Predictive analytics can help industrial organizations improve the way they monitor equipment, identify developing problems, and make maintenance decisions. But what happens when your organization needs to replace or upgrade its existing predictive maintenance software?
Organizations typically consider a change for a few reasons. The current platform may no longer be supported or may lack the capabilities newer technology offers. Operational needs may have shifted as equipment, processes, or plant priorities evolve. Or the existing solution simply may not be delivering the insight or usability the organization needs. Whatever the reason, the challenge is the same: how do you move to a new system without losing the value already built into the old one?
The thought of migrating to a new predictive analytics platform can be intimidating. There may be years of historical data to consider, modification and fine-tuning to review, and existing models to understand and carry forward. Beyond the technical work, a successful migration also means managing the change itself: preparing users for a new system, carrying forward lessons learned from the current platform, and making sure valuable work isn't lost in the process. The good news is that a predictive maintenance migration does not have to mean starting over.
With the right planning, engineering support, and implementation process, organizations can transition to a new predictive analytics solution while preserving the information they need and building a stronger foundation for future analysis.
Getting Started: Understanding Where You Are
Before beginning a predictive analytics migration, it is important to understand what you currently have and what you want from your new solution. And a successful migration starts with more than simply moving data from one system to another. It is an opportunity to evaluate your current predictive maintenance strategy and determine what should be carried forward, improved, or changed.
Part of that evaluation should include a review of how reliable your current insights are. Predictive analytics is only useful if people trust it, and that trust depends on managing two kinds of errors.
- False positives occur when the system flags a problem, such as equipment degradation, that isn't actually there. Too many false positives create a boy-who-cried-wolf effect, where operators and maintenance teams begin to tune out alerts altogether.
- False negatives are the opposite: the system fails to flag a real problem, which means a developing failure can go unnoticed until it becomes a bigger issue.
Either type of error undermines confidence in the system's insights, and understanding where your current solution falls short in each area can help shape what you need from your next one. With that assessment in mind, the next step is to translate it into concrete goals for the new system.
Identify Your Goals
Because this is a migration and not a first-time implementation, your goals should be defined in terms of what you have today versus what you need going forward. Some questions to start the conversation include:
- What problems are we trying to solve with predictive analytics that our current system isn't solving?
- Which equipment is most important to monitor and is our current system monitoring it effectively?
- What information do our operators and maintenance teams need that they aren't getting today?
- Which existing models or analytics are providing real value and should be carried forward?
- Where are the gaps in our current solution?
- What would we like our new system to do better?
The answers to these questions form the basis for a clear set of goals, ensuring the new system is designed around your operational needs rather than replicating the old one.
Gather Your Information
Predictive maintenance tools generally require a similar core set of information to get up and running, regardless of which platform you're using. Knowing this in advance means you can start pulling this information together now, or at least let the relevant people on your team know it will likely be needed, rather than scrambling to track it down once the project is underway.
Your implementation team will need a clear understanding of your equipment and existing data environment. This typically includes your tag list, historical equipment data, system overview, equipment hierarchy, and tag naming conventions, along with hardware, server specifications, and communication architecture.
For a migration specifically, it also helps to gather an export or documentation of your existing models and equipment hierarchies, an understanding of who currently has access to the system and how, and a clear picture of how data currently flows between your equipment, historian, and analytics platform. This context helps the implementation team understand not just your data, but how your current solution is structured and used.
Plan for Change Management
Technology is only part of a migration. Your team also has to get comfortable using something new, and that takes planning.
Decide early how long you'll run the old and new systems side by side. This gives your team time to check that the new platform is producing accurate results before you rely on it fully. Figure out what training operators, maintenance staff, and administrators will need, and try to schedule it close to go-live so it's still fresh when they start using the system day to day. It also helps to have someone on your team who knows the new platform well and can field questions as people adjust.
Set a firm date to retire the old system. If that date keeps slipping, people tend to fall back on what they know, which slows adoption and leaves your team unsure which system to trust for accurate information.
Choosing the New Solution
With your goals defined, your information gathered, and a change management plan taking shape, the next step is selecting the solution you'll migrate to. This is where the goals identified earlier come back into play: weigh potential platforms against the problems you're trying to solve, the gaps you've identified in your current system, and the capabilities that matter most to your operators and maintenance teams.
It's also worth asking how much of the implementation work a vendor will handle versus how much falls on your team. Building equipment-specific models, correlating tags, and configuring a system around your operation takes real engineering effort, and vendors vary in how much of that work they take on.
The HanAra Difference
We Handle Engineering
Your team should not have to become experts in building predictive models just to implement a predictive analytics platform.
During implementation, HanAra's experts create the required models and configure the system based on your equipment and operational environment. This reduces the engineering burden on your organization while taking advantage of our team's experience with predictive analytics implementations.
We Build Around Your Equipment
Every plant and every piece of equipment operates differently.
HanAra models the system based on how your organization operates and organizes its equipment. This helps ensure the resulting insights provide meaningful information rather than generic measurements.
We Partner Through the Whole Process
Implementation isn't something we hand off and walk away from.
HanAra stays involved from initial analysis through training to ongoing usage, so your team has support at each stage rather than being left to figure out a new system on their own.
Putting It Into Place
Implementation approaches vary by vendor, but most follow a similar general pattern. Here's an example of what that process looks like with HanAra's predictive maintenance solution HanPHI.
1. Analyze
The first step is understanding your system.
HanAra reviews your existing data, equipment, requirements, and technical environment to determine how the predictive analytics solution should be configured. This includes reviewing your tag list, historical data, system overview, equipment breakdowns, naming conventions, hardware, server requirements, and communication architecture.
The goal is to establish a clear understanding of the environment before the technical work begins.
2. Import
Once the requirements and data have been reviewed, implementation can begin.
HanAra builds the database and imports historical equipment data to learn how each asset normally behaves. This equipment data includes measurements like pressures, temperatures, and flows from across the site.
Historical data can be particularly valuable because predictive analytics depends on understanding what normal looks like for an asset.
3. Create
This is where the predictive analytics solution begins to take shape.
HanAra creates a hierarchical representation of your system and builds machine learning models based on how your operation actually runs. During this process, relevant tags are correlated, and problematic data, including bad-quality data, downtime, and outliers, is addressed.
Because the models are built around your specific equipment and processes rather than generic templates, the health indexes they produce point operators and maintenance teams to the specific equipment and conditions driving an issue, not just a general warning.
4. Install & Optimize
Implementation doesn't stop once the software is installed.
After the system and models have been prepared, HanAra installs the solution and establishes the necessary connections. Once the system is running, it's tuned using real-time plant data.
Bringing in real-world operating conditions during this step sharpens the models and improves the accuracy of the system's health information.
5. Train
The final step is making sure your organization knows how to use the system.
A predictive analytics platform is only valuable if the people responsible for operating and maintaining equipment can understand and use its insights. HanAra provides training for users and system administrators so organizations can confidently incorporate HanPHI into their day-to-day operations.
Training also helps organizations get value from the system sooner instead of spending months figuring out how to use a newly installed platform.
What Comes Next
Implementation isn't the finish line. Equipment ages, processes shift, and plants change over time, so predictive analytics models need occasional attention to stay accurate, whether that means adding or removing tags, checking data quality and connections, backing up data, or retraining models after major changes.
And if your organization is ready to look at options for replacing its predictive analytics solution, let's talk about how HanAra can help.
