scholarly journals Optimizing Neuro-Fuzzy Fault Diagnostic Algorithm for Photovoltaic Systems

2016 ◽  
Vol 18 (04) ◽  
pp. 64-71
Author(s):  
A.H. Mohamed ◽  
K.H. Marzouk
Keyword(s):  
Diagnostic Algorithm ◽  
Photovoltaic Systems ◽  
Neuro Fuzzy ◽  
Fault Diagnostic

Hybrid Model-Based and Data-Driven Diagnostic Algorithm for Gas Turbine Engines

2020 ◽  
Author(s):  
Amare Fentaye ◽  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Mikael Stenfelt ◽  
Konstantinos Kyprianidis
Keyword(s):  
High Pressure ◽  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Diagnostic Algorithm ◽  
Data Driven ◽  
Intermediate Pressure ◽  
Path Component ◽  
Fault Diagnostic ◽  
Component Faults ◽  
Pressure Compressor

Abstract Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.

scholarly journals Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2269
Author(s):  
Ahmed F. Bendary ◽  
Almoataz Y. Abdelaziz ◽  
Mohamed M. Ismail ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection Methods ◽  
Photovoltaic Systems ◽  
Inference System ◽  
Photovoltaic Array ◽  
Neuro Fuzzy ◽  
Effective Manner ◽  
Simulation Results

In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.

scholarly journals A novel vibration-based fault diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement

2017 ◽  
Vol 94 ◽  
pp. 14-32 ◽  
Author(s):  
Liu Hong ◽  
Yongzhi Qu ◽  
Jaspreet Singh Dhupia ◽  
Shuangwen Sheng ◽  
Yuegang Tan ◽  
...  
Keyword(s):  
Rotational Speed ◽  
Diagnostic Algorithm ◽  
Speed Measurement ◽  
Fault Diagnostic ◽  
Speed Fluctuations

A Fault Diagnostic Algorithm for Fault Tolerant Control Systems

1986 ◽  
Vol 19 (13) ◽  
pp. 119-122
Author(s):  
Z. Bien ◽  
M.J. Youn ◽  
M.J. Chung ◽  
J.H. Kim ◽  
B.C. Moon ◽  
...  
Keyword(s):  
Control Systems ◽  
Fault Tolerant ◽  
Diagnostic Algorithm ◽  
Fault Tolerant Control ◽  
Fault Diagnostic

scholarly journals A Simultaneous Fault Diagnosis Method Based on Cohesion Evaluation and Improved BP-MLL for Rotating Machinery

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yixuan Zhang ◽  
Rui Yang ◽  
Mengjie Huang ◽  
Yu Han ◽  
Yiqi Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Error Function ◽  
Diagnostic Algorithm ◽  
High Sensitivity ◽  
Rotating Machinery ◽  
Feature Vectors ◽  
Evaluation Algorithm ◽  
Fault Diagnostic ◽  
Low Dimensional

In this paper, an improved simultaneous fault diagnostic algorithm with cohesion-based feature selection and improved backpropagation multilabel learning (BP-MLL) classification is proposed to localize and diagnose different simultaneous faults on gearbox and bearings in rotating machinery. Cohesion evaluation algorithm selects high sensitivity feature parameters from time and frequency domain in high-dimensional vectors to construct low-dimensional feature vectors. The BP-MLL neural network is utilized for fault diagnosis by classifying the feature vectors. An effective global error function is proposed in BP-MLL neural network by modifying distance function to improve both generalization ability and fault diagnostic ability of full-labeled and nonlabeled situations. To demonstrate the effectiveness of the proposed method, simultaneous fault diagnosis experiments are conducted via wind turbine drivetrain diagnostics simulator (WTDDS). The experiment results show that the proposed method has better overall performance compared with conventional BP-MLL algorithm and some other learning algorithms.

scholarly journals Neuro-Fuzzy Fault Detection Method for Photovoltaic Systems

2014 ◽  
Vol 62 ◽  
pp. 431-441 ◽  
Author(s):  
Luca Bonsignore ◽  
Mehrdad Davarifar ◽  
Abdelhamid Rabhi ◽  
Giuseppe M. Tina ◽  
Ahmed Elhajjaji
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Photovoltaic Systems ◽  
Neuro Fuzzy
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