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Abstract: This paper presents a dynamic model of a counter flow water to oil heat exchanger when all inputs (inlet temperatures of the fluids and the mass flow rates) are simultaneously varying. Although interesting results about modeling of heat exchanger by linear parameter varying (LPV) can be found in , several problems remain to be solved such as the structure estimation or a proper initial MISO model for the optimization algorithms. This paper introduces a new model structure called quasi LPV model which simulates accurately the temperature and flow transients in a counter flow heat exchanger (COFHX). The quasi LPV model is compared to a realistic numerical model of a counter flow heat exchanger adjusted with the test rig heat exchanger of the University of Valenciennes in France. Comparisons indicate that the developed quasi LPV model is capable of predicting the transient performance of the heat exchangers satisfactorily. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]