This article reports the design and validation of a novel predefined-time barrier function adaptive sliding-mode control (PTBFASMC) strategy for robust control of disturbed systems. The PTBFASMC strategy is established by integrating the time base generator along with the barrier function. Unlike existing similar works, the proposed method enables global predefined-time convergence, i.e., the system trajectory returns to the ultimate bound even if an escape occurs at a certain time instant. Besides, the convergence time can be predefined by the user, which is independent of the initial conditions and disturbance. Moreover, the reaching phase is eliminated and the magnitude of initial control output is zero. Another attractive feature of the proposed method lies in that the ultimate bound can be predefined, i.e., the ultimate bound is independent of the upper bound of disturbance. To avoid large control magnitude, a modified control strategy is provided, which extends the proposed scheme to different scenarios. The stability of the control system is demonstrated, and its superiority is verified through numerical simulations and experimental investigations on a piezoelectric actuator.

Machining chatter is a self-excited vibration between the tool and the workpiece that affects surface quality and productivity. In this article, we propose a novel approach for early detection of machining chatter based on multivariate variational mode decomposition (MVMD) and chatter correlation factor (CCF). The proposed approach is featured by early chatter detection, and it is not susceptible to machining parameters. In order to obtain effective intrinsic mode functions (IMFs) and avoid mode mixing problem in MVMD, an automatic selection method of MVMD's parameters based on the difference of correlation coefficients (DCC) is developed. By using MVMD, multichannel signals are decomposed into multiple IMF groups with mode-alignment to extract common frequency components across all channel signals. Because multichannel signals are highly correlated at the chatter frequency when chatter occurs, the proposed CCF derived by correlation coefficients between the selected chatter-sensitive IMFs is sensitive to early chatter and can simultaneously measure the level of chatter. A support vector machine chatter identification model is obtained based on the CCF. The effectiveness of the proposed method is demonstrated by simulation and experimental findings.

Convolutional neural network (CNN)-based intelligent fault diagnosis approaches have showcased remarkable performance in the assessment of machine safety. The data monitored from mechanical systems in industries is primarily characterized by class imbalance. Nevertheless, most of the current CNN-based models are designed under the assumption of balanced sample distributions, which do not align with the prevalent conditions observed in real industrial scenarios. To tackle this challenge, a state-of-the-art multiattention-based feature aggregation convolutional network (MFACN) is developed in this study. The key contributions of this study are outlined as follows: 1) this study designs an attention-based multiscale module (AMM) and a multiscale feature aggregation module (MFAM) to facilitate comprehensive learning across multiple levels; 2) a robust CNN model based on AMM and MFAM is established. The constructed model can explore abundant information from mechanical signals; and 3) a dual focal loss (DFL) function is introduced to enhance diagnostic results under conditions of data imbalance. To assess the applicability of the proposed MFACN in machine health state identification, two experiments were conducted using the bearing dataset and the planetary gearbox dataset. The experimental results unequivocally show that MFACN surpasses seven other state-of-the-art approaches, especially when dealing with imbalanced datasets.