Mass Laplacian Discriminant Analysis and Its Application in Gear Fault Diagnosis

Abstract

Fault diagnosis is essentially the identification of multiple fault modes. How to extract sensitive features and improve diagnostic accuracy is the key to fault diagnosis. In this paper, a new manifold learning method (Mass Laplacian Discriminant Analysis, MLDA) is proposed. Firstly, it is assumed that each data point in the space is a point with mass, and the mass is defined as the number of data points in a certain area. Then, the idea of universal gravitation is introduced to calculate the virtual universal gravitation between data points. Based on the Laplace eigenmaps algorithm, the gravitational Laplacian matrix between the same kind of data and the heterogeneous data is obtained, and the discriminant function is constructed by the ratio of the virtual gravitation between the heterogeneous data and the virtual gravitation between the similar data; the projection function with the largest discriminant function value is the optimal feature mapping function. Finally, based on the mapping function, the eigenvalues of the training data and the test data are calculated, and the softmax algorithm is used to classify the test data. Experiments on gear fault diagnosis show that this method has higher diagnostic accuracy than other manifold learning algorithms.

A Fault Diagnosis Method for Out‐of‐Round Faults of Metro Vehicle Wheels with Strong Noise

“…AlShorman et al reviewed condition monitoring and fault diagnosis based on sound and acoustic emission for four types of faults: bearings, rotor, stator, and compound [12]. Mass Laplacian discriminant analysis was used to diagnose faults of gear and had a higher diagnostic accuracy [13]. Yang et al proposed a novel multilayer domain adaptation method to diagnose the compound fault and single fault of multiple sizes simultaneously [14].…”