Many advanced product manufacturing approaches have been introduced in the market in recent years. Thus, it is critical to develop modern techniques which can effectively familiarize budding minds with the latest manufacturing procedures. In fact, the contemporary training methods and advanced education practices are crucial to uphold the interest of the new generation as well as to equip them with state-of the art systems. There is a need for innovative ideas and effective methodologies to inculcate the desired competency and prepare students for prospective manufacturing set ups. In the latest Industry 4.0 paradigm, visualization technologies, especially virtual reality, have been emphasized to sustainably train and educate young students. This work presents a technique for utilizing the leading visualization method based on virtual reality in product manufacturing. It aims to acquaint students with the prominent concept of Industry 4.0, the reconfigurable manufacturing system (RMS). The RMS has been a demanding topic for the novice and, most often, amateurs are not able to grasp and interpret it. Therefore, this paper outlines the various steps that can be useful for students in order to anticipate the RMS design, interact with it, understand its operation, and evaluate its performance.

In this paper, the fault detection (FD) problem of uncertain discrete-time switched systems under the constrained switching law and external disturbances is addressed. In the multiple Lyapunov functions (MLF) framework with ℋ-/ℋ _∞ performance index, a robust fault detection filter (RFDF) is designed in order to generate a residual signal which is sensitive to the faults and robust against the disturbances. The design method is formulated to be solved by linear matrix inequality (LMI) technique. The main contribution in this paper is to improve the performance of the FD for switched systems by considering switch detection logics. The design of the RFDF is based on the local performance index of each subsystem. An adaptive threshold is set based on both, the bounds of disturbances and the individual performance index for each subsystem.

This study presents the methodology for detecting single phasing fault of three-phase induction motors in steady-state operation based on acoustic sound signal analysis. In the proposed methodology, the signal analysis based on the Root Mean Square (RMS) parameter is used as a comparison between a healthy and a faulty motor in the time-domain. Whereas for the frequency-domain, the Fast Fourier Transform (FFT) with the Average power parameter is used. The hypothesis test is used to prove the statistical significance of the difference between the calculated parameters of the faulty and healthy three-phase induction motors. To illustrate the functionality of the proposed technique many tests have been carried out on a real machine. The results show that integrating motor sound with the traditional control system can increase the protection and reliability of three-phase induction motors.

This paper addresses the problem of design a fault detection system for switched systems with unknown inputs. The residual generator based robust fault detection filter (RFDF) will be used, where the switching signal is assumed to be known, and the continuous states will be estimated, resulting a generation of residual signals for each linear model. Dwell-time constraint will be used to ensure the stability for the switched system. The RFDF design is formulated as optimization problem, and solved iteratively by linear matrix inequality (LMI). Stability is analyzed by using the switched Lyapunov functions. Finally a practical example from lateral vehicle dynamics is provided to illustrate the functionality of the proposed technique.