When expected design life doesn’t match reality

Consider a common reliability scenario.

A mechanical component — such as a rolling element bearing — has an expected operating life defined by the manufacturer. Under specified loads, lubrication regimes, and environmental conditions, it should operate reliably for years.

Yet in practice, it fails significantly earlier.

The immediate assumptions are often:

• Poor manufacturing quality

• Pre-installation structural flaws

• Installation error

• Lubrication issues

Sometimes these are correct.

However, in many industrial environments, premature mechanical degradation is strongly influenced by operating conditions. Age related failures are not random most of the time — they are cumulative.

Consider that same bearing, now in the context of a gas compressor in a gas export plant, that is experiencing frequent yet excessive vibration due to process-related slugging issues or anti-surge valve malfunction. Excessive or prolonged vibration, abnormal suction conditions, or repeated transient load events may still remain technically “within operating envelope limits.”

From a control room perspective, nothing has breached an alarm.

But mechanically, the bearing is operating under sustained abnormal stress — sometimes for days or weeks.

The design life assumptions no longer reflect operational reality.

 

Process Conditions and Mechanical Ageing

Mechanical components such as bearings, seals, couplings, and gears are designed around assumed boundary conditions:

• Steady loads

• Controlled temperature ranges

• Appropriate lubrication regimes

• Predictable duty cycles

In real-world industrial operations, particularly process-intensive industries like oil and gas, assets are exposed to:

• Fluctuating or abrupt load changes

• Repeated start-stop cycles

• Process upsets and trips

• Environmental variability

• Changes in setpoints and throughput demands

• Slight load increases.

• Minor temperature shifts.

• Small variations in operating envelopes.

• Repeated transient events.

• Human error

Individually, these deviations may appear insignificant. Collectively, they can accelerate wear well beyond manufacturer expectations.

Over time, degradation rates shift.

The component may still appear “within limits” from a traditional operating thresholds perspective — but its degradation trajectory has already changed.

 

Seeing the Bigger Picture: Process and Mechanical Health Together

It is important to distinguish between two complementary perspectives.

On one hand, purely physics-based approaches — such as vibration spectrum analysis in the frequency domain — remain fundamental for tracking bearing health. These techniques are grounded in mechanical principles and provide deep insight into fault modes.

On the other hand, understanding why degradation is accelerating often requires a broader view of process context.

If a compressor ecosystem is experiencing subtle but sustained deviation from its intended operating philosophy — for example:

• Lack of process optimization

• Prolonged operation due to lack of readiness of subsequent assets

• Filter or strainer clogging

• Valve performance drift

• Lubrication system instability

• Seal gas, air instruments, fuel or other utility inconsistencies

— those process-level deviations can influence both reliability and degradation behavior across mechanical components.

Tracking mechanical health in isolation is valuable. Connecting it to operating envelopes and process philosophy provides context.

Both perspectives matter.

 

Why Early Signs Are Often Missed

In most plants, monitoring strategies rely on thresholds and alarm rationalization — for good reason. Excessive alarms create noise, fatigue, and operational risk.

As a result, early-stage degradation often looks like normal variation:

• A 5% increase in bearing temperature

• A slight rise in vibration amplitude

• A small shift in power draw

These changes may not justify investigation. Engineers are prioritizing immediate operational risks. If parameters remain comfortably below alarm thresholds, it is entirely rational not to intervene.

The challenge is that degradation does not always evolve linearly.

A modest thermal rise that appears insignificant today can be followed by a sharp increase tomorrow — once lubrication breakdown, friction escalation, or load redistribution reaches a tipping point.

Some failures accelerate rapidly.

What appeared stable can become critical in a short time frame.

This is not a failure of engineering practice. It is a consequence of scale, workload, and necessary alarm management discipline.

 

Mathematical Methods and AI: Complementary, Not Competing

It is important to be precise here.

If the root cause of a failure is present in the data, traditional statistical analysis, signal processing, thermodynamic modelling, and reliability engineering techniques can uncover it.

These methods remain highly effective and essential.

Data-driven AI modelling does not invalidate physics-based or mathematical approaches.

Its primary advantage is scalability.

Modern industrial facilities generate high-frequency time-series data across large fleets of interconnected assets. Continuously analyzing subtle, correlated deviations across process and mechanical parameters — under changing operating conditions — is difficult to sustain manually.

AI-based models can:

• Monitor multivariate behavior continuously

• Detect correlated deviations across systems

• Surface early shifts without increasing alarm noise

• Scale analysis across multiple assets simultaneously

They extend engineering visibility.

They do not replace engineering judgement.

 

The Real Constraint: Bandwidth and Visibility

Engineers do not miss early degradation because they lack capability.

They miss it because:

• Operational priorities demand attention

• Alarm thresholds are appropriately rationalized

• Subtle deviations do not justify immediate escalation

• Analyzing every small variation is not practical

In this environment, data-driven monitoring helps identify which small deviations deserve closer inspection.

It does not declare every 5% shift a failure risk.

It highlights patterns that indicate a change in degradation behavior — allowing informed decisions earlier, with context.

 

From Premature Failure to Informed Intervention

When a bearing expected to operate for years fails in months, the question is rarely whether monitoring existed.

A more relevant question is:

Were the sustained process-driven stressors visible early enough — and connected clearly enough — to act?

Age-related mechanical degradation in industrial systems is often cumulative, context-driven, and nonlinear.

Combining classical engineering techniques with scalable data-driven monitoring improves visibility into that progression.

The objective is not to replace established methods.

It is to ensure that early mechanical degradation — especially when influenced by process variability — is recognized in time to make informed, controlled decisions.

 

This article is part of the Sia Thought Leadership Series — engineering-led perspectives on industrial asset health, predictive analytics, and the practical application of AI in real-world operations.