Is SciML the Predictive Maintenance Breakthrough Manufacturing Has Been Waiting For?

When it comes to industrial equipment failures, most problems are only discovered after they’ve disrupted production, damaged equipment or exhausted maintenance budgets. And the consequences are more than significant as equipment downtime can lead to as much as 20% productivity loss. The impact on the bottom line is equally staggering as the U.S. Department of Energy reports cost associated with lost productivity and equipment repairs to reach roughly $50 billion for the nation’s collective manufacturers.

Predictive Maintenance

To help mitigate downtime, organizations are adopting proactive approaches to equipment maintenance. And it is having an effect as research from McKinsey & Company finds that implementing predictive maintenance programs can cut unplanned downtime in half and reduce overall maintenance costs by 18-25%.

Moving away from a reactive mindset is a big step forward, and machine learning (ML) has become an essential tool. Despite its advantages, however, one roadblock remains: To create predictions, traditional ML tools learn purely from examples relying heavily on large volumes of clean historical data patterns. 

Because such information rarely exists in the real world, ML predictions tend to fall apart if the data is incomplete, noisy or not representative, which is often the case for pumps, motors, gearboxes, compressors and process equipment. The result can be millions of dollars invested with, at best, incremental returns.

A Physics-Driven Alternative

Scientific machine learning (SciML) is different. Learning from both examples and fundamental equations, SciML complements data-driven ML with established scientific rules. The result is a smarter, more reliable model for science and engineering problems. Because the system learns from data and applies the laws of physics, predictions are more realistic (less prone to making predictions that break the laws of physics) and generally more accurate with less training data required.

In the case of equipment maintenance, SciML models are derived from the governing physics associated with fluid dynamics, torque-load relationships, thermodynamics, pump curves, friction and mass balance, geometric constraints and other engineering fundamentals.

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With the model grounded in physics, in the most general terms:

• Roughly 80% of accuracy comes from engineering specifications alone
• Only the remaining 20% require on-site telemetry for calibration
• Failure modes can be detected even if they never appear in historical data

This approach delivers predictive accuracy despite data that is poor or incomplete, which is largely why aerospace, aviation, motorsport, automotive engineering, pharmaceuticals and HVAC optimization were early adopters.

A Real-World Example

Consider a sewer rising main in a wastewater management facility. In this application, a conventional ML approach would require years of historical data related to inflow/outflow, pump behavior, tank levels and environmental conditions. In addition, the data should be labeled so that failure modes can be learned. If any of that data was missing or incomplete, accuracy and reliability would collapse.

For example, when a tank spills, this information isn’t necessarily captured by the sensors. A level sensor reporting in meters would simply report that the tank reached a particular value and then held constant. In reality, the tank reached its maximum volume and began overflowing. In this case, a ML model wouldn’t be able to infer the correct mass balance, whereas a SciML model would have already properly captured this information from the starting engineering information.

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SciML’s scientific approach begins with known variables such as tank geometry and volume, mass-balance equations, pump-curve behavior and physical limits on flow, head and pressure. In this scenario, with only minimal telemetry, the SciML model will self-correct, maintain physical realism (understanding that tanks spill and don’t have infinite volume) and remain reliable.

This is critical in situations where flawless data rarely exists. What’s more, it can be readily applied across similar rotating equipment including pumps, gearboxes, compressors, motors and distributed mechanical systems.

Building a Digital Twin

A digital twin acts as a virtual sensor, providing insight as to what’s going on inside the physical piece of equipment. Because those scenarios are driven by physics, rather than historical patterns alone, the model can even simulate failure modes that have never appeared in historical data.

The model delivers real-time predictive health indicators tied directly to underlying equipment mechanics and using State Machine techniques can remain reliable even when instrumentation isn’t.

Delivering Measurable Results

In a recent water-utility virtual study, SciML was used to monitor borehole pumps using only existing data (no added sensors). This provided maintenance with the confidence that there were no changes in bearing wear or impeller erosion. It also uncovered energy losses driven by subtle drift in control settings, which were not previously visible.

Even when no prior examples are available, the model can simulate missing failure modes and provide early warnings. Throughout the study, SciML produced real-time predictive health scores, enabling continuous condition monitoring and informed maintenance decisions.

Because physics governing pumps, motors, valves, HVAC plants and rotating machinery are universal, SciML delivers repeatable improvements across industries:

Manufacturing

• 50% increase in operational efficiency, yielding $400 million in financial gains

Aviation

• 20% reduction in maintenance costs on a $1billion budget
• 99.9% diagnostic accuracy
• 100× faster model execution

Automotive

• 500x faster and 2× more accurate models
• 20% reduction in development costs

HVAC/Building Systems

• 15% reduction in downtime
• 10% reduction in energy cost

Motorsports

• 4× faster models with 50% lower error
• 169× faster insights vs. sensor-only strategies
• 10% cost reduction in sensor optimization

Pharmaceuticals

• 50% cost reduction in clinical-trial modeling, saving $80 million per trial

The Real ROI

Traditional predictive-maintenance programs make improvements one piece of equipment or production line at a time. Because SciML begins with a physics-based model, wider deployment becomes fast, repeatable and less expensive.

In this way, measurable ROI is unlocked as a single model can scale across equipment lines. Instead of a long series of pilots, results occur when similar types of equipment are included, no matter if it’s ten or ten thousand machines.

A Shift in Strategy

Rather than depending on wide sensor coverage, engineers use physics to create virtual sensors that infer missing behavior from data that is incomplete or bad. These virtual sensors produce outputs in physical units (stress, temperature, wear rates), enabling engineers to directly quantify equipment health, rather than rely on opaque RAG (Red/Amber/Green) indicators that offer limited diagnostic value.

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This shift is also transforming deployment strategy. Instead of running isolated pilot projects or continually installing instrumentation, teams scale physics-informed intelligence across assets, lines and facilities.

As results demonstrate the value of SciML, skepticism from earlier predictive-maintenance failures begins to fade and is replaced by confidence in a physics-grounded approach.

Modernizing Industrial Operations

Manufacturers face relentless pressure to increase throughput, reduce downtime, cut energy use, extend asset life and cut operating costs, all while relying on aging equipment and imperfect data.

SciML addresses these challenges by:

• Working with the data you have already collected
• Accelerating digital-twin adoption through physics
• Eliminate guesswork
• Enhancing engineering judgment instead of replacing it

As organizations embrace science-based models that understand equipment at its core, reliability becomes proactive rather than reactive and practical to scale. For manufacturers determined to compete on uptime, efficiency and operational resilience, SciML isn’t just the next step, it’s the transformative starting point.