Picture this: you’re responsible for monitoring hundreds of pieces of equipment across a sprawling industrial facility where a single failure could cost thousands in downtime. Every day, you and your team face an impossible choice: inspect every piece on a rotating schedule, missing developing issues between checks, or focus only on critical assets, leaving potential problems undetected elsewhere. Either way, something will be missed.
This dilemma drives many operations teams to seek technological solutions. Technology offers 24/7 vigilance without the human limitations of fatigue, shift changes, or attention spans. But the next question becomes critical: What kind of technology truly helps?
Some vendors promise an easy button, a magical solution that eliminates complexity. But experienced industrial operators know better than others. In the real world of heavy equipment, complex processes, and interconnected systems, there are always tradeoffs that must be carefully balanced.
Preventing Failures, Not Just Predicting Them
When it comes to anticipating equipment health issues and potential failures, two primary approaches have emerged:
Theoretical modeling, also known as first principles modeling or mechanistic modeling, uses physics-based equations and engineering principles to explain complex processes. These models excel at understanding specific input-output relationships and can be especially valuable for newly designed systems where historical data doesn’t exist.
Empirical modeling relies on actual observations and historical data patterns to detect subtle changes in system behavior. Rather than attempting to model every physical interaction, empirical models identify meaningful patterns in real-world data.
Many organizations feel pressured to choose one approach exclusively. But as the popular taco commercial asks, “Por qué no los dos?” Why not both? The truth is that combining these approaches offers far greater value than using either of them in isolation.
What Makes Empirical Modeling Powerful
Empirical modeling excels at detecting subtle changes in system behavior without requiring complex physical equations. By learning from historical patterns, these models can:
- Detect anomalies before they become critical failures
- Adapt to the unique personality of each piece of equipment
- Identify correlations that aren’t obvious even to expert operators
- Provide early warnings with sufficient lead time for intervention
The beauty of an empirical approach lies in its practical focus. It acknowledges a fundamental truth that theoretical models sometimes miss: in the real world, nothing stays perfect. Equipment ages, environmental conditions fluctuate, and processes drift over time.
How Empirical Models Work
Empirical models create patterns from historical data when systems operate normally. The model uses statistical techniques to identify correlations between different parameters, including temperature, pressure, flow, vibration, and other types.
The process typically works through:
- Data Collection: Gathering historical analog data (temperatures, pressures, flows, etc.) during normal operations
- Parameter Grouping: Identifying related parameters through correlation analysis
- Model Training: Creating baseline expectations for how these parameters should behave together
- Real-time Comparison: Continuously comparing current readings against expected values
- Health Indexing: Calculating a health score based on how well current behavior matches expected patterns
When current readings deviate from expected patterns, the system can detect potential issues long before traditional alarm thresholds are triggered.
The Hybrid Approach: Combining Data with Domain Knowledge
The most effective implementations of empirical modeling don’t discard first principles modeling or human expertise. Instead, they incorporate them through:
Intelligence Parameter Grouping
By grouping related parameters based on correlation coefficients AND engineering knowledge, we create models that respect both statistical relationships and physical realities. This isn’t just blind number-crunching—it’s guided by understanding what parameters should be related in a healthy system.
Strategic System Visualization
Effective systems organize components hierarchically, mirroring the actual structure of the plant. This allows engineers to quickly drill down from a high-level health index to specific components and ultimately to individual sensors causing issues.
Expert Human Oversight
No algorithm can explain everything in our world. The best systems provide insights that complement human expertise rather than replacing it. When operators see patterns that match their experience, trust in the system grows.
Real-World Impact
When implemented thoughtfully, empirical modeling with domain and first principles expertise delivers tangible benefits:
- Early detection of potential issues weeks or even months before traditional alarms would trigger
- Reduced maintenance costs by focusing efforts where they’re actually needed
- Improved reliability through consistent monitoring of system health
- Enhanced operator confidence by providing clear, actionable insights
For example, one site monitored its charging pumps, a critical asset supplying filling water to the reactor coolant system and sealing water to prevent coolant leakage, using an empirical model combined with first principles and human expertise. The operations typically alternate between two pumps every three months for redundancy, with bearing temperatures normally running between 80–90°C.
During a routine operation, the model detected subtle anomalies in the behavior of charging pump A well before the Distributed Control System (DCS) alarm was triggered by analyzing both the bearing temperature and bearing lubricating oil temperature. The early alert allowed operators to focus their attention, validate the findings, and prepare for intervention. Within 30 minutes, the team initiated charging pump B and executed an emergency stop on pump A, preventing potential equipment failure and unplanned downtime.
Following the event, the maintenance team disassembled the faulty pump and confirmed the issue: a worn thrust bearing. They replaced the component and rebuilt the pump, avoiding a much costlier failure and reinforcing confidence in the system’s predictive capabilities.
Embracing Nuance with First Principles Modeling
Remember that impossible choice we started with? The one that forces maintenance teams to either inspect everything on a rotating schedule or focus only on critical assets?
The future of industrial analytics transforms this dilemma by eliminating the either/or thinking that has constrained our approach. By embracing the strengths of different modeling philosophies and combining them intelligently, we create a more comprehensive solution.
Empirical models excel at detecting subtle deviations from normal behavior. First principles modeling provides context and explanatory power. Human expertise adds critical judgment and domain knowledge that no algorithm can match.
When these approaches work in harmony—empirical techniques informed by physical understanding and enhanced by human expertise—the result is something more powerful than any single method could provide on its own: a system that can monitor everything, all the time, with the intelligence to distinguish between normal variations and early warning signs of failure.
After all, in the complex world of industrial operations, why limit ourselves to black and white when we can access the full spectrum of analytical power?
Want to learn more about how empirical modeling can transform your operations? Contact our team to discuss your specific challenges and opportunities.
