A Nonintrusive and In-Service Motor-Efficiency Estimation Method Using Air-Gap Torque With Considerations of Condition Monitoring

2008 ◽  
Vol 44 (6) ◽  
pp. 1666-1674 ◽  
Author(s):  
Bin Lu ◽  
Thomas G. Habetler ◽  
Ronald G. Harley
Keyword(s):  
Condition Monitoring ◽  
Estimation Method ◽  
Air Gap ◽  
Motor Efficiency ◽  
Efficiency Estimation

Estimation of the Automated Video Control Device’s Target Functions Performance Probability

2021 ◽  
Vol 26 (3-4) ◽  
pp. 291-301
Author(s):  
N.V. Stepanov ◽  
Keyword(s):  
Estimation Method ◽  
Target Function ◽  
Video Camera ◽  
Optimal Receiver ◽  
Efficiency Estimation ◽  
Use Efficiency ◽  
Target Environment ◽  
Receiver Model ◽  
Video Control

Operating quality of automated video control systems depends on optical specifications of video camera and peculiar features of video algorithm. Specified target function performance probability can serve as criterion of automated video control use efficiency. In this work, a new performance efficiency estimation method for automated equipment of target environment video control is suggested: to estimate the probability of target functions’ (object detection, capture, and auto tracking) performance. Theoretical prediction of target functions performance probability was built upon Johnson’s criterion and the use of optimal receiver model. The results of suggested method’s experimental verification have shown that target detection occurred when signal/noise ratio level was above 6. This level can be regarded as low value to ensure that object is detected with probability 0.9.

A utilizing efficiency estimation method for wave-driven unmanned surface vehicle

2018 ◽  
Vol 164 ◽  
pp. 650-660 ◽  
Author(s):  
Peng Liu ◽  
Yumin Su ◽  
Fushun Liu ◽  
Yebao Liu ◽  
Jianhua Zhang ◽  
...  
Keyword(s):  
Estimation Method ◽  
Unmanned Surface Vehicle ◽  
Efficiency Estimation

scholarly journals Non-Invasive Method for In-Service Induction Motor Efficiency Estimation Based on Sound Acquisition

2020 ◽  
Vol 10 (11) ◽  
pp. 3757
Author(s):  
Júlio César da Silva ◽  
Thyago Leite de Vasconcelos Lima ◽  
José Anselmo de Lucena Júnior ◽  
Gabriela Jordão Lyra ◽  
Filipe Vidal Souto ◽  
...  
Keyword(s):  
Embedded System ◽  
Motor Speed ◽  
Monitoring Methods ◽  
Increase Energy Efficiency ◽  
Non Invasive ◽  
System A ◽  
Motor Efficiency ◽  
Efficiency Estimation ◽  
Invasive Method ◽  
The Fourier Transform

Induction motors (IMs) are present in practically all production processes and account for two-thirds of the energy consumption in industrial settings. Therefore, monitoring them is essential to prevent accidents, optimize production, and increase energy efficiency. Monitoring methods found in the literature require a certain level of invasiveness, causing some applications to be unfeasible. In the present study, a new completely non-invasive method implemented in an embedded system performs the embedded processing of the sound signal emitted by an in-service IM to estimate speed, torque, and efficiency. Motor speed is estimated from the analysis in the frequency domain using the Fourier Transform. Torque and efficiency are estimated from the speed and motor nameplate information. To perform the tests and validate the proposed method/system, a workbench with a controllable torque was used. The workbench was also equipped to allow the results to be compared with the airgap torque method. The results indicate a high accuracy for the nominal load (error of approximately 1%) in the measurement of the efficiency and torque, and a mean relative error of 0.2% for the speed.

Feature-Based Tool Condition Monitoring in a Gear Shaving Application

2008 ◽  
Author(s):  
A. J. Brzezinski ◽  
Y. Wang ◽  
D. K. Choi ◽  
X. Qiao ◽  
J. Ni
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Feature Fusion ◽  
Estimation Method ◽  
Principal Component ◽  
Time Frequency ◽  
Tool Condition ◽  
Tool Wear Estimation ◽  
Artificial Neural Network Ann ◽  
Feature Dimension

Condition monitoring (CM) is an effective way to improve the tool life of a cutting tool. However, CM techniques have not been applied to monitor tool wear in an industrial gear shaving application. Therefore, this paper introduces a novel, sensor-based, data-driven, tool wear estimation method for monitoring gear shaver tool condition. The method is applied on an industrial gear shaving machine and used to differentiate between four different tool wear conditions (new, slightly worn, significantly worn, and broken). This research focuses on combining, expanding, and implementing CM techniques in an application where no previous work has been done. In order to realize CM, this paper discusses each aspect of CM, beginning with data collection and pre-processing. Feature extraction (in the time, frequency, and time-frequency domains) is then explained. Furthermore, feature dimension reduction using principal component analysis (PCA) is described. Finally, feature fusion using a multi-layer perceptron (MLP) type of artificial neural network (ANN) is presented.

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