Intelligent Dashboard for Gas Turbine Performance Prediction and Diagnosis: Machine Learning-Driven Energy Equipment Management

This article introduces the GitHub open-source project GT_Performance_Prognostics_Dashboard, an ML-based gas turbine performance analysis dashboard that offers real-time monitoring, performance prediction, anomaly detection, data visualization, and other functions. It helps the energy industry achieve equipment health management and predictive maintenance, addressing the high cost and high risk issues of traditional maintenance models.

Gas turbines are core equipment in energy systems, but they face high failure risks under extreme operating conditions. Traditional maintenance relies on periodic inspections (high cost, over-maintenance) and post-failure repairs (significant losses from unplanned downtime, tens of thousands of dollars per hour). Data-driven predictive maintenance technology can shift from passive repair to active prevention.

The core functions of the project include: 1. Real-time performance monitoring (connecting to sensor data streams); 2. Performance baseline modeling (baseline for ideal operating conditions); 3. Anomaly detection (automatic deviation identification); 4. Degradation prediction (trends of key parameters); 5. Visualization dashboard (interactive charts); 6. Report generation (automatic analysis reports).

Data Acquisition Layer: Monitors thermal (intake/exhaust temperature and pressure, etc.), mechanical (rotational speed, vibration, etc.), and performance (power, efficiency, etc.) parameters. Supports MQTT/OPC UA real-time streams, CSV/Parquet files, and SQL/InfluxDB database access. Data Processing Layer: Cleaning (missing value/anomaly handling), feature engineering (operating condition standardization, derived features), data alignment (time synchronization). Analysis Model Layer: Performance baseline (physical/data-driven/hybrid models), anomaly detection (statistical/ML/DL methods), degradation prediction (time series/ML/DL/survival analysis). Visualization Layer: Built with React/Vue + Plotly/D3, including core views like overview dashboard, performance curves, and heatmaps.

1. Performance deviation quantification: Calculate deviation rates of power, efficiency, etc., via ISO operating condition/load/aging correction.
2. Multivariate anomaly detection: Use PCA (dimensionality reduction, Hotelling T² statistic) and ICA (separate fault modes) to identify coupled changes.
3. Trend prediction and early warning: Trend extraction (moving average, STL decomposition), set multi-level warning thresholds, trigger early warnings in advance.

Power plant operation and maintenance: Monitor combined cycle performance, optimize parameters, predict component degradation, and develop maintenance plans. Aviation engines: Monitor during flight, evaluate after landing, predict remaining life, and support condition-based maintenance. Industrial drives: Monitor drive system efficiency, predict bearing/gearbox failures, and optimize load distribution.

Features: Open-source and scalable (custom access/models/interfaces), modular design (decoupled for easy integration), industrial-grade features (high concurrency, fault tolerance, security). Limitations: Dependent on data quality (sensor drift affects results), complex operating conditions (startup/shutdown increase modeling difficulty), scarce fault samples (limits supervised learning), insufficient model interpretability (DNN decisions are opaque).

Future directions: Digital twin integration, edge computing deployment, federated learning (collaborative training under privacy protection), knowledge graph (root cause analysis), AR/VR visualization. Conclusion: This project provides a reference for intelligent operation and maintenance of gas turbines, lowers technical barriers, promotes industry collaboration and innovation, and is worth the attention of energy sector engineers and data scientists.