AI In Predictive Maintenance

Discover Senseye Predictive Maintenance live from Transform 2024! Ryan Falcini walks us through the key elements of the Senseye Predictive Maintennace platform covering: ❓ What is Senseye?: Senseye is a cloud-based AI and machine learning tool designed to detect and alert users to potential machine failures and forecast breakdowns. It is industry-agnostic, supporting various sensors and technologies. ⚙️ Primary Use: Senseye acts as a decision support tool, guiding users on maintenance priorities through the Attention Index. This index uses a traffic light system (green, yellow, red) to indicate priority levels for asset issues. 👩🏻💻 User Interaction: Users receive detailed cases highlighting anomalies or trend detections, showing specific measures causing concern. Feedback from users helps fine-tune algorithms and improve the Senseye experience. 💻 Advanced Capabilities: Senseye employs generic AI to offer prescriptive guidance, beneficial for organizations lacking the expertise to interpret complex data. Language learning models provide actionable checklists to restore asset health. 🤓 Main Goal: The primary objective is delivering the right information to the right person at the right time, preventing unplanned downtime and reducing maintenance costs. #PredictiveMaintenance #Transform2024 #Industry40

Meet anyone in manufacturing, and for their top two concerns, you'll hear about:   1. Supply Chain Disruptions: Challenges related to inventory and supply chain management. 2. Operating Costs: Navigating economic headwinds and operational inefficiency.   Our clients in the manufacturing sector work in a fast-paced world where maintaining operational efficiency is crucial. One of our clients faced significant challenges with their Clean-In-Place (CIP) process, which directly impacted their quality check procedures. Frequent unplanned downtimes due to equipment failures were hampering productivity and throughput, highlighting the need for a more proactive maintenance approach. They needed real-time insights to make informed preventive maintenance decisions! To address their challenges, our team developed and implemented an AI-based predictive maintenance solution for the CIP equipment. Leveraging data analytics and machine learning, this solution integrated critical datasets from batch processes, sensors, and maintenance records.   By empowering our client with real-time insights through anomaly detection and a risk scoring system, we enabled them to make informed preventive maintenance decisions. This proactive approach not only improved their operational efficiency but also set a new standard for maintenance practices in the manufacturing industry.   Our client went from reactive and corrective maintenance to predictive maintenance! Would love to hear from the network on what you are seeing in this area. If you have a story, let us talk.

Here are my Top AI Mistakes over the course of my career - and guess what thebtakeawaybis - deploying AI doesn’t guarantee transformation. Sometimes it just guarantees disappointment—faster (if these common pitfalls aren’t avoided). Over the 200+ deployments I’ve done most don’t fail because of bad models. They fail because of invisible landmines—pitfalls that only show up after launch. Here they are 👇 🔹 Strategic Insights Get Lost in Translation Pitfall: AI surfaces insights—but no one trusts them, interprets them, or acts on them. Why: Workforce mistrust OR lack of translators who can bridge business and technical understanding. 🔹 Productivity Gets Slower, Not Faster Pitfall: AI adds steps, friction, and tool-switching to workflows. Why: You automated a task without redesigning the process. 🔹 Forecasting Goes From Bad → Biased Pitfall: AI models project confidently on flawed data. Why: Lack of historical labeling, bad quality, and no human feedback loop. 🔹 The Innovation Feels Generic, Not Differentiated Pitfall: You used the same foundation model as your competitor—without any fine-tuning. Why: Prompting ≠ Strategy. Models ≠ Moats. IP-driven data creates differentiation - this is why data security is so important, so you can use the important data. 🔹 Decision-Making Slows Down Pitfall: Endless validation loops between AI output and human oversight. Why: No authorization protocols. Everyone waits for consensus. 🔹 Customer Experience Gets Worse Pitfall: AI automates responses but kills nuance and empathy. Why: Too much optimization, not enough orchestration. 👇 Drop your biggest post-deployment pitfall below ( and it’s okay to admit them - promise) #AITransformation #AIDeployment #HumanCenteredAI #DigitalExecution #FutureOfWork #AILeadership #EnterpriseAI

