scholarly journals A Random Forest and Current Fault Texture Feature–Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR

2021 ◽  
Vol 9 ◽  
Author(s):  
Lei Kou ◽  
Xiao-dong Gong ◽  
Yi Zheng ◽  
Xiu-hui Ni ◽  
Yang Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Texture Feature ◽  
Texture Features ◽  
Current Sensor ◽  
Stable Operation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Three Phase ◽  
Feature Based

Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature–based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.

scholarly journals Fault Diagnosis of Sensors for T-type Three-Level Inverter-fed Dual Three-Phase Permanent Magnet Synchronous Motor Drives

2019 ◽  
Vol 4 (1) ◽  
pp. 167-178
Author(s):  
Xueqing Wang ◽  
Zheng Wang ◽  
Wei Wang ◽  
Ming Cheng
Keyword(s):  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Voltage Sensor ◽  
Current Sensor ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Voltage Difference ◽  
Speed Sensor ◽  
Three Phase

AbstractTo improve the reliability of motor system, this paper investigates the sensor fault diagnosis methods for T-type inverter-fed dual three-phase permanent magnet synchronous motor (PMSM) drives. Generally, a T-type three-level inverter-fed dual three-phase motor drive utilizes four phase-current sensors, two direct current (DC)-link voltage sensors and one speed sensor. A series of diagnostic methods have been comprehensively proposed for the three types of sensor faults. Both the sudden error change and gradual error change of sensor faults are considered. Firstly, the diagnosis of speed sensor fault was achieved by monitoring the error between the rotating speed of stator flux and the value from speed sensor. Secondly, the large high-frequency voltage ripple of voltage difference between the estimated voltage and the reference voltage was used to identify the voltage sensor faults, and the faulty voltage sensor was determined according to the deviation of voltage difference. Thirdly, the abnormal current amplitude on harmonic subspace was adopted to identify the current sensor faults, and the faulty current sensor was located by distinguishing the current trajectory on harmonic subspace. The experiments have been taken on a laboratory prototype to verify the effectiveness of the proposed fault diagnosis schemes.

scholarly journals Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2248
Author(s):  
Dimitrios A. Papathanasopoulos ◽  
Konstantinos N. Giannousakis ◽  
Evangelos S. Dermatas ◽  
Epaminondas D. Mitronikas
Keyword(s):  
Fault Diagnosis ◽  
Harmonic Component ◽  
Motor Drives ◽  
Brushless Dc Motor ◽  
Position Sensor ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Brushless Dc ◽  
Non Invasive ◽  
Dc Motor Drives

A non-invasive technique for condition monitoring of brushless DC motor drives is proposed in this study for Hall-effect position sensor fault diagnosis. Position sensor faults affect rotor position feedback, resulting in faulty transitions, which in turn cause current fluctuations and mechanical oscillations, derating system performance and threatening life expectancy. The main concept of the proposed technique is to detect the faults using vibration signals, acquired by low-cost piezoelectric sensors. With this aim, the frequency spectrum of the piezoelectric sensor output signal is analyzed both under the healthy and faulty operating conditions to highlight the fault signature. Therefore, the second harmonic component of the vibration signal spectrum is evaluated as a reliable signature for the detection of misalignment faults, while the fourth harmonic component is investigated for the position sensor breakdown fault, considering both single and double sensor faults. As the fault signature is localized at these harmonic components, the Goertzel algorithm is promoted as an efficient tool for the harmonic analysis in a narrow frequency band. Simulation results of the system operation, under healthy and faulty conditions, are presented along with the experimental results, verifying the proposed technique performance in detecting the position sensor faults in a non-invasive manner.

scholarly journals Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

2011 ◽  
Vol 2-3 ◽  
pp. 117-122 ◽  
Author(s):  
Peng Peng Qian ◽  
Jin Guo Liu ◽  
Wei Zhang ◽  
Ying Zi Wei
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Energy Analysis ◽  
Sensor Faults ◽  
Analysis Method ◽  
Sensor Fault ◽  
Fault Type ◽  
Ideal Tool ◽  
Non Stationary Signal

Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.

An ensemble feature method for food classification

2017 ◽  
Vol 26 (1) ◽  
pp. 13-39
Author(s):  
Niki Martinel ◽  
Christian Micheloni ◽  
Claudio Piciarelli
Keyword(s):  
Random Forest ◽  
State Of The Art ◽  
Texture Feature ◽  
Random Forest Classifier ◽  
Texture Feature Extraction ◽  
Feature Encoding ◽  
Food Recognition ◽  
Feature Based ◽  
Food Image ◽  
Comparative Results

In the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classification. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this specific task. In this work, we adopt a Random Forest classifier to measure the performances of different texture filter banks and feature encoding techniques on three different food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classifiers with other feature-based state-of-the-art solutions.

Gabor-oriented local order feature-based deep learning for face annotation

2019 ◽  
Vol 17 (05) ◽  
pp. 1950032
Author(s):  
A. Kasthuri ◽  
A. Suruliandi ◽  
S. P. Raja
Keyword(s):  
Semantic Network ◽  
Real Life ◽  
Texture Feature ◽  
Texture Features ◽  
Feature Representation ◽  
Local Order ◽  
Face Images ◽  
Novel Approach ◽  
The Face ◽  
Feature Based

Face annotation, a modern research topic in the area of image processing, has useful real-life applications. It is a really difficult task to annotate the correct names of people to the corresponding faces because of the variations in facial appearance. Hence, there still is a need for a robust feature to improve the performance of the face annotation process. In this work, a novel approach called the Deep Gabor-Oriented Local Order Features (DGOLOF) for feature representation has been proposed, which extracts deep texture features from face images. Seven recently proposed face annotation methods are considered to evaluate the proposed deep texture feature under uncontrolled situations like occlusion, expression changes, illumination and pose variations. Experimental results on the LFW, IMFDB, Yahoo and PubFig databases show that the proposed deep texture feature provides efficient results with the Name Semantic Network (NSN)-based face annotation. Moreover, it is observed that the proposed deep texture feature improves the performance of face annotation, regardless of all the challenges involved.

scholarly journals Proportional-Type Sensor Fault Diagnosis Algorithm for DC/DC Boost Converters Based on Disturbance Observer

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1412
Author(s):  
Kyunghwan Choi ◽  
Kyung-Soo Kim ◽  
Seok-Kyoon Kim
Keyword(s):  
Fault Diagnosis ◽  
Disturbance Observer ◽  
Estimation Error ◽  
State Observer ◽  
Operating Conditions ◽  
Feedback Gain ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Boost Converters ◽  
Diagnosis Algorithm

This study seeks an advanced sensor fault diagnosis algorithm for DC/DC boost converters governed by nonlinear dynamics with parameter and load uncertainties. The proposed algorithm is designed with a combination of proportional-type state observer and disturbance observer (DOB) without integral actions. The convergence, performance recovery and offset-free properties of the proposed algorithm are derived by analyzing the estimation error dynamics. An optimization process to assign the optimal feedback gain for the state observer is also provided. Finally, a fault diagnosis criteria is introduced to identify the location and type of sensor faults online using normalized residuals. The experimental results verify the effectiveness of the suggested technique under variable operating conditions and three types of sensor faults using a prototype 3 kW DC/DC boost converter.

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