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Effectiveness of online bearing status monitoring (OBSM) depends deeply on the online data processing ability and the sensitivity of data features used to recognize the mechanical-system dynamic response change. Focusing on these, we present a novel method of OBSM based on singular spectrum analysis (SSA) and adaptive neuro-fuzzy inference system (ANFIS) with the highlights as follows. A sensitive and stable multi-feature is discovered to better the ability to distill the valuable information in noisy and massive databases (NMDs) and process impulse-noise in them. The SSA-based high-frequency noise removal solution, the ANFIS' interpolating and identifying capability, and the dual function of the proposed multi-feature are combined in a new algorithm named AfOBSM for building a system of OBSM through two phases, offline and online. The offline is to identify the mechanical-system in the presence of the typical kinds of bearing faults. The ANFIS is trained in this phase using a training dataset. Meanwhile, the online is to estimate online the real status of the bearing(s) based on the trained ANFIS and a monitoring dataset. Surveys from an experimental-system were performed. The obtained results showed the positive effects of the AfOBSM. [ABSTRACT FROM AUTHOR]

The effectiveness of control of the active railway suspension system (ARSS) using a magnetorheological damper (MRD) with unknown track profile and load depends deeply on (1) the control strategy and the ability to adapt to noise, (2) system’s response delay compared with the real status of track profile impacting on it, and (3) uncertainty of the model used to describe the ARSS and external disturbance. Deriving from these, in order to improve the control efficiency, in this paper, we focus on three following factors. The first is to improve the accuracy of the MRD model. The second is to establish the ability to predict the track-profile’s status to update adaptively the optimal parameters of the control system. Finally, it is to build an uncertainty and disturbance observer (DUO) to compensate for noise. A novel algorithm for fuzzy C-means clustering (FCM) in an overlapping data space deriving from the Kernel space and the data potential field named PKFCM is proposed. Based on the PKFCM, the inverse MRD model is established as well as the design of a fuzzy-based predicting sliding controller (FPSC) for the ARSS is performed which is always updated by the optimal parameters adapting to the status of track profile. The stability of the FPSC is proved theoretically while its performance is estimated by numerical surveys. [ABSTRACT FROM AUTHOR]

Reality shows that 1) the effectiveness of compensators for uncertainty and disturbance (UAD) depends deeply on UAD's time varying rate (TVR), and 2) controlling a system over a network introduces different constraints and conditions, in which some of these are variable delays in control signal, packet losses, data quantization, safety, and security. This paper presents a new design of fuzzy sliding mode control (FSMC) enhanced by compensation for UAD using a disturbance observer (DO), named DO-FSMC, for a class of nonlinear systems subjected to UAD. First, in order to weaken partly the negative influence of TVR of UAD on the compensation effectiveness, we separate explicitly unidentified aspects into two groups, one related to the model error while the other coming from external disturbances, to distinctly consider. To stamp out the chattering status and reduce calculating cost, we propose an adaptive gain updated directly based on the sliding surface convergence status, to which two new control laws, one for the FSMC and the other for the DO-FSMC, are given via Lyapunov stability analysis. In order to evaluate the DO-FSMC, simulations as well as surveys based on a real semi-active suspension system using a Magnetorheological damper (MRD) with measured datasets are performed. The results obtained from the surveys coincide with the theoretical analysis which show that the competence to stamp out vibration is the advantage of the proposed method compared with the other published methods. [ABSTRACT FROM AUTHOR]

In this study, a useful indirect cutting force measurement method using an acceleration sensor and current hall sensors is proposed. A series of experiments was performed on a precise micro machining stage with a high-speed spindle. A three-axis acceleration sensor was installed on the spindle head, and current hall sensors were connected to the motor current inlet cables. From the results obtained, the correlations of the tool teeth rotation and current amplitude showed a linearity of 92.0% precision for hall sensor signals, and 98.0% precision for acceleration sensor signals. Even though the results using the acceleration sensor showed better linearity than those of the current hall sensors, the signals can be easily affected by chattering, spindle vibration, and other external disturbances. From this perspective, the current hall sensor can provide more robust results. [ABSTRACT FROM AUTHOR]

The objective of this research is to develop an efficient reverse engineering method of 3-dimensional compound surfaces for rapid prototyping processes. As a first step, the image-processing techniques were applied to scanned data obtained using a laser scanner for edge detection. Based on the detected edges, the boundary information of the compound surface was extracted in order to divide the surface into several separate regions. As a next step, the Delaunay triangulation method was applied in order to independently reconstruct the surface on the basis of the scanned data and the boundary information. Finally, STL files were created for the rapid prototyping process. Required simulations and experiments were performed and the results were analyzed to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]