The prediction of bearing remaining useful life (RUL) plays a pivotal role in ensuring the safe operation of machinery and reducing maintenance loss. Traditional prediction methods only consider the features of one domain or integrate the features of multiple domains into a one-dimensional sequence as the model input, which leads to some inaccuracy in prediction. In order to improve the prediction accuracy, a bearing RUL prediction method based on the parallel deep residual convolution neural network (P-ResNet), which is considered both time-domain features and time–frequency features, is proposed in this paper. Synchronous wavelet transform (SWT) is adopted to extract time–frequency features from original vibration signals. Both the time domain features and time–frequency domain features after dimension reduction by PCA are used as input to P-ResNet, which contains two series of parallel convolution operations to learn the time–frequency features and time-domain features, respectively, to ensure the comprehensiveness of information-bearing degradation. The residual layers were added to enhance the learning ability of time–frequency features. Kalman filter algorithm was used to smooth the prediction results. The IEEE PHM 2012 Data Challenge datasets were used as data sources for model training and prediction. Compared with the traditional convolutional neural network (CNN), the P-ResNet model maintains the synchronization of global and local information and has a stronger learning ability. The experiment data validate the effectiveness of the proposed method, and the comparison between the prosed methods and the others proves the superiority of the proposed method.

Influence of geometric and cutting parameters of cemented carbide cutting tool on reliability of cutting tool has become more and more mature, yet influence of its physical and material parameters on reliability is still blank. In view of this, cutting test and fatigue crack growth test of YT05 cemented carbide cutting tool are conducted to measure such data as the original crack size, growth size, times of impact loading, number and time of cutting tool in failure, and stress distribution of cutting tool is also obtained by simulating cutting process of tools. Mathematical models on dynamic reliability and dynamic reliability sensitivity of cutting tool are derived respectively by taking machining time and times of impact loading into account, thus change rules of dynamic reliability sensitivity to physical and material parameters can be obtained. Theoretical and experimental results show that sensitive degree on each parameter of tools increases gradually with the increase of machining time and times of impact loading, especially for parameters such as fracture toughness, shape parameter, and cutting stress. This proposed model solves such problems as how to determine the most sensitive parameter and influence degree of physical parameters and material parameters to reliability, which is sensitivity, and can provide theoretical foundation for improving reliability of cutting tool system.

In light of the increasing demand for the reliability analysis of self-recovery products, with features of limited storage period and multistage degradation, a reliability evaluation model in whole life cycle is proposed. The degradation process comprises one storage phase and two working phases. On the basis of the idea of competitive failure, the shock process and the feature of self-recovery were introduced into the model. Furthermore, the problem of it being difficult to add variables to a reliability model is solved with the use of the Stieltjes integral. The influences of the parameters of model reliability are analyzed, and the results demonstrate that the new model could adequately describe the competing failure process. The model also exhibited certain feasibility and theoretical reference values.

Graph-based two-stage algorithms have widely developed and achieved good performance to detect salient objects. For these algorithms, choosing the proper seeds using for saliency propagation is quite crucial and difficult. In this paper, we consider using background/foreground probability values of candidate background/foreground seeds as the estimation of the reliable seeds, not considering the refinement of candidate seeds. Moreover, these probability values are integrated into the proposed saliency models, which can avoid hard filtering for candidate seeds as well as simplify the procedure of the algorithm. In addition, considering the manifold structure of an image, we fuse the manifold-preserving term into the saliency models. Especially, reconstruction matrix A is determined based on the deep features extracted from FCN-32s, which can further improve detection performance of salient objects. The results of experiments in which the proposed SBFMP algorithm is applied to four datasets demonstrate SBFMP algorithm is prior to some existing state-of-the-art algorithms in terms of the different metrics.