Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

Moosavian, Ashkan; Khazaee, Meghdad; Najafi, G.; Kettner, Maurice; Mamat, Rizalman

doi:10.1016/j.apacoust.2015.01.008

Cited by 43 publications

(21 citation statements)

References 49 publications

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“…As shown in Figures 8 to 10 and Tables 2 to 4, the feature values extracted from timing belt signals in the H state had a larger margin compared to faulty belt. In Khazaee et al, 51 Pan et al, 52 and Moosavian et al, 53 the maximum classification accuracy also belonged to the H state, consistent with the study results.…”

Section: Primary Classification Of Defectssupporting

confidence: 93%

Fault detection of engine timing belt based on vibration signals using data-mining techniques and a novel data fusion procedure

Khazaee

Banakar

Ghobadian

et al. 2016

Structural Health Monitoring

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In this research, an intelligent procedure was designed and implemented based on vibration signals for detecting and classifying prevalent faults of an internal combustion engine timing belt. The vibration signals of the timing belt were captured during operation in six different states: healthy, tooth crack, back crack, wear, separated tooth, and oil pollution. These signals were processed at three domains, namely, time, frequency, and time-frequency domains. Time-domain signals were transformed into the frequency and time-frequency domains using fast Fourier transform and wavelet transform, respectively. Then, six statistical features were extracted from vibration signals at all three domains. The extracted features were used as inputs to an artificial neural network for the primary classification of timing belt defects. Classification accuracy of artificial neural network in detecting and classifying timing belt faults in the time, frequency, and time-frequency domains have obtained 71%, 78%, and 84%, respectively. Combining separate classification accuracies from time, frequency, and time-frequency domains has been implemented using Dempster-Shafer theory of evidence. Classification accuracy based on the fusion of time-and frequency-domain classifiers was 97%, from time and timefrequency results was 98%, and from frequency and time-frequency results was also 98%, whereas the combination of results for all domains led to a .99% accuracy. Results show that the proposed methodology can detect and classify timing belt defects with high precision and reliability before failure occurrence.

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Section: Primary Classification Of Defectssupporting

confidence: 93%

Fault detection of engine timing belt based on vibration signals using data-mining techniques and a novel data fusion procedure

Khazaee

Banakar

Ghobadian

et al. 2016

Structural Health Monitoring

Self Cite

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“…The evidence theory makes the sum of the fusion feature values in one group equal to 1. 22 In addition, experimental results show that the number of fusion feature values near the maximum in one group is uncertain. When thruster fault degree is bigger, the number of fusion feature values near the maximum may be bigger too.…”

Section: Problem Statementmentioning

confidence: 99%

Thruster fault identification method for autonomous underwater vehicle using peak region energy and least square grey relational grade

Zhang

Yin

Liu

et al. 2015

Advances in Mechanical Engineering

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A novel thruster fault identification method for autonomous underwater vehicle is presented in this article. It uses the proposed peak region energy method to extract fault feature and uses the proposed least square grey relational grade method to estimate fault degree. The peak region energy method is developed from fusion feature modulus maximum method. It applies the fusion feature modulus maximum method to get fusion feature and then regards the maximum of peak region energy in the convolution operation results of fusion feature as fault feature. The least square grey relational grade method is developed from grey relational analysis algorithm. It determines the fault degree interval by the grey relational analysis algorithm and then estimates fault degree in the interval by least square algorithm. Pool experiments of the experimental prototype are conducted to verify the effectiveness of the proposed methods. The experimental results show that the fault feature extracted by the peak region energy method is monotonic to fault degree while the one extracted by the fusion feature modulus maximum method is not. The least square grey relational grade method can further get an estimation result between adjacent standard fault degrees while the estimation result of the grey relational analysis algorithm is just one of the standard fault degrees.

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“…If a fault is correctly detected and diagnosed, corrective measures can be applied to repair the fault and reduce any further damage to the system. Many researchers carried out intensive research on FDD [18][19][20][21] . Authors 22 used the energy balance equations which balance the relation among pressure, mass flow rate and power at various locations to diagnose the faults of the space shuttle main engine.…”

Section: Tests and Experimentsmentioning

confidence: 99%

Using Sensors Data and Emissions Information to Diagnose Engine’s Faults

Xie¹,

Wang²,

MacIntyre³

et al. 2018

IJCIS

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This paper proposes using engine's sensors data flow and exhaust emissions information to diagnose engine's faults, enhancing the accuracy of fault diagnosis. Engine fault diagnosis model is built using both this information and the mature BP neural network and genetic algorithms. In order to verify the method, we build a test platform, which includes South Korea Hyundai fault test vehicle and X-431 diagnosis instrument and AUTO5-1 exhaust gas analyzer and computer. The diagnostic accuracy rate can reach 98.33%, which is higher than using sensors data flow or the exhaust emissions information alone.

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Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

Cited by 43 publications

References 49 publications

Fault detection of engine timing belt based on vibration signals using data-mining techniques and a novel data fusion procedure

Fault detection of engine timing belt based on vibration signals using data-mining techniques and a novel data fusion procedure

Thruster fault identification method for autonomous underwater vehicle using peak region energy and least square grey relational grade

Using Sensors Data and Emissions Information to Diagnose Engine’s Faults

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