AI projects are failing—not loudly, but quietly and often. Last week, I shared some learnings from AI initiatives we've run over the past couple of years. These were not theoretical ideas. These were real projects, built for real businesses, by real teams. Some succeeded. Some taught us what not to do. Warren Buffett: "The first rule is: don’t lose money." In the AI world, the first rule should be: don’t let the project fail. 🔁 1. Chasing AI without a real business problem This is the #1 reason AI projects fail. The excitement is real, but the clarity is missing. Too many initiatives start with, “We have to do something in AI. The Board/CEO wants it.” When you ask “Why?”—the answers get fuzzy. There’s often no alignment with a meaningful problem, no defined outcome, and no plan for business value. You must start with a sharp, urgent problem. Ask: - Is it real and recurring? - Is it costing us time, money, or customers? - Is solving it a priority for leadership? If the answer is lukewarm, drop it. Don’t chase hype—solve pain. 📉 2. No data, but big ambitions AI needs fuel—and that fuel is data. Most companies don’t even have decent dashboards, but they want AI to “think” for them. You can’t train models on instincts or opinions. AI needs history, decisions, edge cases, and volume. Before even thinking about AI, get your data stack in order: - Start capturing what matters. - Structure and cleaning it consistently. - Build visibility through dashboards. 🧠 3. Ignoring the role of context Even the best algorithms are clueless without context. What works in one scenario may totally fail in another. AI can’t figure that out on its own. Think of it like this: if I’m asked to speak at an event, I’ll want to know the audience, their challenges, the format—otherwise, I’ll miss the mark. AI is the same. Without business logic, edge conditions, and constraints, its outputs are generic at best, misleading at worst. ⚡ 4. Forgetting hidden and ongoing costs Many leaders assume AI is a one-time build. It’s not. Even after a model is trained, there’s hosting, fine-tuning, monitoring, guardrails, integrations, and more. And the infra isn’t free—especially if you’re using Gen AI APIs. Today, a lot of this cost is masked by subsidies from big players. But like every other tech cycle, the discounts won’t last. 🧭 So what should companies actually do? - Map where time and money are leaking internally. - Start capturing data in those areas—every day, every interaction. - Use dashboards and analytics before jumping to AI. - Identify where automation or decision support can create value. - Train your systems not just with data, but with your decision logic. And make sure AI is embedded where work happens—not in some separate tab. If your team needs to “go to ChatGPT”, they won’t. The AI has to come to them—right inside their workflows. 🚶♂️ Crawl → Walk → Run The hype will make you want to run. But strong AI systems are built the boring way.

In this post, I’ve outlined the various capacities AI couls assist a Process Safety expert: 1. AI as a Tool Use Case: Automated P&ID or PHA Report Extraction AI-powered document processing tools can scan and extract relevant data from Process & Instrumentation Diagrams (P&IDs) or old Process Hazard Analysis (PHA) reports to create tag lists, identify safeguards, or summarize historical recommendations. Example: Uploading 200 scanned PHA PDFs into an AI system to extract equipment tag references, risk rankings, and open action items. ⸻ 2. AI as an Assistant Use Case: Preparing Safety Audit Checklists and Reports An AI assistant can help safety engineers generate customized audit checklists based on process type (e.g., refinery vs. polymer plant) and regulatory framework (e.g., OSHA PSM vs. Seveso). It can also draft the report after the audit based on notes and photos. Example: Engineer inputs plant type and a few findings, and AI drafts a comprehensive audit report or a Management of Change (MOC) summary. ⸻ 3. AI as a Peer or Collaborator Use Case: HAZOP Session Support During a HAZOP meeting, AI acts as a real-time collaborator by suggesting additional deviations, identifying overlooked failure modes, or cross-referencing previous similar analyses. It enhances creative hazard brainstorming. Example: AI interjects: “In a similar plant, loss of reflux in the distillation column led to a runaway—should we analyze this scenario?” ⸻ 4. AI as an Independent Expert Use Case: Predictive Risk Assessment from Operating Data Trained on historical incident data and plant DCS trends, AI models predict potential failures (e.g., seal failures, overpressure scenarios) and recommend preventive actions—potentially before human operators notice issues. Example: AI alerts: “Based on pressure trend anomalies and past incidents, the likelihood of pump cavitation in Unit 3 exceeds threshold—recommend inspection.” #processsafety #automation #pha #AI

