ThirdEye has developed an open-loop system backed by complex machine learning algorithms to control the manufacturing system in real time. The algorithms are built to predict whether a product is likely to be defective, in which case the manufacturing system is adjusted to prevent the defect. Additionally, the algorithms predict whether a machine is likely to fail, in which case the machine is taken offline for preventive maintenance.

1. Component Health Assessment:

   • The system ingests sensor data from various sources on the helicopters, such as vibration sensors, temperature sensors, and performance data.
   • AI models analyze this sensor data to identify subtle changes that may indicate a component is nearing failure. This allows for early detection of potential issues before they escalate into critical problems.

2. Remaining Useful Life (RUL) Prediction:

   • The system doesn’t just detect anomalies; it goes a step further by predicting the remaining useful life (RUL) of a component.
   • Machine learning models are trained on historical data, including past maintenance records, component performance data, and industry benchmarks. This allows the model to predict with high accuracy how many operational hours a component has left before failure.

3. Repair Cycle Optimization:

   • By analyzing historical maintenance data and component specifications, the system can estimate the maximum number of repairs a component can undergo before needing replacement.
   • This helps optimize maintenance strategies. For example, if a component is nearing its maximum repair threshold, replacing it proactively could prevent a potential in-flight failure.

Integration with Natural Language Processing (NLP):

As mentioned before, the project is currently focusing on integrating NLP capabilities.

• Extracting Knowledge from Documents:
  AI models are trained to process technical documentation, such as maintenance manuals and component specifications. This allows the system to automatically extract critical information like component lifespans, recommended maintenance procedures, and failure indicators.

• Enriching Data for Deeper Insights:
  The extracted data from documents is then combined with sensor data from the helicopters. This creates a richer dataset that fuels the AI models, leading to more accurate predictions and comprehensive maintenance insights.

By combining sensor data analysis, RUL prediction, repair cycle optimization, and NLP integration, this solution provides a holistic approach to predictive maintenance for public sector aerospace and defense applications.