We aim to address the issues of difficult acquisition of bearing fault data, few feature data sets, and low efficiency of intelligent diagnosis. In this paper, an orthogonal wavelet transform K-nearest neighbor (OWTKNN) diagnosis method has been proposed. The (OWT) method extracts the peaks of each detail signal as training samples and uses the K-Nearest Neighbor (KNN) method for fault classification. The classification results of the multiple fault test data obtained through rolling bearing tests show that the method can reach a fault recognition rate of 100%, and compared with KNN without extracted eigenvalues, it significantly improves the classification effects from various unknown fault data of the bearing inner ring and ball, shortens classification time, and improves the intelligent diagnosis efficiency. In addition, it achieves an overall recognition rate exceeding 95%, Comparing OWT, EMD, and VMD feature extraction methods, both the OWTKNN and k-center point clustering algorithm do not exceed 80% (KCA), also bearing testimony of the effectiveness of this method.

The Gaussian mixture model (GMM) is an unsupervised clustering machine learning algorithm. This procedure involves the combination of multiple probability distributions to describe different sample spaces. Principally, the probability density function (PDF) plays a paramount role by being transformed into local linear regression to learn from unknown f failure samples, revealing the inherent properties and regularity of the data, and enhancing the subsequent identification of the operating status of the machine. The wavelet transform is a multiresolution transformation that can observe the signal gradually from coarse to fine, highlighting the localization analysis of nonstationary signals. Orthogonal wavelet transform selects the appropriate orthogonal wavelet function to transform so that the local characteristics of the signal in the time domain and frequency domain can be specifically described and the feature information of the original data can be mastered more effectively. In this study, a diagnostic method based on the Gaussian mixture model (OWTGMM) of orthogonal wavelet transform is proposed, in which orthogonal wavelet transform (OWT) is used to extract each detailed fault signal, the signal peak-to-peak value eigenvector is used as the construction model, and the GMM is used for fault classification. Based on the classification result from the rolling bearings’ test data, the use of detail signals extracted through OWT as the training data of the Gaussian mixture model promotes fast classification of bearing faults. Compared with the GMM without the extraction of the characteristic values, this method can reliably distinguish the categories of bearing faults about 100% of the time, which is consistent with the service life test chart. Furthermore, the unknown fault data is subject to classification with the orthogonal wavelet Gaussian model, and the bearing fault data is well distinguished, with an overall recognition rate of over 95%.

C6H9ClN2O7, triclinic, P 1 ‾ $\bar{1}$ (no. 2), a = 6.8667(4) Å, b = 7.5196(4) Å, c = 10.0908(5) Å, α = 74.237(2)∘, β = 81.603(3)∘, γ = 88.888(3)∘, V = 495.95(5) Å3, Z = 2, R gt (F) = 0.0377, wR ref (F 2) = 0.0902, T = 150(2) K.

Support vector data description (SVDD) is common supervised learning. Its basic idea is to establish a closed and compact area with the objects to be described as integrity. The described objects are all included within the area or as far as possible. In contrast, other objects are excluded out of the area as far as possible. The inherent nature and laws of data are subsequently revealed, thereby distinguishing the operation state of the machine. In this paper, an orthogonal wavelet transformation-support vector data description (OWTSVDD) is proposed to evaluate the performance of bearings, where the peak-to-peak value of detail signal is extracted through orthogonal wavelet transformation as the set of test samples, thus solving the distance R z from the set of test samples to the center of the sphere. Based on HI = R z 2 − R 2 , its distance to the hypersphere is calculated to judge whether it belongs to the normal state training samples. Finally, the performance and health of bearings are evaluated with HI. According to the classification of two sets of experimental data of rolling bearings, the proposed method better reflects the degeneration of bearing’s performance compared with the (SVDD) HI value without extraction of characteristic value, being entirely able to evaluate the entire life cycle of bearings from normal operation to fault and degradation. The HI evaluation result based on experimental data in Xi’an Jiaotong University is consistent with the life-cycle vibration signal of bearings, providing a scientific basis for production and equipment management and improving the prognostics technology-centered prognostics and health management (PHM).