scholarly journals Robust Fault Diagnosis and Adaptive Parameter Identification for Single Phase Transformerless Inverters

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Jian Li ◽  
Xinxin Guo ◽  
Bo Li
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Parameter Identification ◽  
Single Phase ◽  
Fault Identification ◽  
Luenberger Observer ◽  
Adaptive Parameter ◽  
Wide Range ◽  
Gradient Descent Algorithm ◽  
Simulation Results

The paper presents the theoretical analysis and simulation verification of robust fault diagnosis and adaptive parameter identification for single phase transformerless inverters. The fault diagnosis is composed of two parts, fault detection and fault identification. In the fault detection part, a Luenberger observer is designed to realize the detection of faults. Then, we apply a bank of observers to identify the location of faults. Meanwhile, the fault identification observers based estimation along with a gradient descent algorithm are also used in the parameter identification to estimate the actual values of components in a single phase transformerless inverter. Not only we develop the design methodology for the robust fault diagnosis and adaptive parameter identifier but also we present simulation results. The simulation results show the effectiveness of fault diagnosis and the accurate tracking of changes in component parameters for a wide range.

scholarly journals Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7278
Author(s):  
Tito G. Amaral ◽  
Vitor Fernão Pires ◽  
Armando J. Pires
Keyword(s):  
Image Processing ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Power Plants ◽  
Principal Component ◽  
Processing Algorithm ◽  
Maximum Efficiency ◽  
Image Processing Algorithm ◽  
Wide Range ◽  
Pv Modules

Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based on a new image processing algorithm to determine the PV slopes is proposed. The fault detection is obtained comparing the slopes of several modules. This algorithm is based on a new image processing approach in which principal component analysis (PCA) is used. Instead of using the PCA to reduce the data dimension, as is usual, it is proposed to use it to determine the slope of an object. The use of the proposed approach presents several benefits, namely, avoiding the use of a wide range of data and specific sensors, fast detection and reliability even with incomplete images due to reflections and other problems. Based on this algorithm, a deviation index is also proposed that will be used to discriminate the panel(s) under fault. Several test cases are used to test and validate the proposed approach. From the obtained results, it is possible to verify that the PCA can successfully be adapted and used in image processing algorithms to determine the slope of the PV modules and so effectively detect a fault in the tracker, even when there are incomplete parts of an object in the image.

scholarly journals Detection of Node Failure in Wireless Image Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Arunanshu Mahapatro ◽  
Pabitra Mohan Khilar
Keyword(s):  
Fault Diagnosis ◽  
Fault Tolerant ◽  
Image Sensor ◽  
High Accuracy ◽  
Sensor Nodes ◽  
Message Complexity ◽  
Wide Range ◽  
Node Architecture ◽  
Diagnosis Model ◽  
Simulation Results

A sequenced process of fault detection followed by dissemination of decision made at each node characterizes the sustained operations of a fault-tolerant wireless image sensor network (WISN). This paper presents a distributed self-fault diagnosis model for WISN where fault diagnosis is achieved by disseminating decision made at each node. Architecture of fault-tolerant wireless image sensor nodes is presented. Simulation results show that sensor nodes with hard and soft faults are identified with high accuracy for a wide range of fault rate. Both time and message complexity of the proposed algorithm are for an -node WISN.

scholarly journals Advancing Building Fault Diagnosis Using the Concept of Contextual and Heterogeneous Test

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2510 ◽  
Author(s):  
Mahendra Singh ◽  
Nguyen Trung Kien ◽  
Houda Najeh ◽  
Stéphane Ploix ◽  
Antoine Caucheteux
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Complex Task ◽  
Building Modeling ◽  
Wide Range ◽  
Behavioral Constraints ◽  
Facility Manager ◽  
Detection And Diagnosis ◽  
Diagnosis Approach

Fault diagnosis and maintenance of a whole-building system is a complex task to perform. A wide range of available building fault detection and diagnosis (FDD) tools are only capable of performing fault detection using behavioral constraints analysis. However, the validity of the detected symptom is always questionable. In this work, we introduce the concept of the contextual test with validity constraints, in the context of building fault diagnostics. Thanks to a common formalization of the proposed heterogeneous tests, rule-, range-, and model-based tests can be combined in the same diagnostic analysis that reduces the whole-building modeling effort. The proposed methodology comprises the minimum diagnostic explanation feature that can significantly improve the knowledge of the building facility manager. A bridge diagnosis approach is used to describe the multiple fault scenarios. The proposed methodology is validated on an experimental building called the center for studies and design of prototypes (CECP) building located in Angers, France.

