Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. However, current methods of tool fault diagnosis lack accuracy. Therefore, in the present paper, a fault diagnosis method was proposed based on stationary subspace analysis (SSA) and least squares support vector machine (LS-SVM) using only a single sensor. First, SSA was used to extract stationary and non-stationary sources from multi-dimensional signals without the need for independency and without prior information of the source signals, after the dimensionality of the vibration signal observed by a single sensor was expanded by phase space reconstruction technique. Subsequently, 10 dimensionless parameters in the time-frequency domain for non-stationary sources were calculated to generate samples to train the LS-SVM. Finally, the measured vibration signals from tools of an unknown state and their non-stationary sources were separated by SSA to serve as test samples for the trained SVM. The experimental validation demonstrated that the proposed method has better diagnosis accuracy than three previous methods based on LS-SVM alone, Principal component analysis and LS-SVM or on SSA and Linear discriminant analysis.
Recently, deep learning has developed rapidly in the fault diagnosis technology of axial piston pumps. However, when the training data is scarce and the label information is insufficient, many traditional intelligent fault diagnosis models are invalid. To solve these problems, an intelligent fault diagnosis method for axial piston pumps is proposed based on deep convolutional generative adversarial network (DCGAN). Firstly, the continuous wavelet transform (CWT) and DCGAN are designed to enhance the fault features and expand dataset, respectively. Secondly, according to the number of labeled samples, DCGAN and semi-supervised GAN (SGAN) are used to extract the deep features of the image domain. Finally, the clustering algorithm is used to classify the extracted features to realize the fault diagnosis of the axial piston pump bearing. To verify the feasibility of the proposed method, experimental investigation and public dataset are adopted. When the evaluation indicators of the clustering results are close to 1, the proposed method shows the advantages of high diagnostic accuracy, superior generalization ability and excellent anti-noise ability.
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