Overview
Turning fragmented sensor data into early warnings that maintenance teams can act on.
For a natural gas compression operator, an unexpected engine shutdown can mean lost production, urgent repairs, and travel to remote sites. The client needed a way to spot developing faults earlier and give maintenance teams useful information before dispatching them.
Its existing systems made that difficult. Engine and compressor data was spread across separate platforms, large volumes of sensor readings slowed processing, and identifying meaningful warning signs depended heavily on experienced engineers.
Fusemachines consolidated the client’s data pipelines into Microsoft Fabric and built a machine learning model trained on anomalies reviewed by equipment specialists. The system flags developing engine faults hours in advance and pairs alerts with AI-generated summaries of possible causes and recommended diagnostic checks.
Power BI dashboards bring those alerts into the maintenance workflow. Equipment specialists review findings, raise work orders, and provide feedback to improve the model. The solution is now deployed across more than 1,000 engines spanning multiple engine families.
The challenge
Maintenance Driven by BreakdownsFrequent equipment shutdowns interrupted operations, required urgent travel to remote sites, and resulted in lost revenue.
A Platform Migration Without DisruptionExisting Databricks and Dataiku pipelines needed to move to Microsoft Fabric while production monitoring continued.
More Sensor Data Than Pipelines Could Efficiently HandleHigh-frequency readings from across the fleet created processing bottlenecks during peak operating hours.
A Fragmented View of Equipment HealthSeparate sources of engine and compressor data made it difficult for technicians and managers to identify emerging problems.
Expert Knowledge That Had Not Been CapturedHistorical anomalies lacked verified labels. Experienced equipment specialists needed to distinguish meaningful warning signs from normal operating variations before the model could learn from them.
Fuse solution
Early Fault DetectionBuilt a supervised machine learning model using anomalies verified by equipment specialists, enabling the system to flag developing engine faults hours in advance.
A Unified Microsoft Fabric PlatformMigrated legacy Databricks and Dataiku pipelines into Microsoft Fabric, creating a shared foundation for sensor data, analytics, and model operations.
Expert Feedback Built Into the WorkflowIntegrated Power BI dashboards that let equipment specialists review alerts and record feedback. Their assessments feed back into the platform to support model retraining and performance evaluation.
Automated Sensor AlertsConnected sensor data from Azure Data Lake Storage to Microsoft Fabric and automated notifications to equipment specialists and field teams.
Clear Guidance for InvestigationAdded AI-generated summaries that explain possible causes and recommended diagnostic checks, helping technicians decide where to begin their investigation.
Coverage Across Engine FamiliesExpanded the solution from a single engine model to multiple engine families using the same underlying data and monitoring pipeline.
The impact
Earlier warnings, informed maintenance decisions, and a shared platform for fleet monitoring.
Earlier Warning
Developing engine faults are flagged hours in advance, giving teams time to investigate and plan repairs.
Action in the Field
Equipment specialists review alerts and raise work orders, connecting model findings directly to maintenance decisions.
Unified Operations
Previously fragmented sensor data is consolidated in Microsoft Fabric, giving teams a shared foundation for monitoring equipment health.
Ongoing Improvement
Feedback from equipment specialists supports continued model evaluation and refinement.
Scale
Deployed across more than 1,000 engines spanning multiple engine families, with support for additional families in development.
