For years, manufacturers have relied on process data to guide operations and optimize efficiency. While process data helps steer the ship, it doesn’t always tell you when trouble is coming. That’s where predictive maintenance (PdM) data comes in, condition monitoring tools such as vibration analysis, oil quality sensors, and thermography offer early warning signs of equipment deterioration.
However, process and predictive data are often siloed, and organizations miss opportunities to avoid downtime and make the strategic maintenance move from problem identification to overcome the challenges created by the segmented data silos of reactive maintenance.
Why Separate Data Silos Are a Problem
Process data is typically captured automatically and stored in a dedicated software system. It’s used to identify the best-performing production cycles — the so-called “golden batch.”
On the other hand, PdM data often lives in other systems, making it difficult to correlate machine health with process conditions. This lack of interoperability across the silos prevents reliability engineers from seeing the deeper insights hidden in the separate data stacks.
Take the example of a pump impeller damaged by cavitation. PdM tools might detect the damage, but those tools won’t tell you why it happened. The root cause is operational, not mechanical. Only process data can show that the pressure and flow conditions at that time contributed to the failure.
Connecting the dots between PdM and process data gives you the ability to avoid such a failure entirely.
Avoiding the “Data Lake” Trap
Many companies assume that they need to throw all their data into a “lake” and sift through it later. The problem is that “later” never comes. As a result, this approach rarely delivers value.
Instead, think strategically about the data you need to anticipate failure and its preconditions. Methodologies like data-oriented failure analysis (DOFA), which is rooted in reliability-centered maintenance, shine in this use case.
DOFA helps identify which sensor inputs (vibration, temperature, oil condition) are actually useful in predicting and preventing failures. Think of all the bits of data you dump into the lake as fish. Reliability-centered maintenance and the DOFA framework help you determine which ones are worth catching.
The Real Barrier Isn’t Tech — It’s People
While integrating legacy systems can present challenges, the biggest obstacle to predictive maintenance is often cultural. The success of PdM depends on cross-functional collaboration. You need to bring together operations, IT, engineering, and procurement. This shift from traditional roles and responsibilities to a digital, data-driven collaboration can be uncomfortable for the humans involved in it.
People often fear making the wrong call when using new tools. They worry about the pace of decision-making in a digital environment. Overcoming the human resistance to change can speed up your PdM adoption journey considerably.
How to Move Forward
Are you ready to embrace PdM principles? Step one is to unify your process and predictive data. However, you don’t need to overhaul everything at once. Start by assessing how your data is stored, and whether it’s accessible and shareable. Next, explore solutions to combine your PdM and process data.
Then, look at your people and determine whether they’re ready for the shift. Investing in training and partnering with a collaborative solutions provider can be far more valuable than purchasing the latest sensors or applications.
The future of maintenance is about avoiding predictable failures, and you’ll need all the systems and people to be on the same page to make that happen. If you’re ready to see how it can work for your organization, explore our Predictive Maintenance solutions here.
