Determination of Engine Misfire Location using Artificial Neural Networks

Ghazaly, Nouby M.; Abdel-Fattah, Muhammad; Makrahy, Mostafa M.

doi:10.4273/ijvss.11.4.13

Cited by 5 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using sound quality metrics with a support vector machine (SVM) [ 36 , 37 ] also improves upon vibration analysis. Parallel tasks in misfire fault analysis consider finding the location of the misfire [ 38 ] or extracting the fault component from the acoustic signal [ 39 ].…”

Section: Prior Artmentioning

confidence: 99%

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

Terwilliger

Siegel

2022

Sensors

View full text Add to dashboard Cite

In a world dependent on road-based transportation, it is essential to understand automobiles. We propose an acoustic road vehicle characterization system as an integrated approach for using sound captured by mobile devices to enhance transparency and understanding of vehicles and their condition for non-expert users. We develop and implement novel deep learning cascading architectures, which we define as conditional, multi-level networks that process raw audio to extract highly granular insights for vehicle understanding. To showcase the viability of cascading architectures, we build a multi-task convolutional neural network that predicts and cascades vehicle attributes to enhance misfire fault detection. We train and test these models on a synthesized dataset reflecting more than 40 hours of augmented audio. Through cascading fuel type, engine configuration, cylinder count and aspiration type attributes, our cascading CNN achieves 87.0% test set accuracy on misfire fault detection which demonstrates margins of 8.0% and 1.7% over naïve and parallel CNN baselines. We explore experimental studies focused on acoustic features, data augmentation, and data reliability. Finally, we conclude with a discussion of broader implications, future directions, and application areas for this work.

show abstract

Section: Prior Artmentioning

confidence: 99%

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

Terwilliger

Siegel

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…As the optimal number of past values is not necessarily high (Chi, 2021b), the study of soybeans global price time series forecasting has used 3 feedback delays with 8 hidden neurons in the NARNN model. Regarding COVID-19 forecasting, Ghazaly et al (2020) set the number of delays from 1: 2 to 1: 10 for the NARNN model to forecast COVID-19 cases. The result reflects that the model with 1: 6 feedback delays has the lowest error.…”

Section: Introductionmentioning

confidence: 99%

“…In previous research regarding COVID-19 forecasting using the NARNN model, a different number of hidden neurons is set to train the model as the complexity needed for the model is different for each dataset training process. Ghazaly et al (2020) tested the NARNN model's performance with 1 to 4 hidden neurons in conducting COVID-19 forecasting. It turns out that a network with 3 hidden neurons recorded with the lowest MAPE becomes the most appropriate configuration.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia

ABD RAHMAN

2023

JOSMA

View full text Add to dashboard Cite

A nonlinear autoregressive neural network (NARNN) model is a feedforward neural network for handling complex nonlinear time series problem. In this study, the tangent sigmoid (tansig) activation function with different number of past values and different number of hidden neurons for NARNN model is determined. The COVID-19 daily confirmed cases in Malaysia are collected with different amount of sample used which are 100, 500 and 900. Therefore, data of 100, 500 and 900 days prior to 21 September 2022 are extracted for the NARNN model training, validation and testing procedure. The lowest average mean squared error (MSE) are considered as the best combination. Result shown that the past value 1:10 and number of neurons of 10 when the sample size is 100. At sample size 500, past values of 1:10 and number of neurons of 8 enables the model to perform at its best. Whereas for sample size 900, network setting of 1:5 past value and 5 hidden neurons gives the least MSE. Multi-step ahead time series forecasting is conducted to forecast the number of COVID-19 confirmed cases in 7 days which is from 22 to 28 September 2022. The result shown for 7-days-ahead confirmed cases forecasting Malaysia datasets, the best forecasting outcome occurs when 900 samples are inputted.

show abstract

“…During the past three decades, diagnostic techniques for the detection of gearbox defects have been intensively researched, including those reported by Dalpiaz et al [10], Ma et al [11], Mohammed et al [12], Saxena et al [13], Wu et al [14], and Li [15]. Rezaei et al [16] detected multicrack locations and lengths from transmission-error ratios.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Crack on Gearbox Using Vibration-Based Approaches

2022

Self Cite

View full text Add to dashboard Cite

This study experimentally investigates vibration-based approaches for fault diagnosis of automotive gearboxes. The primary objective is to identify methods that can detect gear-tooth cracks, a common fault in gearboxes. Vibrational signals were supervised on a gearbox test rig under different operating conditions of gears with three symmetrical crack depths (1, 2, and 3 mm). The severity of the gear-tooth cracks was predicted from the vibrational signal dataset using an artificial feedforward multilayer neural network with backpropagation (NNBP). The vibration amplitudes were the greatest when the crack size in the high-speed shaft was 3 mm, and the root mean square of its vibration speed was below 3.5 mm/s. The vibration amplitudes of the gearbox increased with increasing depth of the tooth cracks under different operating conditions. The NNBP predicted the states of gear-tooth cracks with an average recognition rate of 80.41% under different conditions. In some cases, the fault degree was difficult to estimate via time-domain analysis as the vibration level increases were small and not easily noticed. Results also showed that when using the same statistical features, the time-domain analysis can better detect crack degree compared to the neural network technique.

show abstract

Determination of Engine Misfire Location using Artificial Neural Networks

Cited by 5 publications

References 0 publications

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia

Fault Diagnosis of Crack on Gearbox Using Vibration-Based Approaches

Contact Info

Product

Resources

About