Sensor Fault Diagnosis Based on Wavelet Transform in Smart Grid

2012 ◽  
Vol 588-589 ◽  
pp. 680-683
Author(s):  
Xiao Da Sun ◽  
Gui Lan Xing ◽  
Ming Yue Zhai ◽  
Li Peng Lu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Smart Grid ◽  
Output Signal ◽  
Sensor Fault ◽  
Basic Principles ◽  
Simulation Results

In the paper, the output signal of sensor in smart grid is analyzed by wavelet, which could detect and diagnose the abrupt fault of sensor. First, the applications of sensor for smart grid are introduced. Then the basic principles of fault detection of sensor using wavelet are proposed. Finally, the simulation results in MATLAB verify the feasibility of the method.

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

scholarly journals Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture

2021 ◽  
Vol 11 (8) ◽  
pp. 3623
Author(s):  
Omar Said ◽  
Amr Tolba
Keyword(s):  
Packet Loss ◽  
Large Scale ◽  
Performance Metrics ◽  
Medical System ◽  
Healthcare Data ◽  
High Altitude Platforms ◽  
Wide Range ◽  
Simulation Results ◽  
Healthcare Architecture ◽  
The Internet Of Things

Employment of the Internet of Things (IoT) technology in the healthcare field can contribute to recruiting heterogeneous medical devices and creating smart cooperation between them. This cooperation leads to an increase in the efficiency of the entire medical system, thus accelerating the diagnosis and curing of patients, in general, and rescuing critical cases in particular. In this paper, a large-scale IoT-enabled healthcare architecture is proposed. To achieve a wide range of communication between healthcare devices, not only are Internet coverage tools utilized but also satellites and high-altitude platforms (HAPs). In addition, the clustering idea is applied in the proposed architecture to facilitate its management. Moreover, healthcare data are prioritized into several levels of importance. Finally, NS3 is used to measure the performance of the proposed IoT-enabled healthcare architecture. The performance metrics are delay, energy consumption, packet loss, coverage tool usage, throughput, percentage of served users, and percentage of each exchanged data type. The simulation results demonstrate that the proposed IoT-enabled healthcare architecture outperforms the traditional healthcare architecture.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

Simulation of Intrinsic Diffusion in Multicomponent Multiphase Systems

1998 ◽  
Vol 527 ◽  
Author(s):  
M. Hunkel ◽  
D. Bergner
Keyword(s):  
Single Phase ◽  
Diffusion Flux ◽  
Wind Effect ◽  
Concentration Profiles ◽  
Intrinsic Diffusion ◽  
Cross Terms ◽  
Multiphase Systems ◽  
Random Alloy ◽  
Simulation Results ◽  
Vacancy Flux

ABSTRACTA simulation model for intrinsic diffusion of multicomponent multiphase systems is presented. The model is not restricted onto a certain number of components or phases. For simplicity, Manning's random alloy model with vanishing vacancy wind effect is used. Then the cross terms of the diffusion flux can be neglected. The simulation routine uses equations for the fluxes, the equation of continuity and an equation for the change of the thickness of volume elements due to the vacancy flux. With this model diffusions paths, concentration profiles, fluxes of the components as well as marker positions can be calculated. The shift of interfaces and the growth of new phases can also be determined. The simulation results were compared with experimental data of the Cu-Fe-Ni system. Diffusion was studied in single-phase areas and across interfaces.

FAULT DIAGNOSIS IN A PROCESS EXPERIMENTAL CONTROL SYSTEM WITH UNKNOWN FACTORS

2008 ◽  
Vol 07 (01) ◽  
pp. 151-155 ◽  
Author(s):  
AKIRA INOUE ◽  
MINGCONG DENG
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Experimental System ◽  
Detection Problem ◽  
Experimental Control ◽  
Blind System Identification ◽  
Experimental Process ◽  
Identification Approach ◽  
Simulation Results ◽  
Detecting Method

A fault detection problem in a process control experimental system with unknown factors is presented in this paper. The fault detecting method is based on blind system identification approach. The experimental system actuator output includes unknown dynamics and unknown fault signal. By using the fault detecting method, the fault signal is detected. Simulation results for the experimental process are presented to show the effectiveness.

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