Use Case Renewables in Maximo

Renewable energy is reshaping the world, and wind parks are at the forefront of this transformation. In this blog, I’ll dive into a practical use case: the setup, maintenance, and operations of a wind park featuring five wind turbines (7 MW each), a related substation, and how IBM Maximo Application Suite provides the foundation for effective management and monitoring.

Starting with the Basics: The Asset Register

Every great maintenance strategy starts with a solid foundation—an asset register. Think of it as a detailed blueprint of all the components that make up your wind park. For this use case, the asset register will logically break down the wind turbines and the substation into manageable parts, including:

• Wind turbines: blades, nacelle, generator, gearbox, tower, and base.
• Substation: transformers, switchgear, control systems, and protective relays.

This structured approach is essential for tracking, maintaining, and optimizing the performance of each component.

Creating a Maintenance Strategy

Wind turbines face unique challenges: high winds, extreme weather, and the wear-and-tear of continuous operation. To ensure maximum reliability and performance, a risk-based maintenance strategy is critical. Here's how the IBM Maximo Application Suite comes into play:

1. Maximo Manage: Establishes a robust preventive and corrective maintenance regime, ensuring every asset gets the attention it needs, when it needs it.
2. Maximo Health: Monitors the condition of assets in real time, providing insights into potential issues before they escalate.
3. Maximo Monitor: Keeps an eye on performance metrics, identifying trends that could indicate future problems.

The focus here is on reducing risks while optimizing operability and efficiency. With a combination of Condition-Based Maintenance (CBM) and Risk-Based Inspection (RBI), the system helps prioritize tasks based on asset health and criticality.

Integrating IT/OT Data

A key aspect of managing a wind park is integrating IT (Information Technology) and OT (Operational Technology)data. By connecting edge devices, SCADA systems, and sensors with IBM Maximo, you can create a seamless flow of information. This enables:

• Real-time updates on asset status.
• Predictive insights using historical and live data.
• Automated alerts and workflows for faster response times.

Why IBM Maximo?

IBM Maximo Application Suite is designed to handle complex operations like those in a wind park. With its modular approach, you can scale and adapt as your needs evolve. Here's how the suite covers this use case:

• Maximo Manage ensures that all maintenance activities are planned, tracked, and executed efficiently.
• Maximo Health provides a clear picture of asset condition, enabling data-driven decision-making.
• Maximo Monitor keeps the entire system running smoothly by identifying anomalies and trends.

A Look Ahead

This use case showcases the potential of combining smart software with renewable energy operations. With the right tools and strategy, managing a wind park becomes a streamlined process that prioritizes sustainability, reliability, and performance. Whether it’s ensuring the turbines stay operational through harsh weather, or optimizing the substation’s performance, IBM Maximo Application Suite proves to be a game-changer for asset management in renewable energy.

The following chapters are based on the ZNAPZ Business Capability Model framework as well as on the ZNAPZ Asset Management Maturity Framework. More information about these model can be read in previous blogs I have published.

Know your Assets

There are a bunch of wind turbine setups that could be explored and compared based on current setups, as well as future improved setups. I uses a practical decomposition model that is officially published by reseachgate ((PDF) Reliability and Preventive Maintenance

The Use Case Location Setup

I created a "virtual" wind park (inspired by a real one near my neighborhood) and named it "Nieuwegein." It features five wind turbines, each with a 7 MW capacity, and one substation. With this setup, I’ve covered the three main asset types:

• Wind Park
• Wind Turbine Generators
• Substation

The substation is within the wind park area, while the five turbines are spread out at different locations.

To bring this into IBM Maximo Manage, I set up a logical location system: the wind park is the main location, with the substation as one sub-location and the five turbines as additional sub-locations. Simple and organized! Here’s how it looks in IBM Maximo Manage:

The Use Case Asset Register Setup

Figure 1 breaks down the whole system hierarchy used to build the reliability model. Here’s a quick rundown of the main parts and how they work:

• Rotor Module: This has a hydraulic pitch system that adjusts the blade positions to match the wind direction for better efficiency. Fun fact: it also doubles as the turbine's primary braking system.
• Drive Train Module: This is where the wind's force and torque are passed from the rotor to the main shaft. It uses a gearbox with a cool setup: one planetary stage followed by two parallel stages, plus a mechanical brake for extra control.
• Nacelle Module: This includes four electric yaw gears with motor brakes. The yaw system is what rotates the top part of the nacelle to face the wind, helping boost power output while keeping the loads manageable.
• Power Module: For this setup, a 10 MW doubly fed induction generator (DFIG) does the job.
• Converter: There’s a converter hooked up between the generator and the grid. It’s a four-quadrant converter using an insulated-gate bipolar transistor (IGBT) on the generator side. Oh, and there’s an active crowbar unit on that side to make sure everything stays compliant.