Near Misses Tell Us What Almost Happened. pSIFs Tell Us What Will. In safety management, leaders long relied on near miss reporting, what almost happened to learn, reflect, and prevent future harm. But near misses are lagging indicators by nature. They require something to go wrong first. It’s time for a shift. Imagine this: At a manufacturing site, a worker trips over an unsecured hose. Luckily, no injury occurs. If we are lucky again, that was observed and logged as a near miss. It’s reviewed a week later. Now imagine the same scenario, but the hazard is flagged in real time, 24/7, before anyone is even near it because it’s been identified as a potential Serious Injury or Fatality (pSIF) risk. That’s not just prevention. That’s transformation. 🔍 pSIF scores are the new frontier in safety—rooted in predictive modeling and hazard severity, not just frequency. They shift the focus from what might have been to what must not happen. At intenseye, we’ve built the first Real-Time Safety Management and SIF Prevention platform that detects hazards, predicts risk, and helps safety teams act before incidents unfold. Most AI safety solutions today aren’t real-time and their claims to “stop incidents” fall flat. Incidents happen in seconds. Expecting to prevent them with delayed data is like expecting an airbag to deploy a week after a crash. Predictive and proactive alone is an overpromise—safety must be real-time. ➡️ From retroactive reports… ✅ To real-time interventions. ➡️ From near miss logging… ✅ To pSIF-driven automated prioritization. The future of workplace safety isn’t reactive. It’s predictive, prioritized, and powered by AI. #WorkplaceSafety #SIFPrevention #EHS #pSIF #NearMiss #AI #SafetyTech #Intenseye

🌀 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

Condition Based Calibration & Calibration by Exception As Pharma companies continue to evaluate use cases for AI, I wanted to share an idea regarding equipment calibration and AI. Please ponder this concept and let me know your thoughts…. as absurd as it may sound. Will we ever get there? AI has the potential to significantly optimize and, in some cases, alter the traditional approach to scheduled equipment calibrations, but it is unlikely to completely remove the requirement for calibration. Here’s why and how it might change: Why Calibration is Required Regulatory Compliance: Industries such as pharmaceuticals, manufacturing, and aviation are governed by strict regulations (e.g., FDA, ISO standards). Calibration ensures traceability to a standard and compliance with these requirements. Accuracy and Precision: Calibration verifies that instruments and equipment perform accurately within specified tolerances, which is critical for safety, quality, and consistency. How AI Can Change Calibration Approaches Condition-Based Calibration (CBC): AI can analyze real-time performance data from sensors, equipment logs, and historical calibration trends to predict when calibration is actually needed, rather than relying on fixed schedules. Example: AI identifies drift patterns and determines that a device remains within tolerance longer than anticipated, reducing unnecessary calibrations. Automated Self-Calibration: Some modern equipment integrates self-calibrating mechanisms that AI can monitor and manage autonomously, minimizing human intervention. Example: High-precision scales in laboratories can adjust themselves, with AI overseeing the process to ensure alignment with external standards. Digital Twins: AI-driven digital twins can simulate equipment behavior and identify calibration needs based on virtual performance analysis. Example: A digital twin of a pressure sensor might show drift in performance, triggering calibration only when necessary. Optimization of Scheduling: By analyzing equipment usage patterns, environmental conditions, and operational factors, AI can create dynamic calibration schedules, reducing downtime and optimizing resources. Example: Equipment used less frequently might require calibration less often, while heavily used instruments might need more frequent checks. Regulatory Integration: AI systems can be validated and documented to meet regulatory requirements, ensuring that condition-based or automated calibration methods comply with industry standards. AI is unlikely to entirely remove the need for equipment calibration but can shift the paradigm from rigid schedules to data-driven, dynamic strategies. This can lead to cost savings, reduced downtime, and improved compliance while maintaining the required accuracy and reliability. However, validation, robust data management, and regulatory acceptance are key factors for widespread adoption.

