MACHINE LEARNING / INDUSTRIAL AI

Predictive Maintenance & Equipment Health

Using operational data to make equipment condition more understandable and actionable.

Spline regression for normal-behaviour modellingAnomaly detectionHealth scoringRemaining useful lifeTime-series analytics

Illustration for Predictive Maintenance & Equipment Health

01 / OVERVIEW

The challenge.

Maritime equipment-health intelligence built around modelling normal behaviour, identifying deviations, health scoring, and remaining useful life prediction. Includes a ClassNK-endorsed maritime predictive-maintenance achievement documented in the résumé.

THE BIGGER PICTURE

Why this matters.

Industrial machinery generates large volumes of data, yet it can be difficult for operators to tell whether equipment behaviour is normal, drifting or indicative of an emerging issue. The goal was to convert that data into interpretable equipment-health intelligence.

My work in maritime predictive maintenance connects domain knowledge, time-series analytics, modelling and operational software. It includes a ClassNK-endorsed predictive-maintenance application for vessels, as recorded in my résumé.

MY CONTRIBUTION

From idea to working system.

Applied normal-behaviour modelling using spline regression, anomaly detection, equipment health scoring and Remaining Useful Life estimation. The work extended beyond model development into data preparation, production inference and operational presentation.

Condition information becomes useful when engineers can connect abnormal signals to equipment, track change over time and prioritize attention. The objective is to support maintenance decisions rather than issue a stream of unexplained alerts.

WHERE IT CREATES VALUE

Practical applications.

Behaviour-aware equipment condition monitoring

Earlier visibility of abnormal operating patterns

Interpretable health indicators and trends

A foundation for condition-based maintenance decisions

02 / WHAT I WORKED ON

Engineering highlights.

Condition monitoring driven by equipment behaviour

Models supporting early anomaly identification

Interpretation of sensor deviations as maintenance intelligence

03 / TECHNOLOGIES & APPROACHES

Technical capabilities.

Spline regression for normal-behaviour modellingAnomaly detectionHealth scoringRemaining useful lifeTime-series analyticsProduction ML

04 / PRACTICAL VALUE

What it enables.

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Equipment health visibility

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Condition-based decision support

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Early identification of performance deviations

Descriptions are high-level capability summaries. Client-specific designs, sensitive data, source code and proprietary implementation details are intentionally omitted.