1st
Dare to Dream 2.0
National Innovation Contest
SITUATION
- Gas turbine failures cause catastrophic downtime and safety issues in aerospace and power generation
- Existing monitoring systems were reactive rather than predictive
- At Modelicon InfoTech, we identified a critical gap in failure prediction capabilities
- The key challenge: Absence of real failure data made traditional ML approaches ineffective
- Power plants and aerospace companies had extensive normal operation data but almost no failure examples
- This data imbalance created a fundamental barrier to developing reliable prediction models
Figure 1: Dare to Dream 2.0 Innovation Contest
TASK
- Develop an innovative approach to predict gas turbine failures without sufficient real failure data
- Create a solution that could transform the field of predictive maintenance
- Design a methodology that would work with existing sensor data while overcoming the data imbalance
- Produce a proof-of-concept for the "Dare to Dream 2.0" national innovation contest
- Apply cutting-edge deep learning techniques to a critical industrial problem
CHALLENGE
No failure data for training
INNOVATION
GAN for synthetic data
IMPLEMENTATION
LSTM for prediction
RESULTS
Early failure detection
GAN
Generative Adversarial Network
Creates synthetic failure data
Precursor to today's GenAI
LSTM
Long Short-Term Memory
Analyzes time-series data
Foundation for Transformers
VIGnAN Tool
Integration platform
Diagnostic & Prognostic
Real-time monitoring
THE CHALLENGE
- Extreme imbalance between normal and failure data
- Failure events are rare but catastrophic
- Traditional ML requires balanced datasets
- Time-series nature of sensor data adds complexity
- Real-world testing is prohibitively expensive
→
THE INNOVATION
- GAN architecture to generate synthetic failure patterns
- LSTM networks to learn temporal dependencies
- Feature extraction using AutoEncoders
- Similarity-based comparison for early warning
- Remaining Useful Life (RUL) prediction
ACTION
- Developed a revolutionary approach using Generative Adversarial Networks (GANs) to create synthetic failure data
- Designed a dual architecture system combining GANs with LSTM networks for time-series prediction
- Created the VIGnAN (Vibration Intelligent Gas turbine Adversarial Network) Diagnostics & Prognostics Tool
- Implemented feature extraction techniques to identify failure precursors in sensor data
- Trained the system to recognize early signs of degradation that would lead to failure
- Validated the model against industry benchmarks and limited available failure cases
- Prepared and presented the solution for the "Dare to Dream 2.0" national innovation contest
- Demonstrated how modern AI techniques could solve a critical industrial problem
Figure 2: The GAN-LSTM hybrid architecture developed for generating synthetic failure data and performing early prediction
DARE TO DREAM 2.0 - DRDO Innovation Contest
RESULTS
- Won First Place in the nationwide "Dare to Dream 2.0" contest in the startup/Data-driven health monitoring category
- Created a breakthrough methodology for predictive maintenance without requiring actual failure data
- Achieved early detection of potential failures up to 500 operating hours before critical issues would emerge
- Developed a solution that could potentially save millions in downtime costs and improve safety in aerospace applications
- Pioneered an approach that is now seen as a precursor to modern GenAI and Transformer-based models
- Established Modelicon InfoTech as an innovator in the predictive maintenance field
- Created intellectual property with significant commercial potential for aerospace and power generation industries
"Transformed the impossible challenge of predicting failures without failure data through innovative application of GANs and LSTMs, demonstrating how creative AI solutions can solve critical industrial problems and winning national recognition."