| Summary: | Aiming at the problem that the vibration signal in diesel engine fault diagnosis is non-stationary and nonlinear, and the original signal is directly input into the Convolutional Neural Network（CNN） for fault diagnosis with poor effect, a new method based on PCA-EDT-CNN is proposed. Firstly, use Principal Component Analysis（PCA） to adaptively reduce the original data collected by the sensor, and construct a qualified Principal Component Eigenvector Matrix（PCEM）; secondly, perform Euclidean Distance Transformation（EDT） on PCEM, calculate the Euclidean distance between each row and construct the Euclidean Distance Matrix（EDM）; finally, flatten PCEM and EDM into one-dimensional vectors and synthesize a one-dimensional sample sequence, input into One-Dimensional Convolutional Neural Network（1 DCNN） to train and diagnosis the model. A diesel engine preset failure test bench was built to verify the effectiveness of the method, and through comparison with traditional methods, the results show that the method has high accuracy in diagnosing different fault states of diesel engines and has practical engineering application value.
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