Based on this logical drill down, I have created the asset decomposition for 5 wind turbine generators as well as an asset decomposition for a sub station. Since I have used an landslide (onshore) situation, the sub station and wind turbine hierarchy may defer from an offshore situation:

Know what to do

Here’s a practical breakdown of how to set up a time- and meter-based maintenance strategy, using three levels of maturity based on RCM2 principles:

1. FBM (Failure-Based Maintenance) This is the "fix it when it breaks" approach. It’s as basic as it sounds—wait for something to fail and then repair it. It’s reactive and doesn’t require much setup, but you’ll deal with more downtime and unplanned disruptions. Think of it as firefighting.
2. TBI (Time-Based Inspections) This is a step up. Instead of waiting for things to break, you schedule regular checks to assess asset condition. These inspections are usually manual, relying on the “HSoT” approach—the “Human Sensor of Things.” It’s all about using your team’s eyes and hands to catch issues early. It’s not to be confused with CBM (Condition-Based Maintenance), where sensor data provides real-time monitoring through SCADA or Edge devices. With TBI, if an issue or anomaly is found during an inspection, you either fix it right away or plan a repair within a reasonable timeframe. This approach reduces unexpected failures without needing high-tech systems.
3. UBM (Usage-Based Maintenance) Here, maintenance is driven by how much an asset is actually used. Instead of just relying on a calendar or periodic checks, you tie maintenance to usage metrics, like operating hours or production cycles. This is especially useful for equipment that wears out based on activity levels rather than time alone.

How it all ties together:

• Start with FBM if you’re just getting your maintenance processes in place.
• Move to TBI once you’ve got the basics down, using regular inspections to stay ahead of failures.
• Upgrade to UBM when you’re ready to fine-tune your strategy based on actual asset usage.

As you level up, you’ll move from reactive to proactive, cutting downtime, extending asset life, and boosting efficiency. The key is to pick the level that fits where you are now and build from there!

Here’s a quick breakdown of the three maintenance levels used in this use case (there are more, I know! just KISS it):

Level 1: Reactive Maintenance (Ad-Hoc)

• What you need: Basic asset info and a history of failures and repairs.
• How it works: Fix things when they break.
• What you get: You can react quickly to failures, track issues in IBM Maximo, and start spotting recurring problems—but expect frequent disruptions.

Level 2: Planned Maintenance (Managed)

• What you need: Asset hierarchy, criticality info, and simple maintenance schedules.
• How it works: Plan maintenance based on how critical assets are and their usage.
• What you get: Less downtime, better scheduling, and smoother operations. Maximo helps organize calendars and routine checks before things go wrong.

Level 3: Preventive Maintenance (Standardized)

• What you need: Detailed asset condition data, performance metrics, and maintenance history.
• How it works: Use data to predict when maintenance is needed and schedule it proactively.
• What you get: Longer asset life, fewer surprise failures, and more efficient operations. Maximo’s advanced tools make it easier to stay ahead of the game.

Start where you are and level up as your maintenance strategy evolves!

The Use Case Reactive Maintenance

Now that we’ve set up a solid location and asset register, we can easily create unplanned or corrective actions for any asset or location. I’m not going to dive into the entire work management process here, but I’ll share a few handy tips to help you streamline and even automate your reactive workflow. Here’s the scoop:

• Keep it simple with a basic failure class and code structure. Link it to the top 5 failure modes (called problems in core Maximo), the top 5 root causes, and a few straightforward remedies (like Repair, Replace, or Invest). Don’t overcomplicate it—start with the 5–10 most common issues. Nobody wants to scroll through 100+ failure modes or 1,000+ root causes. Trust me, your technician will just pick code 99: “Other.”
• Remember Pareto’s principle: around 20% of the causes typically lead to 80% of the issues. Use that to focus on the most impactful problems first.
• Make it part of your workflow to require a failure report for any corrective maintenance. It’ll keep things organized and help build a stronger data foundation.

