Intelligent diagnosis of aviation bearings based on frequency domain features
A fault diagnosis model based on feature extraction was proposed for aeroengine rolling bearing fault diagnosis.The original vibration signals were preprocessed by envelope demodulation,and only 512 points of each segment of data were taken as fault features,and used as input of bidirectional long short-term memory(BiLSTM)model to diagnose the inner ring faults,outer ring faults,rolling body faults and three different fault degrees corresponding to each fault.The model made up for not only the disadvantages of long input data and obscure features caused by the original vibration signal input,but also the uncertainty of fault diagnosis by extracting vibration features manually.Experiments on the open data set of rolling bearings showed that the accuracy of fault identification was above 99.8％.An aero-bearing tester was built to verify the method and model.BiLSTM based on feature extraction can diagnose the bearing faults more accurately.The proposed method has important engineering value for the fault diagnosis of aeroengine rolling bearings.
fault diagnosisfeature extractionneural networkrolling bearingbidirectional long short-term memory
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