scholarly journals Analysis of Total Harmonic Distortion and implementation of Inverter Fault Diagnosis using Artificial Neural Network

2022 ◽  
Vol 2161 (1) ◽  
pp. 012060
Author(s):  
T G Manjunath ◽  
A C Vikramathithan ◽  
H Girish
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Harmonic Distortion ◽  
Total Harmonic Distortion ◽  
Auxiliary Cell ◽  
Diagnosis Accuracy ◽  
Multi Level ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract As power electronics devices dependability is very significant to guarantee Multi Level Inverter (MLI) systems stable functioning, it is imperative to identify and position faults as promptly as possible. Due to the fault occurrences, the Total Harmonic Distortion (THD) on the system gets a hit. In this perspective, to improve fault diagnosis accuracy and efficient working of a Cascaded Multi level Inverter System (CHMLIS), a quick and accurate fault diagnosis strategy with an optimized training algorithm using Artificial Neural Network (ANN) is presented. Also, Total Harmonic Distortion (THD) is analyzed for each switch Fault simulated using MATLAB/Simulink and the results are presented. Results shows the efficacy of Algorithm in identifying the fault. The auxiliary cell is replaced while the fault occurs in the main cell thus making the uninterrupted working of the Multi-Level Inverter (MLI) in the Induction Motor Drive (IMD).

Orbit Shape Automatic Recognition Based on Artificial Neural Network

2005 ◽  
Author(s):  
Dongmei Du ◽  
Qing He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Recognition Performance ◽  
Shape Recognition ◽  
Vibration Monitoring ◽  
Moment Invariants ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Orbit is a significant symptom in the fault diagnosis of rotating machine. The orbit is a 2-D image and can be described by moment invariants, the shape property of 2-D image, which is a description with translating-, rotating-, and scaling-invariants for 2-D image. The descriptive method of orbit image is investigated and an automatic orbit shape recognition based on artificial neural network (ANN) with moment invariants is proposed in this paper. The ANN of orbit shape recognition is trained by the training patterns generated by computer simulation for plenty of orbit shapes. It is shown that the trained ANN is of good recognition performance and generalization capability when applied to recognition of the measured orbits. This method can be used to the intelligent expert system of fault diagnosis to obtain automatically online orbit symptom in shafts vibration monitoring of turbine generator, which will improve the automatization of obtaining fault symptom and the automatic diagnosis in the expert system.

An Investigation of Artificial Neural Network (ANN) in Quantitative Fault Diagnosis for Turbofan Engine

2000 ◽  
Author(s):  
Sun Bin ◽  
Zhang Jin ◽  
Zhang Shaoji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Turbofan Engine ◽  
Quantitative Diagnosis ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper is aimed at investigating two kinds of Artificial Neural Network (ANN) applied to quantitative fault diagnosis of turbofan engine gas path components. Among them, one is Back Propagation neural Network (BPN) and the other is Adaptive Probabilistic Neural Network (APNN). Using BPN in order to achieve quantitative fault diagnosis, number of training samples will increase greatly which may lead to the difficulty of iteration convergence. A new learning rule named hybrid rule is introduced to avoid the algorithm falling into static areas and expedite convergence. Recently, a new method to improve the adaptability of multi-layer feed-forward neural network has been developed by the application of Radial Basis Function (RBF). In this paper, the APNN is put forward based on the theory of radial basis function, Bayesian estimation and normal distribution hypothesis of information. It is proposed that the adaptability of APNN can be obtained by applying maximum-likelihood estimation of the output of test case based on a posteriori probability of its input. The investigation shows that BPN and APNN have their own advantages and disadvantages. BPN has faster diagnostic speed and fits the requirement of quantitative diagnosis for single fault. APNN is more adaptive and fit better to quantitative diagnosis for multiple faults.

Artificial Neural Network based SVPWM for Five Level Cascaded H-bridge Inverter fed Grid connected PV System

2020 ◽  
Vol 39 (6) ◽  
pp. 8453-8462
Author(s):  
R. Palanisamy ◽  
K. Mohana Sundram ◽  
K. Selvakumar ◽  
C.S. Boopathi ◽  
D. Selvabharathi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pulse Width Modulation ◽  
Switching Time ◽  
Harmonic Distortion ◽  
Output Current ◽  
Pv System ◽  
Non Linear Systems ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

An Artificial Neural Network (ANN) based Space Vector Pulse Width Modulation (SVPWM) for five level cascaded H-bridge inverter (CHBI) fed grid connected photovoltaic (PV) system. The multilevel inverter topologies are offers better performance compare conventional two level inverter like reduced total harmonic distortion, less electromagnetic interferences and voltage stresses across switches are low. The ANN based SVPWM generates the switching pulses for cascaded H-bridge inverter; it improves the accuracy in reference vectors tuning and identification, which leads to improve the inverter output voltage, better utilization of dc-link voltage and controlled output current. The ANN control makes the implementation of SVPWM becomes simple and minimizes the intricacy in tracking reference vector and calculation of switching time; it is suitable for any type of non-linear systems. This proposed system is energized using PV system and it is boosted using dc-dc boost converter, and the output of CHBI is synchronized with grid connected system using coupled inductor. The simulation and experimental results of ANN based SVPWM for CHBI is verified using simulink-matlab and DSP processor.

Experimental-Based Fault Diagnosis of Rolling Bearings Using Artificial Neural Network

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
R. A. Kanai ◽  
R. G. Desavale ◽  
S. P. Chavan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Simulated Data ◽  
Vibration Characteristics ◽  
Machine Element ◽  
Deep Groove ◽  
Innovative System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper, an innovative system for condition-based monitoring (CBM) using model-based estimation (MBE) and artificial neural network (ANN) is proposed. Fault diagnosis of deep groove ball bearings (DGBB) is a key machine element for stability of rotating machinery. MBE model is proposed to demonstrate and estimate the vibration characteristics of bearings. It is realized that it may be worth mentioning that the vibration analysis of damaged bearings at all the positions of a structure is difficult to obtain. For this purpose, methods have been discussed to get the utmost information to notify bearing faults. The ANN approach enables us to determine the effects of various parameters of the vibrations by conducting the experiments. The results point out that defect size, speed, load, unbalance, and clearance influence the vibrations significantly. Experimental simulated data using the MBE and ANN models of rotor–bearing are used to identify the damage diagnosis at a reasonable level of accuracy. The results of the experiments consist in constantly evaluating the performance of the bearing and thereby detecting the faults and vibration characteristics successfully. The effects of faults and vibration characteristics obtained using the experimental MBE and ANN are studied.

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