Fault Prediction of High-Altitude Civil Aviation Aircraft Air Conditioning System Based on SSA-LSTM
In the complex climatic environment of high-altitude airline,the overall performance requirements for onboard equipment are higher.Under conditions of low temperature,low pressure,and strong ultraviolet radiation,the aging of critical aircraft components is accelerated.Domestic airlines usually adopt a post-maintenance approach to aircraft upkeep.In response to the traditional post-maintenance method,which involves long response times,high costs,and potential safety risks,and which makes it difficult to manually control the maintenance cycle and depth accurately,a big data-driven method for predicting failures in aircraft air conditioning systems is proposed.This method introduces the Sparrow Search Algorithm-Long Short-Term Memory(SSA-LSTM)network.Utilizing data collected by the Wireless Quick Access Recorder(WQAR),and comparing it with two other prediction methods,the results demonstrate a clear advantage of the SSA-LSTM.By predicting and identifying potential early faults,this method supports the shift from post-maintenance to preventive maintenance for airlines.
SSA-LSTM neural networkWQARaircraft air conditioning systemproactive maintenancehigh-altitude airlinecivil aviation aircraft
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