IoT Platform with Databricks Intelligence Data Platform - Ingesting real-time Industrial Sensor Data for Prescriptive Maintenance

What is The Databricks Intelligence Data Platform for IoT & Manufacturing?

The Databricks Data Intelligence Platform for Manufacturing unlocks the full value of manufacturing data, enabling intelligent networks, enhanced customer experiences, smarter products, and sustainable businesses. It empowers data teams with unmatched scalability, real-time insights, and innovative capabilities across all data types and sources. Manufacturers benefit from reduced costs, increased productivity, improved customer responsiveness, and accelerated innovation. The platform integrates diverse data sources with top-tier AI processing and offers manufacturing-specific Solution Accelerators and partners for powerful real-time decision-making.


**Intelligent**
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Wind Turbine Prescriptive Maintenance with the Databricks Intelligence Data Platform: Bringing Generative AI to Predictive Maintenance

Being able to collect and centralize industrial equipment information in real time is critical in the energy space. When a wind turbine is down, it is not generating power which leads to poor customer service and lost revenue. Data is the key to unlock critical capabilities such as energy optimization, anomaly detection, and/or predictive maintenance. The rapid rise of Generative AI provides the opportunity to revolutionize maintenance by not only predicting when equipment is likely to fail, but also generating prescriptive maintenance actions to prevent failurs before they arise and optimize equipment performance. This enables a shift from predictive to prescriptive maintenance.




Prescriptive maintenance examples include:

- Analyzing equipment IoT sensor data in real time
- Predict mechanical failure in an energy pipeline
- Diagnose root causes for predicted failure and generate prescriptive actions
- Detect abnormal behavior in a production line
- Optimize supply chain of parts and staging for scheduled maintenance and repairs

What we'll build

In this demo, we'll build an end-to-end IoT platform to collect real-time data from multiple sources.

We'll create a predictive model to forecast wind turbine failures and use it to generate maintenance work orders, reducing downtime and increasing Overall Equipment Effectiveness (OEE).

Additionally, we'll develop a dashboard for the Turbine Maintenance team to monitor turbines, identify those at risk, and review maintenance work orders, ensuring we meet our productivity goals.

At a very high level, this is the flow we will implement:



1. Ingest and create our IoT database and tables which are easily queriable via SQL.
2. Secure data and grant read access to the Data Analyst and Data Science teams.
3. Run BI queries to analyze existing failures.
4. Build ML model to monitor our wind turbine farm & trigger predictive maintenance operations.
5. Generate maintenance work orders for field service engineers utilizing Generative AI.

Being able to predict which wind turbine will potentially fail is only the first step to increase our wind turbine farm efficiency. Once we're able to build a model predicting potential maintenance, we can dynamically adapt our spare part stock, generate work orders for field service engineers and even automatically dispatch maintenance team with the proper equipment.

Our dataset

To simplify this demo, we'll consider that an external system is periodically sending data into our blob storage (S3/ADLS/GCS):

- Turbine data *(location, model, identifier etc)*
- Wind turbine sensors, every sec *(energy produced, vibration, typically in streaming)*
- Turbine status over time, labelled by our analyst team, and historical maintenance reports *(historical data to train on model on and to index into vector database)*

*Note that at a technical level, our data could come from any source. Databricks can ingest data from any system (SalesForce, Fivetran, queuing message like kafka, blob storage, SQL & NoSQL databases...).*

Let's see how this data can be used within the Data Intelligence Platform to analyze sensor data, trigger predictive maintenance and generate work orders.