Prescient

What if you could predict equipment failures months in advance? Today, most Condition-Based Maintenance (CBM) solutions predict equipment failures hours or days in advance. While this is great, it is still somewhat reactive. We've developed an Asset Life Model that predicts equipment failures months in advance. You can think of it as a data-driven fatigue model. The model takes into account the cumulative fatigue on the equipment based on its operating conditions, across it's life time, so that it can make equipment or component life predictions from the day they are put into service. What's the implication of this? You can anticipate maintenance needs months in advance, and you have full supply chain predictability. What did it take to build and run this model? It took about 600-Billion data points to train, and at scale, it monitors over 7,000 equipment components and processes 6-Trillion data points per day for inference. What amazing things we can do today:) https://lnkd.in/einkfipR

Scaling digital twins can feel overwhelming. Complex systems, scattered data sources, and resource-intensive engineering often slow progress. But what if there were a better way? Precision Drilling’s digital twin adoption journey shows that building and scaling #digitaltwin solution doesn’t have to be a drawn-out process. Their team deployed a highly advanced digital twin across 101 rigs in just over a year — enabling real-time insights into asset lifecycles and improving operational efficiency. Here’s how they made it happen: ?? Rapid Iteration: Starting with a blank slate in January 2023, they focused on a single asset class (mud pumps) and delivered the first version in just two months. ??User-Centric Development: Weekly feedback loops with field teams drove continuous improvement, with new features deployed within days. This built trust and encouraged adoption from the ground up. ??Massive Data Processing: The system handled 25,000 high-speed data tags daily, translating to 2.1 billion #data points processed every day across #rigs. ??95% Less Engineering Time: Automation streamlined processes, enabling rapid development and reducing resource strain. By prioritizing iterative development, automation, and collaboration, Precision team scaled their digital twin organically, delivering measurable outcomes like reduced downtime and cost savings. Watch how they did it: https://rb.gy/42smjq

Predictive maintenance is just the start. Sensors and AI can warn you before failures, but real asset productivity goes beyond avoiding downtime. The real question: How do you continuously improve performance rather than just react to failures? Equipment data from vibration sensors, current logs, and maintenance records often remain siloed. The key is to connect real-time machine data with historical insights to optimize asset use, refine replacement cycles, and drive smarter decisions. Leading companies don’t just rely on #AI alerts, they train models with field feedback, label failure events, and refine predictions, creating a system that gets smarter over time. The result? Higher reliability, lower costs, and more efficient operations. Is your asset strategy just predicting failures, or is it improving performance?

Thank you MIT Startup Exchange for featuring our partnership with Precision Drilling! We are proud to lead the industry with our asset health digital twin solution with visionary partners such as Precision.

Acquiring raw data is easy. Turning it into knowledge is hard. Oil rigs, whether onshore or offshore, generate billions of data points daily, but only a fraction of this #data is actionable. ? Event data points get lost in the noise ? Historical data disappears over time What’s the fix? ?? Automated event detection – Identify anomalies in real-time before they become costly failures ?? Context-rich metadata – Store operational insights so your system learns from past events ?? Scalable data pipelines – High-frequency event data processing (24kHz sensor rates) We help turn scattered, messy industrial data into a long-term asset, ensuring your knowledge compounds instead of getting lost. Watch now to learn how: https://lnkd.in/dUuQaRXA #predictivemaintenance #oilandgas

How can businesses build large-scale, data-powered solutions 20x faster? On the field, raw data from machines, sensors, and software APIs is just noise until it’s processed and contextualized. That’s where Edge Data Pipelines (a distributed functional block programming technology backed solution) come in. They bridge the gap between unstructured edge data and real-time, consumable insights, making data solutions like #digitaltwin faster and more scalable. Capabilities of an efficient edge data pipeline: ? Reliability & Security – Cleans, validates, and contextualizes data before it reaches the cloud ? Scalability – Processes high-speed, high-volume data across thousands of sites without bottlenecks ? Flexibility – Supports multiple data formats, protocols, and event extraction, reducing redundant data flow From predicting failures with vibration data to analyzing KPIs across industrial sites, edge data pipelines power digital twins, AI models, and real-time decision-making. Know how: https://lnkd.in/dYMu2N-B #oilandgas #bigdata

Scaling an edge data pipeline across hundreds of sites involves intricate data handling and error management. This article dissects a real-world case example, highlighting how deep expertise can turn years of work into months. It covers the answer to the question: "If it only takes a few hours to get data from a Raspberry Pi to the cloud, why does it take so long to get to production?" Link to the article: https://rb.gy/g7giy2 #daas #energy #manufacturing #plc

We help businesses improve equipment health, retain domain expertise, and optimize supply chains with #digitaltwin technology. By leveraging digital twins, companies can manage volatility and complexity with a static workforce, optimize equipment performance and interaction, and enhance process flow and the movement of people, machines, and products within the warehouse space. Implementing them enables near real-time simulation, allowing companies to adjust dynamically to real-time floor conditions, making operations more efficient. Know more: https://rb.gy/4l609s #equipmenthealth #supplychain #drilling #energy

Your #ML model is only as good as the #data feeding it. Most teams track model accuracy, but what about data freshness, completeness, and latency? Without real-time monitoring, even the best-trained ML models degrade quickly. With our help, you can: ? Track inference pipelines in real-time and pinpoint where data issues occur. ? Monitor external pipeline portions to gain visibility beyond in-platform workflows. ? Refresh models automatically, eliminating outdated #AI making poor decisions. ML Flow integrations and containerized environments make this scalable, even for large models. See how it works: https://rb.gy/t41fkk

The backbone of any digital twin project? Data. But when that data comes from thousands of sensors across global sites, managing it becomes challenging. For Bosch, building a #digitaltwin meant overcoming the challenge of handling high-speed, high-volume sensor data with precision. With the help of our data infrastructure (data pipeline), which automatically detects and configures IO-Link sensors, masters, and edge gateways, Bosch achieved: - Real-time Data Processing: Supported 24kHz data rates to ensure clean, actionable insights for analytics. - Seamless Sensor Integration: Unified diverse devices, from vibration sensors to edge gateways, into a streamlined system. - Global Scalability: Automation enabled the deployment of digital twins across thousands of sites without delays. Thus, establishing a scalable and resilient foundation for their digital operations. Curious how data pipelines can help you achieve the same? Find out: https://lnkd.in/dYMu2N-B #bigdata #operationalexcellence