💥 Agentic AI Unleashes the Green Revolution: How Generative Workflows Will Power the Energy Sector Generative AI is no longer just about creating stunning images or crafting compelling text. A new paradigm can alter energy industry - generative AI agentic workflows. Imagine AI not just as a tool for analysis or content creation, but as an autonomous agent, capable of generating solutions, orchestrating actions, and driving complex processes from end-to-end. Nowhere is this transformative potential more profound than in the energy sector, a domain crying out for innovation and efficiency. ✅ What exactly are these agentic workflows? They combine the creative power of generative AI with the proactive execution of intelligent agents. Think of it as AI that can not only imagine optimal energy solutions but also autonomously implement them. These workflows are designed to handle complex, multi-step processes, learn from experience, and adapt to dynamic environments, pushing automation beyond simple rule-based systems ✅ Why is this a game-changer for energy? Because the energy sector faces immense challenges: meeting growing demand, transitioning to renewables, optimizing vast and complex grids, and the like. Generative AI agentic workflows offer a powerful toolkit to tackle these head-on. Let's dive into specific examples of how this will unfold: 🛫 Hyper-Personalized Energy Savings Agents for Consumers: Forget generic energy-saving tips. Agentic AI can analyze a household’s specific energy consumption patterns, appliance usage, and even lifestyle habits. Based on this deep dive, it generates truly personalized energy-saving recommendations – and crucially, it can autonomously implement them. Imagine an AI agent that learns your preferred home temperature, analyzes energy pricing fluctuations, and then subtly adjusts your smart thermostat and appliance schedules to minimize your bill without impacting comfort. 🛫 Predictive Maintenance Agents for Energy Infrastructure: Power plants, wind turbines, and pipelines require constant maintenance to prevent costly failures. Agentic AI can continuously monitor sensor data from these assets, generate predictive maintenance schedules based on subtle anomaly detection, and even autonomously trigger maintenance workflows. This minimizes downtime, extends asset lifespan, and improves the overall reliability of energy infrastructure. ☑️ The implications are staggering: a more resilient, efficient, and sustainable energy sector, powered by AI agents working autonomously to optimize every facet of energy generation, distribution, and consumption. While challenges like data security, ethical considerations, and job displacement need careful consideration, the potential of generative AI agentic workflows to drive a transformation in the energy sector is undeniable. #slb #ai #genAI #energy #tech

The biggest risks in workplace safety aren’t always the obvious ones. Data shows that while TRIR has declined from 5.0 in 2003 to 2.8 in 2018, the rate of reduction slowed in recent years. Have you wondered why serious injuries and fatalities (SIFs) haven’t followed the same trend? If you've hit that plateau and you’re not yet adding AI tools with your existing safety management processes, you may be missing hidden SIF precursors that lead to life-threatening incidents. If you’re stuck on how AI tools can help you, or need help getting others on board at your organization, here are some ideas: 💡 Root cause analysis: go beyond checklists and detect real patterns behind incidents. 💡 Real-time hazard detection: identify risks before they escalate into injuries, improve response times by the right staff to remedy the conditions. 💡 Predictive analytics for proactive safety: AI doesn’t just collect data, it forecasts risks so you can focus your time and effort on implementing prevention measures and activities. You don’t have to start fresh with an AI tool to collect and analyze your data, you can plug your historical data into an AI platform to identify patterns of the past that can inform the present. Safety pros have to stay ahead, and with the volume of data available to us for analysis and prediction, AI is our present and future to impact SIF prevention. Read more about how to move beyond your safety data plateau from Sercan Esen at intenseye:  https://lnkd.in/gSz5HXPY