In industrial applications, the vibration and temperature measurements of rolling element bearings are known as two popular condition monitoring methods. The previously published method for remaining useful life (RUL) prediction has been limited to using the vibration signal. However, a single signal source cannot fully reflect the degradation trend of bearings, influencing the RUL prediction precision. In this paper, a novel general log-linear Weibull (GLL-Weibull) model is constructed by considering vibration and temperature condition monitoring signals to estimate the model parameters. During the feature extraction stage, the relative root mean square (RRMS) is derived from the monitored vibration signal, and the relative temperature trend value is extracted from the monitored temperature signal to eliminate individual differences in bearings and random signal fluctuations. Then, a fuzzy operator is introduced to describe the degree of an “overheated bearing” and “excessive bearing vibrations.” During the RUL prediction stage, both the extracted vibration and temperature characteristics are used to create the GLL-Weibull model. The best parameters are attained by employing the maximum likelihood estimation approach. The algorithm performance is checked with criteria like the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The effectiveness and superiority of the presented approach are validated by two real-life prognosis cases. According to the experimental results, the presented approach provides superior prediction precision and lower computational cost than other approaches for bearings under constant or variable operating conditions.

Recently, various deep learning models, which are mainly based on data-driven algorithms, have received more and more attention in the field of intelligent fault diagnosis and prognostics. However, there are two major assumptions accepted by default in the existing studies: 1) The training (source domain) and testing (target domain) data sets obey the same feature distribution; 2) Sufficient labeled data with fault information is available for model training. In real industrial scenarios, especially for different machines, these assumptions are mostly invalid, which makes it a huge challenge to build reliable diagnostic model. Motivated by transfer learning, we present a novel intelligent method named deep transfer network (DTN) with multi-kernel dynamic distribution adaptation (MDDA) to address the problem of cross-machine fault diagnosis. In the proposed approach, the DTN has wide first-layer convolutional kernel and several small convolutional layers, which is utilized to extract transferable features across different machines and suppress high frequency noise. Then, the MDDA method constructs a weighted mixed kernel function to map different transferable features to a unified feature space, and the relative importance of the marginal and conditional distributions are also evaluated dynamically. The proposed method is verified by three transfer learning tasks of bearings, in which the health states of wind turbine bearings in real scenario are identified by using diagnosis knowledge from two different bearings in laboratories. The results show that the proposed method can achieve higher diagnosis accuracy and better transfer performance even under different noisy environment conditions than many other state-of-the-art methods. The presented framework offers a promising approach for cross-machine fault diagnosis. INDEX TERMS Deep transfer network, multi-kernel dynamic distribution adaptation, cross-machine fault diagnosis, transfer learning, bearings.

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Aiming at the problem that the classification effect of support vector machine (SVM) is not satisfactory due to improper selection of penalty factor C and kernel parameter g, this paper proposes a new modified classifier that uses the improved particle swarm optimization (IPSO) to optimize the parameter of SVM (IPSO-SVM) by introducing the dynamic inertia weight, global neighborhood search, population shrinkage factor and particle mutation probability. The classification result is tested by Common data sets named BreastTissue、 Heart and Wine from the Libsvm toolbox, the results show that IPSO-SVM classifier is obviously superior to SVM and PSO-SVM classifier in terms of prediction accuracy and classification time. Then it is applied to the fault diagnosis in two classification problems and multiple classification problems of rolling bearings. The simulation results show that the IPSO-SVM classifier has stronger global convergence ability and faster convergence speed, and the ideal classification results can be obtained. Finally, the IPSO-SVM classifier is used to diagnose the fault of the actual bearing. The results show that the classifier has a better classification stability and is worthy of further promotion in engineering field.

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