Start simple and scale up as you go—it’s all about progress, not perfection!

Here’s how to get started with setting up a basic failure coding library in IBM Maximo:

1. Set Up a Failure Coding Library: Build a system that includes Problem, Cause, and Remedy codes. Keep it simple and relevant. Attach the right failure class to the corresponding assets—it’s the foundation of smarter maintenance.
2. Use Case Steps to Follow in Maximo Manage:
3. Why This Matters: Historical failure data is gold. It helps you refine your maintenance strategy and make better decisions down the road. Plus, if you ever use Maximo Predict and its AI/ML tools, this data becomes the backbone of smarter, predictive maintenance.

Start simple, keep it practical, and let the data do the heavy lifting as you grow!

The Use Case Basic (time-/ usage based) Preventive Maintenance

For the windmill use case, I implemented a combined time-based and usage-based preventive maintenance approach, often referred to as the "dealership" principle. In this model, the time-based frequency sets the legal limit for when maintenance must be performed, while the usage-based frequency defines the operational limit. If the asset has higher operational usage, a work order is generated based on the actual usage. Conversely, if the asset is hardly used or has minimal operational hours, the time limit will trigger the creation of a work order. The setup for Master PMs and Asset Templates is not detailed here, but you can find comprehensive instructions and explanations on Maximo Secrets (thanks to Andrew Jeffery).

For the creation of Preventive Maintenance Schedules, I followed a default flow, using Master PMs within asset class-specific templates.

For this use case, the maintenance regime is straight forward, so for every critical asset a PM schedule is created:

The Use Case Using Condition Monitoring in Manage

For example, you can create a set of critical condition-based measure points for the windmill turbines at the top level (though measure points can be linked to any level in the asset hierarchy). For the critical meters, create a meter group and attach it to the corresponding asset template for the turbines. This feature is really handy because it saves you from having to manually add all meters or meter groups for each asset. I even asked the IBM Product Development team to automate the creation of measure points within the asset template functionality, since it’s still a manual process that takes a lot of effort unless you use the data loader or MIF. Personally, I used our Xavier Intelligent Data Loader tool to load all the measure points for the critical assets in this use case—mostly because I'm lazy!

Don’t forget to enable the measure point cron task to automate the creation of work orders once an upper or lower limit measurements exceeds the limit.

Know your Asset Health

After all essential data is implemented and completed (level 1 and 2 of the maturity roadmap), we can continue with getting insights about the overall health and condition of the most critical assets. Based on experience the golden tip is NOT to zoom in too deep and focus on the top 5 most critical assets or asset classes (like Mr. Pareto said in the early years). In this use case we focus only on the wind turbines itself (however the real use case is going pretty much into detail regarding underlying assets, equipments and critical spare parts, which all is aggregated to the top level asset, but that would be too much to write down in this article).

Health or Monitor? That’s the question!

We’ve found that there's often some confusion about where to begin: “Should we start with Health Insights and then add an IoT framework and IBM Maximo Monitor, or should we jump straight into IoT and Monitor and add Health later?” Honestly, there’s no one-size-fits-all answer. The key is to have a clear understanding of your current maturity level and a sense of what’s realistic. It’s not like building a roof without a proper foundation—no matter how nice it looks, it won’t deliver the desired business value.

In this article, we’ve used a real-world example from one of our clients to illustrate the approach. This client was still in the early stages of exploring the IoT world with a kind of “pre-pilot” project, but they already had a solid asset register in place, complete with a lot of historical measurement and work order data (like CM, PM, and measure points). Since this was a strong foundation, the decision was made to start with Health Insights, using the existing historical data set.

Here’s a step-by-step guide to setting up a Health-related use case in a clear and structured way:

1. Create a Query for Assets

• Select Asset Types: Use the Asset Type selector to pick the types you want to display on the Health Dashboard. You can choose one or multiple types based on your needs.
• Select Site(s): If needed, filter by one or more specific sites.
• Select Failure Classes: You can choose particular failure classes to further narrow your asset list.
• Use Saved Queries: Alternatively, you can select a query you’ve already saved in the Asset Application in Manage.

