The Power of Data Visualization to Optimize Maintenance and Asset Integrity

As the saying goes, “A picture is worth a thousand words.” When it comes to mechanical integrity (MI), a “picture” – more correctly, data visualization – can provide important context to help operators understand what is occurring with their assets and what levels of risk exist within their facilities. In today’s digital landscape, what’s possible for data visualization in mechanical integrity is more than just a picture: it is a live, dynamic, virtual replica from which critical insights can be remotely accessed in real-time. With the level of context this visualization provides, a “picture” can be worth far more than a thousand words – it can be worth the difference between catastrophic equipment failure and proactive maintenance intervention.

In this article, we will examine what data visualization means in the context of maintenance and asset integrity management: the types of data that are integrated, the dynamic visualization tools that can be utilized, and both the benefits and limitations of implementing them.

What is Data Visualization?

Data visualization refers to translating information into a visual context to make data easier for the human brain to pull insights from. It provides a quick, effective way to communicate complex information universally. It is considered a key step of the data science process, which states that “after data has been collected, processed and modeled, it must be visualized for conclusions to be made [1].” It is reasonable to assert, then, that data visualization of industrial assets is critical to decision-making in managing those assets. It can be transformative in optimizing maintenance and reliability.

Data visualization provides crucial context for interpreting the vast amounts of information that an asset amasses within its lifecycle. It consolidates data sets into a holistic, visual overview from which an operator can quickly determine what the data means. It provides immediate answers to critical questions: What is the current operating condition and health of these assets? Which of them pose the most risk? Where can I maximize the efficiency of my maintenance and inspection planning? And so on.

Visualization is also essential to advanced analytics. When data scientists write advanced predictive analytics, they must be able to visualize the outputs to ensure the models are performing as intended [1]. The same applies to advanced analytics in asset management. By visualizing trends, patterns, root causes, simulated outputs of maintenance decisions, and so on, it becomes possible not only to implement preventive maintenance strategies based on the lessons learned but also to gain a prescriptive understanding of what to expect as an outcome from different scenarios.