The operation status of bearings plays a critical role in the mechanical systems. Current models for condition monitoring of bearings tend to overlook the complex properties of the process, including its non-Gaussian, nonlinear, and dynamic characteristics. To address these issues, this paper proposes a novel approach for condition monitoring based on a novel dynamic cumulative sum (CUSUM) chart with an integrated indicator. The integrated indicator accounts for the non-Gaussian, nonlinearity, and dynamic properties of the process by utilizing optimal components that are decomposed via independent component analysis (ICA) and its extended versions. The developed dynamic CUSUM chart can adapt to the variation of the process. The proposed condition monitoring strategy is validated using the simulation and experiment data of bearings. Results demonstrate that the proposed method yields a significant improvement in hit rates and a reduction in false alarms compared to conventional methods.

Machine learning-based classification strategy has been successfully applied in actual industrial monitoring but it is often hindered when the data set is imbalanced. Technically, the misclassification phenomenon, as a serious performance degradation of generalisation ability, often occurs in minority class. For this problem, Borderline-Synthetic Minority Over-sampling Technique (B-SMOTE), which aims to enrich the quantity of minority samples around decision boundaries, has received considerable attention. However, most imbalanced classification techniques under the framework of B-SMOTE generate instances by a random weight number from 0 to 1, which may result in an authentic reduction of newly-born samples. Herein, a novel over-sampling strategy, which aims to provide new safety criteria and reassign the threshold of weight coefficient, is proposed to boost the authenticity of generated samples and classification accuracy. In addition, light gradient boosting machine (LightGBM) is adopted to build the classification model. Related experiments show the effectiveness and superiority of the proposed method in handling imbalanced classification tasks.

A synchronized Cu-cantilever structure, used for characterizing the kinetic friction, is proposed by integrating frictional motion with synchronized amplification for the first time in this paper. A theoretical expression is further derived to predict the entrainment range of synchronization under periodically variable perturbation, rather than the invariable one. A two-cantilever resonator system, mainly consisting of a long cantilever and a short one coupled by an overhang, is designed in this paper. A friction block is fixed on the free end of the long cantilever to contact with target surfaces. The kinetic friction force representing a periodically varying perturbation is applied to the long cantilever via the friction block. The resonance frequency shift of the long cantilever, found to be related to the kinetic friction force, is magnified multiple times by that of the short cantilever. This not only implies the feasibility of applying the synchronized amplification to periodically variable perturbation but also improves the sensitivity of detecting such kind of perturbations. If both the frictional pair and tribological conditions are fixed, the abovementioned periodically variable perturbation, kinetic friction force in this paper, tribologically correlates to the surface feature, which is an invariable parameter like surface roughness and surface hardness. This paper validates that the proposed method based on the synchronized amplification is capable of characterizing extremely small differences in invariable parameters which cannot be detected by conventional imaging methods, especially those out of identification limits (like no change in color or surface feature) of conventional imaging methods.

In recent years, flexible sensors based on laser-induced graphene (LIG) have played an important role in areas such as smart healthcare, smart skin, and wearable devices. This paper presents the fabrication of flexible sensors based on LIG technology and their applications in human–computer interaction (HCI) systems. Firstly, LIG with a sheet resistance as low as 4.5 Ω per square was generated through direct laser interaction with commercial polyimide (PI) film. The flexible sensors were then fabricated through a one-step method using the as-prepared LIG. The applications of the flexible sensors were demonstrated by an HCI system, which was fabricated through the integration of the flexible sensors and a flexible glove. The as-prepared HCI system could detect the bending motions of different fingers and translate them into the movements of the mouse on the computer screen. At the end of the paper, a demonstration of the HCI system is presented in which words were typed on a computer screen through the bending motion of the fingers. The newly designed LIG-based flexible HCI system can be used by persons with limited mobility to control a virtual keyboard or mouse pointer, thus enhancing their accessibility and independence in the digital realm.