance

America’s power grid is a 40-year-old boxer still trying to go 12 rounds, tired, bruised, and one bad hit away from collapse. NAES Corporation and Gecko Robotics just threw $100M into the ring to change that, with an option to push it to $250M. If they pull this off, AI and robotics won’t just be maintaining our #energyinfrastructure; they’ll be saving it. Let’s talk numbers, because the numbers don’t lie. 80% of U.S. #powerplants are older than the first MacBook. Maintenance backlogs top $200 billion. #Electricity demand is surging 16% in the next four years, and #AIdatacenters alone are set to increase #powerconsumption by 160% by 2030. Meanwhile, the workforce propping all this up? Retiring four times faster than it’s being replaced. That’s not a problem. That’s a national energy emergency. So, what’s the play? Jake Loosararian and his team at Gecko Robotics are bringing in an army of wall-crawling #robots, #drones, and #sensorfed AI. These machines aren’t just scanning infrastructure for fun; they’re feeding real-time diagnostics into Cantilever®, Gecko’s AI platform, predicting failures before they happen, doubling #assetlifespans, and optimizing #plantperformance without the guesswork and Hail Mary fixes. Meanwhile, Mark S. Dobler and NAES Corporation already oversee 65GW of U.S. #energyproduction. That’s an empire of power plants: #coal, #gas, #renewables, and #nuclear that needs more than duct tape and prayers to stay online. NAES has the scale, Gecko has the tech. Together, they’re betting that #predictivemaintenance powered by AI can make downtime a relic of the past. If this works, it’s not just about keeping old plants alive longer. It’s about rewriting the #energyeconomy. AI-driven maintenance could become the new gold standard across #nuclear, #wind, and #industrialmanufacturing. Power buyers, think Microsoft, Amazon Web Services (AWS), @Tesla, could demand “modernization premiums” in their contracts. #Gridreliability goes up. Costs go down. Everyone wins. But let’s be real. Big bets like this don’t come without risks. Data integration across coal, gas, and nuclear isn’t plug-and-play. Training an entire workforce to trust AI over their own instincts? That’s a cultural shift, not just a software update. And then there’s regulation, today, the White House backs this plan. Tomorrow? Who knows. Betting on government consistency is like betting on a coin flip. Still, this deal is bigger than just NAES and Gecko. This is a blueprint for AI-driven #industrialtransformation. Companies that crack the code first won’t just cut costs, they’ll control the new standard of #efficiency, #reliability, and #safety. The ones who wait? They’ll be stuck dealing with outages, billion-dollar maintenance overruns, and the slow death of infrastructure that couldn’t keep up. Gecko press release in comments 👇 #EnergyTech #DeepTech #AI #Robotics #RobotTech #RoboticAI #IoT #Modernization

🌀 From Predictive Models to Agentic AI — in Just a Few Hours I wanted to experience what it’s like to build an agentic pipeline firsthand. So I did. Use case? Predictive maintenance for wind turbines — minimizing downtime and maximizing efficiency. Here’s the flow I created in Dataiku: 🛠️ Agents in Action: Data Collector Agent → pulls live sensor data (temperature, vibration, performance). Data Processor Agent → cleans, formats, and normalizes the inputs. Predictive Model Agent → Deploys ML models to forecast failures (Offshore, Onshore Small, and Onshore large turbines). Maintenance Scheduler Agent → prioritizes turbine maintenance based on predicted risks. The result? A conversational interface powered by Agentic AI — One place. One entry point. One orchestration layer. And it was built in just a few hours, thanks to the reusable descriptive and predictive artifacts I already had in Dataiku. Here’s what I learned: ✅ Agents get complex fast ✅ Visibility, governance, and usability are critical ✅ If you can’t trust or trace your agents, you’re not scaling — you’re gambling 🔍 With Dataiku, building and debugging agents is possible and straightforward. 📣 Curious how this works in your industry? The Dataiku team will be talking about this stuff live, bring your questions https://lnkd.in/gJ-qJi8s #AgenticAI #PredictiveMaintenance #WindEnergy #DataScience #Dataiku #MLops #AIatScale #ConversationalAI

Here are some examples of Building Automation System (BAS) devices with embedded AI capabilities, illustrating the growing adoption of advanced technologies in the industry: Belimo Energy Valve Capabilities: Combines thermal energy metering, Delta-T management, and IoT functionality. Its embedded AI optimizes energy efficiency by analyzing real-time data, such as flow rates and temperatures, and adjusts valve operation to maintain peak performance. AI Functionality: Delta-T optimization, fault detection, and predictive maintenance alerts. Distech Controls Apex Series Controllers Capabilities: Supports advanced control for HVAC, lighting, and energy management. Integrated with machine learning algorithms to enhance control strategies dynamically. AI Functionality: Adaptive learning to optimize energy consumption and occupant comfort based on historical and real-time data. Honeywell JACE 9000 (Niagara Framework) Capabilities: Facilitates integration of multiple systems and protocols in BAS. When paired with Niagara Framework’s AI modules, it enables predictive analytics, energy forecasting, and anomaly detection. AI Functionality: Enhanced system diagnostics, energy optimization, and machine learning-based predictive controls. Niagara N5 Platform (Upcoming) Capabilities: Promises enhanced computational power for AI and machine learning tasks. The framework will include tools for deeper integration of AI-driven analytics and optimization capabilities. AI Functionality: AI-driven fault detection and diagnostics, dynamic energy modeling, and system optimization. Lynxspring Edge Products Capabilities: Edge devices like the Lynxspring Edge 534 focus on edge computing for BAS. Embedded AI supports real-time analytics and machine learning at the edge. AI Functionality: Localized decision-making for improved responsiveness, predictive maintenance, and reducing latency in system adjustments. Schneider Electric EcoStruxure Capabilities: A comprehensive platform with AI-driven controllers like SmartX AS and edge devices. Supports predictive analytics and adaptive control. AI Functionality: Energy optimization, adaptive algorithms for HVAC control, and integration with digital twin technology. Johnson Controls OpenBlue Capabilities: Combines AI with BAS hardware, such as Metasys controllers, to deliver AI-powered insights and proactive building management. AI Functionality: Occupancy-based control, predictive maintenance, and advanced energy management. Siemens Desigo PXC Controllers Capabilities: Part of the Siemens BAS portfolio, these controllers use AI algorithms to optimize building systems’ performance and efficiency. AI Functionality: Fault detection, predictive analytics, and real-time optimization. Trane Tracer SC+ and Symbio 800 Capabilities: Embedded AI allows these devices to adjust HVAC systems dynamically based on real-time building conditions and user preferences. AI Functionality: Predictive analytics for energy efficiency and occupant comfort.