An Analysis of Sound for Fault Engine

Chomphan,

doi:10.3844/ajassp.2014.1005.1009

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…Given the complexity of the classification task, these results compare favorably to those obtained in other works (Raj & Murali, 2013;Xiao et al, 2013). Due to the employment of neural networks and their GA-and PSO-based hybridization used in the aeronautical fault diagnosis, results comparable to those published in the works (Londhe et al, 2014;Chomphan & Kingrattanaset, 2014) have been obtained.…”

Section: Validation Results and Analysissupporting

confidence: 81%

“…To identify the faults and achieve a better classification performance, it is important that the features selected contain necessary discriminative information. A number of vibration intensity techniques have been used to analyze engine front noise (Chomphan & Kingrattanaset, 2014). In (Lajmi et al, 2017) a fault diagnosis based on fuzzy Petri net interval is designed.…”

Section: Introductionmentioning

confidence: 99%

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Helicopter Main Rotor Fault Diagnosis by Using GA- and PSO-based Classifiers

Mjahed¹,

Hadaj²,

Bouzaachane³

et al. 2020

STUD INFORM CONTROL

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This paper presents an improvement in the recognition of faulty signals, encountered in the case of the Gazelle helicopter's main rotor, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. The main focus is on the distinction between faulty and healthy signals and then between the three subclasses of faulty signals, i.e. faulty bearings, joints problem and mechanical loosening. This research work is divided into three parts. The first part approaches the two above-mentioned classes of signals at the same time, and, to this purpose, the Linear Discriminant Analysis (LDA), Non Linear Discriminant Analysis (NLDA) and Back-propagation Neural Network (BPNN) are used. In the second and third part of the paper, GA and PSO are employed for optimizing the hyperplanes and hypersurfaces which separate the above-mentioned classes of signals, as well as the architecture and connection weights of a neural network (NN). Real data are used, which correspond to the vibration signals measured during periodic technical inspections, and are characterized by amplitudes and frequencies typical of the eight highest peaks of the Welch spectrum. The results obtained confirm the validity of the above-mentioned approaches and comparable favorably with those of other multivariate methods. The GA-or PSO-based neural networks` diagnosis can therefore be established for helicopter computers so that faults can be detected.

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Section: Validation Results and Analysissupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Helicopter Main Rotor Fault Diagnosis by Using GA- and PSO-based Classifiers

Mjahed¹,

Hadaj²,

Bouzaachane³

et al. 2020

STUD INFORM CONTROL

View full text Add to dashboard Cite

show abstract

“…It should be noted that Bearing fault data and Higgs Boson data, have been extensively analysed, also implying the selection of the best attributes, as in (Ouadine et al, 2018) for Bearing fault data, and in (Mjahed et al, 2020;Tang, 2021;Azhari et al, 2020) for Higgs Boson data. For Bearing fault diagnosis and with the use of PSO-ANN approach, one obtained generally better results than those featured by other works, as in (Chomphan & Kingrattanaset, 2014;Ouadine et al, 2018). Regarding the results of the Higgs Boson dataset, PSO-ANN outperforms the results published in (Mjahed et al, 2020;Tang, 2021;Azhari et al, 2020).…”

Section: Resultsmentioning

confidence: 69%

Bio-Inspired Hybridization of Artificial Neural Networks for Various Classification Tasks

MJAHED¹,

HADAJ²,

GUARMAH³

et al. 2022

STUD INFORM CONTROL

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Recently, in order to optimize artificial neural networks (ANNs), several bio-inspired metaheuristic algorithms have been successfully applied. Moreover, these hybrid ANNs were operated using no more than two or three metaheuristic algorithms at a time. Additionally, the classification field is so rich that some issues were not sufficiently addressed. The main contribution of this paper is related to the use of several ANN hybridizations at the same time, while taking into account the datasets for which the ANNs or their hybridizations have been rarely explored. Thus, seven hybridized ANNs with bio-inspired metaheuristic algorithms such as particle swarm optimization (PSO-ANN), genetic algorithm (GA-ANN), differential evolution (DE-ANN), cultural algorithm (CA-ANN), harmony search (HS-ANN), black hole algorithm (BH-ANN) and ant lion optimizer (ALO-ANN) were considered for classifying four kinds of datasets. After a back-propagation neural network (BPNN) was designed, the connection weights and biases of neurons were optimized by using the seven metaheuristic algorithms mentioned above. The four selected data types belong to different domains and differ with regard to the number of classes, variables and examples. As performance measurement is concerned; the efficiencies, purities and F-measure are analysed. For all simulation runs, it can be noticed that metaheuristic algorithms were able to reach optimal efficiencies and that all the PSO-ANN-based networks obtained higher values for efficiency. For this analysis, the dependence of the obtained results on certain metaheuristic parameters was taken into account.

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“…The energy of the engine-running vibration signal often goes to infinity in accordance with the previous constraint. The calculation of the power of the signal is defined by using the sum of the square of the signal samples [17][18][19]. Mathematically, the averaged power of an aperiodic sequence x[n] is defined as the following equation.…”

Section: Averaged Power Of Vibration Signalmentioning

confidence: 99%

“…The equallydivided sections of the root sum square values are therefore provided for all experiments. Subsequently, the extraction of vibration's attributes of averaged power of the vibration signal as mentioned in the previous section has been conducted [18][19]. Moreover, the averaged peak of the vibration signal is also extracted with the same conditions [20].…”

Section: Experimental Designsmentioning

confidence: 99%

Vibration Signal Analysis of a Motorcycle

Chomphan¹

2019

GEOMATE

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Nowadays, the motorcycle is becoming widely popular because of their swiftness and conveniences in crowded cities. It is considered the most popular choice in various aspects of transportation in the urban area. When starting and accelerating the engine, it has been found that there was a vibration throughout all parts of the motorcycle. If the driver perceives more vibration, he will feel more uncomfortable. Consequently, the researcher conducted a study of the vibration signal sent through four core points which are the contacts between driver or rider and the motorcycle body including motorcycle left hand, seat, left front footrest, and left rear footrest. The specimen motorcycle is Honda Wave 100s model. The comparison results have been performed by using two parameters of averaged peak and averaged energy of the vibrational signal. The results reveal that the left front footrest has the highest vibration level in both parameters, while the seat has the lowest vibration level. Last but not least, it can be concluded that the mechanical structure of the motorcycle must be developed by considering the engine's vibration sent through all parts especially the position of front footrest which has the highest impact in order to increase the driver's comfort at the highest satisfaction.

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An Analysis of Sound for Fault Engine

Cited by 4 publications

References 8 publications

Helicopter Main Rotor Fault Diagnosis by Using GA- and PSO-based Classifiers

Helicopter Main Rotor Fault Diagnosis by Using GA- and PSO-based Classifiers

Bio-Inspired Hybridization of Artificial Neural Networks for Various Classification Tasks

Vibration Signal Analysis of a Motorcycle

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