Milling tool wear state recognition plays an important role in controlling the quality of milled parts and reducing machine tool downtime. However, the characteristics of milling process limit the accuracy and stability of tool condition monitoring (TCM) employing vibration signals. To improve this problem, this paper explores the use of vibration signals as sensing approach for recognizing tool wear states during milling operation by using the stacked generalization (SG) ensemble model. In this study, vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time‐frequency domain to extract signal features. The support vector machine recursive feature elimination (SVM‐RFE) algorithm is used to select the main features which are most relevant to tool wear states. The SG ensemble model based on SVM, decision tree (DT), naive Bayes (NB), and SG ensemble strategy is constructed to recognize tool wear states. The proposed method is experimental verified, and the results show that the recognition accuracy of the established SG ensemble model is 98.74% and the overall G‐mean and AUC evaluation value of the model is 0.98 and 0.98, respectively. In addition, compared with other ensemble models and single models, the SG ensemble model based on vibration signals has better recognition accuracy and stability than other models.

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.

Ball screws play a significant role in maintaining transmission accuracy and stability. Accurate prediction of the remaining useful life (RUL) for the ball screw can provide reliable decision-making information for predictive maintenance. However, the RUL prediction of ball screws with the precision indicator under time-varying operating conditions (OCs) is rarely involved. Moreover, obtaining data for the whole life cycle of each OC is impractical due to time and expenditure constraints. To address the aforementioned issues, this article proposes a hybrid approach for RUL prediction under time-varying OCs. First, a time-varying stochastic model considering the precision indicator is established. Then, the data-driven algorithm is proposed to estimate the unknown parameters with limited data. Finally, the effectiveness of the proposed method in prognostics is experimentally validated through the designed platform and compared with the commonly used methods.

This work presents an ergonomic and interactive human-robot collaboration (HRC) framework, through which new collaborative skills are extracted from a one-shot human demonstration and learned through Riemannian dynamic movement primitives (DMP). The proposed framework responds to human-robot interaction forces to adapt to the task requirements, while generating virtual “ergonomic forces” that guide the human toward more ergonomic postures, based on online monitoring of a kinematics-based index. The resulting motion is then integrated into the learned task trajectories. The framework is implemented on a mobile manipulator with a weighted whole-body Cartesian velocity controller, which meets the needs of large-scale HRC. To evaluate the proposed framework, a multi-subject experiment involving a human-robot co-carrying task is conducted. The performance of the ergo-interactive control in terms of task performance and ergonomics adaptation is verified under different experimental conditions. This is followed by a comparative statistical analysis. The experimental results show that the learned trajectory can be reproduced and generalized to several targets and adjusted online according to human preferences and ergonomics.