Abstract
Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value.
Highlights
Energy consumption [1], energy saving [2,3], fault diagnosis [4], and their problem-solving method is getting more and more attention for rotating machinery [5,6,7]
In the context of the above problems and research, in order to use the vibration signal of gearboxes to diagnose the compound fault of gearboxes more effectively, this paper proposes a precise diagnosis method of the compound fault of gearboxes based on multi-feature and BP-AdaBoost
A diagnosis method for the compound fault of gearboxes based on the combination of multi-feature vectors composed of sensitive time domain characteristic parameters and band energy characteristics by wavelet packet decomposition and BP AdaBoost algorithm is proposed
Summary
Energy consumption [1], energy saving [2,3], fault diagnosis [4], and their problem-solving method is getting more and more attention for rotating machinery [5,6,7]. In the context of the above problems and research, in order to use the vibration signal of gearboxes to diagnose the compound fault of gearboxes more effectively, this paper proposes a precise diagnosis method of the compound fault of gearboxes based on multi-feature and BP-AdaBoost. Perform wavelet packet analysis on the reconstructed signal, calculate the waveband energy of each frequency band by wavelet packet decomposition, construct band energy feature vectors, and form gearbox state feature vectors with the selected time domain feature parameters; Establish the BP-AdaBoost model, select training samples and test samples, and use the training samples to train the model; Input the test samples into the trained BP-AdaBoost model to obtain the fault diagnosis results.
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