Transforming Aerospace Reliability

About the Customer

Orion Aerospace Ltd. is a prestigious aerospace company with a robust track record in rocket technology and space missions. Established in 1998, the company has grown to become a significant player in the global space industry. Known for its innovative rocket designs and reliable satellite launch services, Orion Aerospace Ltd. operates with a commitment to advancing space exploration and supporting commercial satellite deployments.

Company Name: Orion Aerospace Ltd.
Headquarters: Tokyo, Japan
Industry: Aerospace and Satellite Launch Services

Challenges

Orion Aerospace Ltd. encountered several critical challenges that jeopardized its mission success:

High Financial Losses:
- Estimated Loss Per Failed Launch: $30 million USD.
- Financial Impact: The high cost of each failure significantly affected the company’s profitability and financial stability.
Operational Delays:
- Project Timelines: Frequent failures led to delays in satellite deployments and mission schedules, impacting client contracts and future revenue.
Reputational Damage:
- Market Impact: Repeated failures damaged the company’s reputation, eroding client trust and affecting competitive standing in the aerospace market.
Technical Setbacks:
- Extended Troubleshooting: Each failure required extensive troubleshooting, redesigns, and re-testing, consuming additional resources and extending the time between launches.

Project Objectives

The project aimed to address the following objectives:

Predict Failure Likelihood:
- Develop models to estimate the probability of a successful rocket launch based on historical and real-time data.
Identify Key Factors:
- Analyze and pinpoint critical variables affecting launch outcomes, such as environmental conditions, mechanical factors, and operational procedures.
Optimize Launch Conditions:
- Determine optimal launch windows and conditions to maximize success chances.
Provide Actionable Insights:
- Offer recommendations for risk mitigation and process improvements to enhance launch reliability and operational performance.

Approach

Flivo.ai’s approach was methodical and multi-faceted, involving several key steps:

Data Collection and Integration

Data Sources:
- Historical Launch Data: Detailed records of past launches, including rocket specifications, payload details, fuel compositions, and outcomes.
- Telemetry Data: Real-time data from sensors during launches, capturing key metrics like temperature, pressure, and engine performance.
- Weather Data: Environmental conditions such as wind speed, humidity, and temperature.
- Maintenance Logs: Records of inspections, maintenance, and component health.
Data Preparation:
- Data Cleaning: Addressed missing values and inconsistencies using imputation and cross-referencing with other data sources.
- Data Integration: Unified datasets based on common identifiers to create a comprehensive dataset for analysis.
- Outlier Management: Identified and handled outliers to maintain the accuracy and reliability of the analysis.

Exploratory Data Analysis (EDA)

Trend Analysis: Investigated historical success and failure rates to identify trends and patterns.
Correlation Analysis: Examined relationships between variables such as weather conditions, engine health, and launch outcomes.
Failure Analysis: Analyzed root causes of failures, focusing on mechanical issues, weather conditions, and operational errors.
Weather Impact: Specific weather conditions, such as high wind speeds and severe temperature fluctuations, were strong predictors of failure.
Engine Health: Certain telemetry indicators were closely correlated with engine performance issues, contributing to failures.

Model Building and Evaluation

Logistic Regression: Applied for binary classification to predict launch success or failure.
Random Forest: Utilized to handle complex interactions and non-linear relationships within the data.
Gradient Boosting (GBM): Improved model performance by combining multiple models and focusing on areas where previous models performed poorly.
Evaluation Metrics:
- Accuracy: Measured the overall correctness of predictions.
- Precision and Recall: Evaluated the models’ ability to correctly identify successful and failed launches.
- Cross-Validation: Ensured that models generalized well across different data subsets.

Optimization and Implementation

Launch Scheduling: Recommended optimal launch windows based on WRI and real-time data.
Pre-launch Recommendations: Suggested maintenance and inspection actions based on model predictions.
Real-Time Alerts: Integrated predictive models with telemetry systems to provide real-time risk management alerts.

Key Features of the Solution

Predictive Modeling: Advanced models forecasting launch success probabilities based on a comprehensive dataset.
Real-Time Risk Management: Dynamic alerts and recommendations derived from real-time data and predictive insights.
Comprehensive Metrics: Detailed insights into critical factors affecting launch success, including weather conditions and engine health.

Future Scope

AI-Powered Optimization: Real-time adjustments to flight trajectories and launch parameters based on predictive insights.
Collaborative Research: Opportunities for partnerships with space agencies to develop and test new technologies.
Enhanced Predictive Models: Incorporation of additional data sources and advanced modeling techniques to further improve accuracy and reliability.

Results

12% - 4%

Reduced Failure Rate

$120 million USD

Cost Savings

Rebuilt market

Reputation Enhancement

Conclusion

Flivo.ai’s collaboration with Orion Aerospace Ltd. demonstrates the transformative potential of data science in addressing complex challenges in the aerospace industry. By leveraging predictive analytics and advanced modeling, we significantly improved launch reliability, reduced financial losses, and enhanced operational efficiency. As we continue to innovate, Flivo.ai remains dedicated to advancing data-driven solutions and contributing to the future of aerospace technology.