Air cycle machine failure alert system
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Solution Overview
Problem
Existing aircraft air conditioning systems, particularly Air Cycle Machines (ACMs), face challenges in detecting failures, especially in compressor bearings or blades, which can lead to inadequate cabin pressure and uncomfortable flying conditions, often requiring costly and time-consuming maintenance.
Innovation Solution
A method and system for predicting ACM failures by calculating energy transfer and efficiency using existing sensors, without direct sensor measurements, and employing machine learning models to analyze airflow data, allowing for early detection of potential failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct sensor measurements are used to detect ACM failures, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses existing sensor measurements (temperatures, pressures, flow rates) as intermediaries to indirectly detect ACM failures. Instead of installing direct sensors on compressor bearings and blades, the system calculates efficiency metrics from intermediary measurements of airflow conditions before and after the compressor, thereby detecting failures without direct sensor contact with critical components.
Solution Approach 2:
The patent replaces direct mechanical sensing (physical sensors on moving parts) with a computational system that uses thermodynamic calculations. The system substitutes mechanical/direct measurement approaches with an information-processing approach that calculates compressor efficiency from remote sensor data, eliminating the need for complex direct measurement systems.
2Reliability
If proactive maintenance is implemented through failure prediction, then reliability is improved, but loss of time for maintenance operations increases
Solution Approach 1:
The system performs preliminary failure detection by continuously monitoring compressor efficiency and identifying degradation trends before actual failures occur. By detecting issues up to 30 cycles in advance, the system enables planned maintenance scheduling that can be coordinated with aircraft ground time, thereby reducing unplanned downtime and maintenance disruption.
Solution Approach 2:
The system implements continuous feedback monitoring of compressor performance parameters, comparing actual efficiency against expected values. This feedback mechanism provides early warning of degradation, allowing maintenance to be scheduled at convenient times rather than responding to sudden failures, thus optimizing the timing of maintenance operations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the prediction of ACM failures up to 30 cycles in advance, facilitating proactive maintenance and reducing downtime and maintenance costs by identifying specific failure types such as bearing or blade issues.
Implementation Method 1
calculating an energy transfer across a heat exchanger between a ram airflow and the ACM airflow
Data Source
AI summary
Disclosed herein is a method for failure prediction in an Air Cycle Machine (ACM). The method includes calculating a change in energy of an ACM airflow passing from an inlet of the ACM compressor to an outlet of the ACM compressor. The method may also include calculating a kinetic energy of the ACM compressor based on calculating the work of the ACM compressor on the ACM airflow as the ACM airflow passes through the ACM compressor, calculating the work of the ACM compressor based on the inlet temperature of the ACM airflow at the inlet of the ACM compressor, compressor pressure ratio of the ACM compressor, and a fluid property of the ACM airflow at the inlet of the ACM compressor. Additionally, the method can include calculating an ACM compressor efficiency as a ratio of the change in energy of the ACM airflow across the ACM compressor to the kinetic energy of the compressor. The method may further include predicting a failure state of the ACM compressor.