User Guide

Welcome to the User Guide for TurboGuard, your comprehensive toolkit for time series anomaly detection and forecasting on turbofan engine data. This guide walks you through the essential components to help you get started, understand the core functionality, and customize the toolkit to your specific needs.

Overview

TurboGuard is designed to provide a complete solution for turbofan engine health monitoring and predictive maintenance:

  • Comprehensive data preprocessing for turbofan data

  • Advanced feature engineering with automated feature selection

  • Multiple model architectures including LSTM, and Autoencoder networks

  • Dual-mode anomaly detection using both forecasting residuals and reconstruction errors

  • Interactive visualization suite with dashboards, heatmaps, and diagnostic plots

  • Flexible configuration system supporting multiple deployment scenarios

Getting Started

Before diving into the individual components, ensure you have installed TurboGuard and its dependencies. This guide assumes you have:

  • Python 3.9+ environment

  • Required packages installed via pip install -r requirements.txt

  • Access to CMAPSS dataset or similar turbofan engine data

  • Basic understanding of time series analysis and machine learning concepts

Core Components

Installation & Setup

Complete installation guide including environment setup, dependency management, and initial configuration for different deployment scenarios.

Data Preprocessing

Comprehensive data handling workflows covering:

  • CMAPSS dataset loading and validation

  • Custom data format adapters

  • Data quality assessment and cleaning

Feature Engineering

Advanced feature extraction and selection:

  • Sensor data transformations

  • Statistical feature derivation

  • Domain-specific turbofan features

  • Automated feature selection algorithms

  • Feature importance analysis

Model Training

Detailed training workflows for multiple architectures:

  • Forecasting Model: LSTM

  • Autoencoder Models: Various architectures for reconstruction-based anomaly detection

  • Training optimization, hyperparameter tuning, and validation strategies (next version of the project)

Anomaly Detection

Multi-layered anomaly detection system:

  • Threshold-based detection using statistical methods (also a dynamical but not integrate in the application yet)

  • Forecasting residual analysis for prediction-based anomalies

  • Reconstruction error analysis for pattern-based anomalies

  • Adaptive thresholding for evolving operational conditions

Forecasting

Prediction capabilities:

  • Multi-horizon predictions

  • Uncertainty quantification

  • Prediction performance monitoring

Visualization

Rich visualization ecosystem:

  • Interactive dashboards for data monitoring

  • Anomaly heatmaps for temporal pattern analysis

  • Model performance plots including training curves and validation metrics

  • Feature importance visualizations

  • Comparative analysis tools for multiple engines or time periods

Configuration

Flexible configuration management:

  • YAML-based configuration for all components

  • Environment-specific settings (development, staging, production)

  • Model configuration templates for common use cases

  • Logging and monitoring configuration

Troubleshooting

Common issues and solutions:

  • Installation problems and dependency conflicts

  • Data loading errors and format issues

  • Model training failures and performance optimization

  • Memory and computational considerations

  • Deployment and scaling guidance

Advanced Features

  • Multi-engine monitoring with fleet-wide anomaly detection

  • Transfer learning for adapting models to new engine types

  • Online learning for continuous model updates

  • A/B testing framework for model comparison

  • Integration hooks for external monitoring systems

Support & Community

  • Documentation: Comprehensive guides and documentation

  • Examples: Jupyter notebooks with real-world use cases

  • GitHub Issues: Bug reports and feature requests

  • Community Forum: Discussion and user support

  • Professional Support: Enterprise consulting and custom development

Note

For detailed implementation examples and tutorials, see the accompanying Jupyter notebooks in the examples/ directory.