Virtual Sensors for Electromobility

2018 ◽  
Author(s):  
Maria Pia Fanti ◽  
Massimiliano Nolich ◽  
Michele Roccotelli ◽  
Walter Ukovich
Keyword(s):  
Virtual Sensors

Virtual Sensors for Fault Diagnosis: A Case of Induction Motor Broken Rotor Bar

2020 ◽  
pp. 1-1
Author(s):  
Zahra Hosseinpoor ◽  
Mohammad Mehdi Arefi ◽  
Roozbeh Razavi-Far ◽  
Niloofar Mozafari ◽  
Saeede Hazbavi
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Virtual Sensors ◽  
Broken Rotor Bar

scholarly journals Automated Classification of Microwave Transmitter Failures Using Virtual Sensors

2019 ◽  
Author(s):  
Ayax D. Ramirez ◽  
Stephen D. Russell ◽  
David W. Brock ◽  
Narayan R. Joshi
Keyword(s):  
Automated Classification ◽  
Virtual Sensors

Inverse model-based virtual sensors for detection of hard faults in air handling units

2021 ◽  
pp. 111493
Author(s):  
Narges Torabi ◽  
H. Burak Gunay ◽  
William O'Brien ◽  
Ricardo Moromisato
Keyword(s):  
Inverse Model ◽  
Virtual Sensors ◽  
Model Based ◽  
Air Handling Units

Input Vector Identification and System Model Construction by Average Mutual Information

2000 ◽  
Author(s):  
Paul B. Deignan ◽  
Peter H. Meckl ◽  
Matthew A. Franchek ◽  
Salim A. Jaliwala ◽  
George G. Zhu
Keyword(s):  
Mutual Information ◽  
System Model ◽  
Input Output ◽  
Output Data ◽  
Average Mutual Information ◽  
Virtual Sensors ◽  
Input Signals ◽  
Model Independent ◽  
Intelligent Model ◽  
Selection Of

Abstract A methodology for the intelligent, model-independent selection of an appropriate set of input signals for the system identification of an unknown process is demonstrated. In modeling this process, it is shown that the terms of a simple nonlinear polynomial model may also be determined through the analysis of the average mutual information between inputs and the output. Average mutual information can be thought of as a nonlinear correlation coefficient and can be calculated from input/output data alone. The methodology described here is especially applicable to the development of virtual sensors.

scholarly journals Multi-Frequency Based Direction-of-Arrival Estimation for 2q-Level Nested Radar & Sonar Arrays

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3385 ◽  
Author(s):  
Hao Zhou ◽  
Guoping Hu ◽  
Junpeng Shi ◽  
Ziang Feng
Keyword(s):  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Research Area ◽  
Frequency Separation ◽  
Frequency Method ◽  
Virtual Sensors ◽  
Order Difference ◽  
Sonar Systems ◽  
Minimum Number

Direction finding is a hot research area in radar and sonar systems. In the case of q ≥ 2, the 2qth-order cumulant based direction of arrival (DOA) estimation algorithm for the 2q-level nested array can achieve high resolution performance. A virtual 2qth-order difference co-array, which contains O(N2q) virtual sensors in the form of a uniform linear array (ULA), is yielded and the Gaussian noise is eliminated. However, some virtual elements are separated by the holes among the 2qth-order difference co-array and cannot be fully used. Even though the application of the multi-frequency method for minimum frequency separation (MFMFS) can fill the holes with low computation complexity, it requires that the number of frequencies must increase with the number of holes. In addition, the signal spectra have to be proportional for all frequencies, which is hard to satisfy when the number of holes is large. Aiming at this, we further propose a multi-frequency method for a minimum number of frequencies (MFMNF) and discuss the best frequency choice under two specific situations. Simulation results verify that, compared with the MFMFS method, the proposed MFMNF method can use only one frequency to fill all the holes while achieving a longer virtual array and the DOA estimation performance is, therefore, improved.

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