Don’t let unplanned engine failures disrupt your operations and compromise safety. Adopt our solution today and move from reactive to predictive maintenance, saving both time and money.
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A forward-looking approach to aircraft engine preservation
Predictive maintenance for turbofan engines
Artificial intelligence (AI) and Machine Learning in predictive maintenance, combined with condition status monitoring and health status monitoring, have changed the engine maintenance landscape. By tracking critical engine performance parameters for ensuring engine health like thrust, fuel flow, exhaust gas temperature (EGT), and compressor discharge pressure, unplanned and costly shutdowns transform into strategic, well-timed maintenance activities. The cornerstone of this transformation is the ability to accurately predict a range of turbofan engine failures using advanced Machine Learning algorithms.
This ability enables stakeholders to make data-driven decisions about the timing and nature of maintenance interventions. Since aircraft engines play a critical role in both safety and operational performance the need for reliable predictive models, powered by AI, is pressing. Moreover, predictive maintenance goes beyond just ensuring engine operational efficiency; it is pivotal in minimizing emissions and ensuring that engines operate within environmentally sustainable parameters.
The challenge
Collect usable data
The task of accurately forecasting turbofan engine failures hinges on inherent challenges related to data quality and associated operational risks:
Obtaining real-time data for actual failures is fraught with risk, including the risk of an engine crash. As a result, most of the data used to train models is generated in the lab under controlled conditions and is less reflective of real-world scenarios.
The solution
From sequence analysis to fleet-wide predictions
Our solution is a customized software model designed to mitigate the challenges outlined above. Features of our model include:
Advanced sequence analysis: Our model includes the ability to accept sequences, which are time series representations of sensor signal vectors. This allows the model to detect and identify trends and patterns within the signals to improve the quality of the data used to make predictions.
Data filtering: The model filters out irrelevant data, focusing only on significant values for more accurate predictions.
Data Points: We accrue a variety of engine condition parameters collected during the flight, including pressure, vibration, and oil temperature.
Remaining life prediction: The model foretells the remaining useful life (RUL) of turbofan engines, integrating condition status monitoring insights.
Prediction for multiple engines: Our system can make predictions of current quality for an entire fleet of engines based on the latest sensor data, flagging engines that are likely to fail soon due to discrepancies in critical engine performance parameters.
Trend prediction: The model also provides a trend prediction feature that uses a range of sensor data to increase confidence in the future performance of specific engines.
Transparency: We provide transparent access to the raw prediction data, enabling further analysis and refinement by the end user.