Ndubuisi Chibuogwu is an aviation safety inspector, regulatory compliance expert, and data analytics professional with over a decade of experience in airworthiness inspection, aircraft maintenance, and aviation safety systems. Recently, he sat down for an interview to discuss his motivation and work on a Predictive Maintenance System for General Aviation — an innovation aimed at improving aviation safety and reliability by addressing long-standing challenges in maintenance practices.

In this conversation, Chibuogwu shares insights into the inspiration behind the project, how the system works, and what it promises for the future of aviation.

What is the Predictive Maintenance System for General Aviation all about?

The Predictive Maintenance System for General Aviation is designed to transform aviation operations by helping aircraft operators predict and prevent failures before they occur. This system combines artificial intelligence, regulatory alignment, and real-world data to address the gaps I have seen in traditional maintenance practices. My motivation comes from seeing how current methods sometimes fall short in preventing accidents and equipment failures. The project is currently in the testing phase with partners here in Houston, TX, as we gather live data and validate the system on operational aircraft.

What is wrong with the widely adopted aviation maintenance framework?

The main framework in use today, called MSG-3, is too rigid in many ways. It focuses heavily on preventing catastrophic failures but often overlooks smaller defects that can still cause serious issues. It also requires parts to be replaced at fixed intervals, which sometimes means replacing components that are still in good condition. This leads to unnecessary costs and wasted resources.

Are these limitations what inspired you to pursue a Predictive Maintenance System as an alternative?

Yes, that is exactly what inspired me. I wanted to create a smarter, data-driven alternative. At the heart of this system are machine learning algorithms — Random Forest models for rapid failure prediction and Long Short-Term Memory (LSTM) networks for analyzing patterns over time. Combined with sensor data from aircraft, these tools help forecast potential issues and support maintenance scheduling at the right time. This enables operators to make precise decisions that align with safety regulations while saving time and cost.

Beyond the technical sophistication, what other approach does the system have?

The system focuses on practicality. It uses a strong database to manage aircraft sensor data, model predictions, and maintenance alerts, helping track the condition and movements of aircraft. The interactive dashboard makes the information easy to understand, giving operators clear visuals on component health, early warnings on possible issues, and links to manufacturer guidance for maintenance actions.

What have you done to bring this Predictive Maintenance System for General Aviation into reality?

To bring this vision to life, I am advancing strategic partnerships in Houston. I have arrangements with West Houston Airport as the main test location and am working with a local biplane operator for data collection and system trials. We are also exploring partnerships with flight schools and maintenance organizations to expand live testing.

To what extent will the partnership advance the system?

These partnerships provide valuable data and facilities for testing the system in real-world conditions. We’re aiming to start pilot testing in October 2025. Our timeline is ambitious but realistic: refining synthetic data by July, securing additional pilot collaborators by August, and launching real-world testing between September and December. The pilot phase will help us fine-tune the system, gather feedback from operators, and prepare for broader deployment and regulatory engagement. Since some aircraft types will require retrofitted IoT devices to collect sensor data, we are also preparing to seek Supplemental Type Certificate (STC) approval where necessary to ensure compliance with FAA certification standards.

What is your future projection with this system?

Looking ahead, I see this system helping general aviation worldwide shift toward smarter, data-driven maintenance. This will reduce unexpected failures, improve safety, lower costs, and provide a model for other countries to follow — similar to how the U.S. pioneered ADS-B technology.

What will success be like for you with this system?

Success for me means seeing measurable reductions in mechanical failures, helping modernize aviation safety rules, creating new high-skill jobs, and establishing predictive maintenance as a global standard in aviation.

By combining advanced technology with practical tools and regulatory alignment, this Predictive Maintenance System offers a promising path to safer, more efficient general aviation operations. With live testing already underway in Houston, the project has the potential to reshape aircraft maintenance practices and set new safety benchmarks for the general aviation industry both in the U.S. and globally.