The concept of a digital twin is increasingly appearing in industrial applications, including the field of predictive maintenance. A digital twin is a virtual representation of a physical system containing all data available on site. This paper presents condition monitoring of ventilation systems through the digital twin approach. A literature review regarding the most popular system faults is covered. The motor current signature analysis is used in this research to detect system faults. The physical system is further described. Then, based on the free body diagram concept and Newton’s second law, the equations of motion are obtained. Matlab/Simulink software is used to build the digital twin. The Concordia method and the Fast Fourier Transform analysis are used to process the current signal, and physical and numerical system current measurements are obtained and compared. In the final step of the modeling, specific frequencies were adjusted in the twin to achieve the best simulation. In addition, a statistical approach is used to create a complete diagnostic protocol.

In this article, two methods for broken bar detection in induction motors are considered and tested using data collected from the LIAS laboratory at the University of Poitiers. The first approach is Motor Current Signature Analysis (MCSA) with Convolutional Neural Networks (CNN), in which measurements have to be processed in the frequency domain before training the CNN to ensure that the resulting model is physically informed. A double input CNN has been introduced to perform a 100% detection regardless of the speed and load torque value. A second approach is the Principal Components Analysis (PCA), in which the processing is undertaken in the time domain. The PCA is applied on the induction motor currents to eventually calculate the Q statistic that serves as a threshold for detecting anomalies/faults. Even if obtained results show that both approaches work very well, there are major differences that need to be pointed out, and this is the aim of the current paper.

This article presents a mechanical fault diagnosis methodology in synchronous machines using only a single current measurement in variable speed conditions. The proposed methodology uses order tracking in order to sample the analysis signal as a function of the rotor angle. The spectrum of the signal is then independent of speed and it could be employed in frequency analysis. Order tracking is usually applied using rotor position measurement. In this work, the proposed method uses one current measurement to estimate the position as well as the analysis signal (rotation speed). Furthermore, a statistical approach is used to create a complete diagnosis protocol. At variable speed and with only one current measurement the diagnosis is challenging. However, order tracking will allow simpler analysis. The method is proved in simulations and experimental set-up.

The simulation and experimental study of new IP (integrator proportional) controlled DC bus voltage of three phase shunt active power filter (APF), to improve power quality by compensating harmonics and reactive power required by nonlinear load is proposed. The compensation process is based on sensing mains currents only, an approach different from conventional methods, which require harmonics or reactive volt-ampere values of the load. The not sinusoidal mains voltage form problem is resolved by using PLL system. PWM signal generation is based on hysteresis control comparators to obtain the switching signals. Various simulation and experimental results are presented under steady state and transient conditions

This article deals with the detection of mechanical faults in synchronous machines from single current measurement at variable speed. The proposed approach is based on an order tracking method in which the analysis signal is sampled as a function of the mechanical angle. In this case, the spectral components become independent of the speed and the frequency analysis can be exploited. Order tracking is generally implemented from a position measurement. In this work we present a method that allows us to estimate this position and the analysis signal from only one current measurement. The proposed approach allows an intuitive adjustment of the algorithm parameters. Secondly, a statistical method is used to finalize the diagnosis. At variable speed, this type of method is difficult to implement and we show that order tracking makes it possible to simplify the analysis. The procedure is tested in simulation and on a experimental test bench.