2. Set Up Health Scoring

• Create Score Types: These can be based on factors like asset condition, regulations, measurable content, risk, criticality, and other key business aspects. Choose the ones that make the most sense for your scenario.
• Create Contributors: Contributors are linked to meters or measurable characteristics that impact the health of the asset. Make sure to associate them properly.

Create a Health Group

• Define Group: Based on the selections made in Step 1, create a group. It’s best practice to use a predefined or previously created filter query from the Asset Application in Manage.
• Name the Group: Give the group a clear name and description that aligns with its purpose.
• Select the Query: Choose the relevant query that defines the group.
• Add Score Types: Add the score type(s) you’ve defined in the Score Types section.

Assign Contributors to Score Types

• Add Contributors: Ensure that at least one contributor is linked to each score type. You can add more contributors or even create hierarchies if needed.

Define Score Impact: Each contributor and its score are weighted by a percentage, which determines their impact on the total score. The total score across contributors must add up to 100%

5. Activate Scores

• Activate at Least One Score: To see measurable results, you must activate at least one score type. Without activation, the scores won’t generate the necessary outputs.

Adjust the List view

• Add new columns: In the list view you can add columns with the information based on the score setup. When new score types are created, these types become available as an attribute in the column list.
• Drag and drop column: This handy .featurs enable you to swipe columns in the list and place then to any appropriate logical view. If the column attributes are set up as filterable (search method in de Database Configurator) each column is filterable

Once you've completed these steps, you’ll have a functional Health-related use case that provides valuable insights based on the scores and contributors you’ve defined!

Asset Health Insight Details

Let’s view the Asset Health Details. This can be easily done by clicking one of the assets in the Asset List. There is a search bar in the banner where you can select the desired predefined view (query based)

1. KPI Tiles

• Visualizes actual scores and relevant lifecycle information, offering a snapshot of the asset's overall performance and health at a glance.

2. Score Details

• Provides a detailed timeline view of the most critical score aspects, giving you a clear understanding of the asset's health trends over time.

3. Asset Timeline

• Displays a graphical overview of all maintenance actions, both historical and planned/expected in the near future. This helps in tracking the asset's maintenance journey.

4. Operational Status

• Shows the asset-related meters and measurement history, providing insights into operational performance and how the asset is being used.

5. Maintenance History

• Offers an overview of all historical work orders executed on the asset, helping you assess the maintenance efforts and trends over time.

6. Replacement Planning

• Provides an overview of future replacement or refurbishment action plans, helping you plan ahead for asset lifecycle management and avoid unexpected failures.

Each of these elements plays a critical role in monitoring, managing, and optimizing asset health and performance throughout its lifecycle.

Map View

In Maximo Health there is also a Map View available to show the asset health for all assets that are geographically spread.

Risk Matrix

The last view in this use case is the Risk Matrix where risks can be displayed against different information axes (an example from another use case with more assets):

Summary Part 1

This use case approach for managing wind park assets with IBM Maximo offers numerous benefits for clients in the renewable energy sector. By combining a structured asset register, a robust maintenance strategy, and real-time health monitoring, clients can significantly enhance operational efficiency, reduce unexpected downtime, and extend asset lifespan.

The use of IBM Maximo's suite, including Maximo Manage, Maximo Health, and Maximo Monitor, enables seamless integration of data from both IT and OT systems, providing actionable insights for predictive maintenance. By applying Condition-Based and Risk-Based maintenance strategies, businesses can proactively address potential issues before they escalate, ensuring that critical assets like wind turbines are always running at peak performance.

Furthermore, with the flexibility to scale from reactive to preventive maintenance, this approach allows clients to start from a foundational level and evolve their strategy as they mature. The ability to automate workflows, integrate with IoT devices, and generate predictive insights from historical data offers a clear path to smarter, more efficient operations.

In summary, this use case provides a comprehensive, scalable solution that aligns with the unique demands of the renewable energy industry. It empowers clients to optimize maintenance, improve asset health, and reduce costs—all while maximizing the potential of their wind park assets.