Asynchronous Motor Fault Detection Circuit Design

2014 ◽  
Author(s):  
Zongming Li ◽  
Baichao An ◽  
Haoyu Lu ◽  
Qiang Liu ◽  
Liying Yuan
Keyword(s):  
Fault Detection ◽  
Circuit Design ◽  
Asynchronous Motor ◽  
Detection Circuit

scholarly journals Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode

2012 ◽  
Vol 52 (9-10) ◽  
pp. 1781-1786 ◽  
Author(s):  
R. Possamai Bastos ◽  
F. Sill Torres ◽  
G. Di Natale ◽  
M. Flottes ◽  
B. Rouzeyre
Keyword(s):  
Fault Detection ◽  
Low Power ◽  
Current Sensor ◽  
Sleep Mode ◽  
Transient Fault ◽  
Detection Circuit

scholarly journals Circuit Design of Surface Acoustic Wave Based Micro Force Sensor

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Yuanyuan Li ◽  
Wenke Lu ◽  
Changchun Zhu ◽  
Qinghong Liu ◽  
Haoxin Zhang ◽  
...  
Keyword(s):  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Circuit Design ◽  
Force Measurement ◽  
Pressure Sensors ◽  
Force Sensor ◽  
Piezoresistive Sensor ◽  
Detection Circuit ◽  
Micro Force Sensor ◽  
The Stability

Pressure sensors are commonly used in industrial production and mechanical system. However, resistance strain, piezoresistive sensor, and ceramic capacitive pressure sensors possess limitations, especially in micro force measurement. A surface acoustic wave (SAW) based micro force sensor is designed in this paper, which is based on the theories of wavelet transform, SAW detection, and pierce oscillator circuits. Using lithium niobate as the basal material, a mathematical model is established to analyze the frequency, and a peripheral circuit is designed to measure the micro force. The SAW based micro force sensor is tested to show the reasonable design of detection circuit and the stability of frequency and amplitude.

scholarly journals Multiclass Incremental Learning for Fault Diagnosis in Induction Motors Using Fine-Tuning with a Memory of Exemplars and Nearest Centroid Classifier

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Magdiel Jiménez-Guarneros ◽  
Jonas Grande-Barreto ◽  
Jose de Jesus Rangel-Magdaleno
Keyword(s):  
Fault Detection ◽  
Incremental Learning ◽  
Induction Motors ◽  
Asynchronous Motor ◽  
Feature Space ◽  
Fine Tuning ◽  
Electromechanical Systems ◽  
Critical Data ◽  
Public Datasets ◽  
Centroid Classifier

Early detection of fault events through electromechanical systems operation is one of the most attractive and critical data challenges in modern industry. Although these electromechanical systems tend to experiment with typical faults, a common event is that unexpected and unknown faults can be presented during operation. However, current models for automatic detection can learn new faults at the cost of forgetting concepts previously learned. This article presents a multiclass incremental learning (MCIL) framework based on 1D convolutional neural network (CNN) for fault detection in induction motors. The presented framework tackles the forgetting problem by storing a representative exemplar set from past data (known faults) in memory. Then, the 1D CNN is fine-tuned over the selected exemplar set and data from new faults. Test samples are classified using nearest centroid classifier (NCC) in the feature space from 1D CNN. The proposed framework was evaluated and validated over two public datasets for fault detection in induction motors (IMs): asynchronous motor common fault (AMCF) and Case Western Reserve University (CWRU). Experimental results reveal the proposed framework as an effective solution to incorporate and detect new induction motor faults to already known, with a high accuracy performance across different incremental phases.

scholarly journals Automatic Fault Detection Circuit for Integrated Gate Drivers of Active-Matrix Displays

2018 ◽  
pp. 1-1
Author(s):  
Byung-Chang Yu ◽  
Jongbin Kim ◽  
Seung-Hyuck Lee ◽  
Hoon-Ju Chung ◽  
Seung-Woo Lee
Keyword(s):  
Fault Detection ◽  
Active Matrix ◽  
Detection Circuit ◽  
Active Matrix Displays ◽  
Gate Drivers
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