Introduction

An international Oil & Gas company using SAP Asset Performance Management (SAP APM) needed a more intelligent way to identify unreliable sensor data before it could impact maintenance decisions.

Lighthouse developed an AI-powered solution that automatically detects recurring sensor data freeze patterns, helping maintenance teams distinguish between isolated anomalies and systematic reliability issues.

Modern industrial environments generate massive volumes of time-series sensor data. When sensor values stop changing (“freeze”), the cause may range from communication failures and sensor malfunctions to data ingestion issues or assets remaining in the same operating state.

Traditional rule-based monitoring often produces too many false positives or fails to identify recurring patterns, making it difficult to trust the underlying data. As a result, maintenance decisions risk being based on outdated or inaccurate information.

Lighthouse developed an AI-driven freeze detection solution built on SAP AI Foundation and integrated with SAP Asset Performance Management.
Instead of relying solely on static thresholds, the solution analyzes historical sensor data to identify recurring freeze events and evaluate their significance. The AI distinguishes one-off anomalies from systematic reliability problems, enabling more accurate detection and reducing unnecessary investigations.

The solution also generates recommendations for optimizing detection parameters such as thresholds and rules, allowing customers to continuously improve monitoring quality over time.