Case Studies
Industrial asset monitoring and risk forecasting platform
Key Results
- 100,000+ monitored assets
- 35M+ reference records analyzed
- 2,000+ asset types supported
About the project
A North American energy software provider engaged PerformaCode to work on a legacy asset risk management platform for power equipment monitoring.
The system collected sensor and diagnostic data from transformers, circuit breakers, and other grid assets, then compared incoming measurements against a large historical reference database. Operators used the platform to track asset health, view alerts, investigate abnormal readings, and support maintenance or replacement planning.
PerformaCode worked on the web platform, reporting workflows, data visualization, ETL processing, and risk-analysis functionality. The project included defect fixing, new feature development, GUI improvements, reporting updates, performance work, and modernization of selected components to meet newer Smart Grid and security requirements.
The platform operated at large infrastructure scale, with telemetry from 100,000+ assets, 35M+ reference records, 2,000+ asset types, and real-time usage by utility and energy operations teams.
5-8
engineers
25
months
Agile DDC
delivery mode
Client challenges
The platform had been in production long enough for the data model, reporting logic, ETL flows, and UI behavior to become tightly coupled. Modernization could not be handled as a frontend refresh because dashboards, maps, alerts, health indexes, and reports all depended on the same SQL-heavy processing path.
The main pressure came from data scale and latency. The system had to compare incoming measurements from 100,000+ assets against 35M+ historical reference records, then expose the result through asset lists, diagnostic views, charts, reports, and geospatial status maps.
The legacy code also carried production defects in data output and reporting behavior. Fixing those defects required engineers who could trace problems through .NET MVC code, Knockout.js UI logic, MS SQL queries, ETL jobs, and analytical rules rather than treating each bug as a UI issue.
The client needed outside engineering support because the work sat across several specialist areas at once: legacy .NET, database performance, ETL, mathematical models, risk scoring, visualization, and Smart Grid/security requirements. A narrow web team would not be enough, and internal product engineers needed additional capacity without losing system context.
Tasks performed
- Modernized legacy web components in the asset risk management platform using .NET MVC and Knockout.js.
- Fixed data-output defects affecting reports, diagnostic views, charts, and asset-level status displays.
- Developed GUI workflows for asset lists, health indexes, diagnostics, measurement history, and system reports.
- Built severity-based asset views with green/yellow/red status indicators for equipment condition and operational risk.
- Implemented geospatial asset mapping so users could locate equipment and inspect asset status by region.
- Created one-click drill-down views for asset diagnostics, subsystem status, measurement history, and failure-cause analysis.
- Developed asset health dashboards for risk assessment, wear-out rate, maintenance timing, and replacement planning.
- Built regional management reports showing operational status, risk assessment, KPIs, and asset health by region.
- Implemented notification workflows for status changes, abnormal readings, and operational alerts.
- Refined ETL processing between sensor data storage, analytical processing, and reporting workflows.
- Implemented proactive monitoring logic for comparing incoming measurements against historical reference values.
- Supported mathematical models for risk analysis including health indexes, wear-out assessment, and replacement forecasting.
- Improved data exchange between platform components across reports, logs, alerts, dashboards, and operational views.
- Optimized SQL-heavy data processing paths for dashboards, reports, historical measurements, and diagnostic views.
- Added logging and audit-related functionality for records, measurement compliance, and system behavior tracking.
- Integrated risk outputs with budgeting, asset-management, and replacement-parts planning workflows.
- Supported Smart Grid and security-related modernization of the legacy platform.
Project results
100,000+ assets monitored
Enabled continuous monitoring of 100,000+ field assets by aggregating sensor and diagnostic data, refining ETL flows, and exposing equipment health through dashboards, alerts, and reporting workflows.
35M+ records analyzed
Compared incoming measurements against 35M+ historical records by refining SQL-heavy analytical paths, reference-value comparison logic, and risk-analysis workflows used to detect abnormal equipment behavior.
2,000+ asset types
Extended diagnostics, reporting, and health-monitoring workflows across 2,000+ asset types by implementing equipment-specific visualizations, health indicators, and subsystem-level diagnostic views.
1,000+ real-time users
Improved operational usability for 1,000+ concurrent users by modernizing GUI workflows, dashboards, geospatial maps, reporting interfaces, and one-click diagnostic access.
One-click equipment diagnostics
Reduced investigation effort by implementing drill-down workflows from asset status to subsystem measurements, historical trends, compliance checks, and failure-cause analysis.
Regional asset risk visibility
Improved operational visibility by building dashboards, severity indicators, geospatial views, and management reports that exposed equipment condition, wear-out rate, replacement timing, and regional KPIs.
Risk-based maintenance planning
Strengthened maintenance and replacement planning by connecting equipment-health indicators, wear-out analysis, and replacement forecasting with budgeting and operational workflows.
Modernization without downtime
Modernized reporting, ETL, visualization, and analytical workflows to support newer Smart Grid and security requirements while preserving an active production system.
Value we bring
Modernizing live industrial systems without downtime
We work on systems where modernization happens while operators are still relying on dashboards, diagnostics, alerts, reports, and historical workflows every day. Instead of separating modernization into a rewrite program, we tend to work incrementally inside the production system itself: stabilizing defects, replacing weak paths, refining ETL and reporting behavior, improving performance, and introducing new functionality while preserving operational continuity and expected system behavior.
Extending production systems under technical debt
Long-running systems accumulate tightly coupled logic, historical assumptions, reporting workarounds, SQL-heavy data processing, fragile integrations, and fixes layered over years of changing requirements. We work inside these constraints rather than around them: tracing dependencies across code, queries, ETL jobs, reports, and user workflows before introducing changes, so new functionality can be added without destabilizing parts of the system already in production.
Turning equipment telemetry into maintenance decisions
Connected equipment produces measurements; operational systems have to turn them into actions. We build software that sits between telemetry and operations by combining sensor data, historical reference values, diagnostics, health indicators, and analytical rules into workflows operators can actually use: asset health visibility, anomaly detection, alerts, risk assessment, maintenance prioritization, and replacement planning. The engineering challenge is usually not collecting the data, but making it usable for day-to-day operational decisions.
Technologies
- C#
- .NET MVC
- JavaScript
- Knockout.js
- MS SQL Server
- Jenkins
- Jira
- PowerShell
- SVN
- Windows Server
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