Forthcoming and Online First Articles

International Journal of Vehicle Noise and Vibration

International Journal of Vehicle Noise and Vibration (IJVNV)

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International Journal of Vehicle Noise and Vibration (5 papers in press)

Regular Issues

  • Kinematic simulations and design of a steering upright for a single seater electric car   Order a copy of this article
    by Shubham Ganju, Kelly Ann Dennis Furtado, Fanil Harshad Birawat, Nagesh Shenoy, Subash Acharya 
    Abstract: This work discusses a systematic approach for design and analysis of a steering upright based on suspension hardpoint analysis. The steps followed would ensure anticipated performance of a vehicle in terms of ride comfort and handling. The suspension hardpoints were determined using LOTUSTM Shark suspension analysis software based on kinematic simulations. The geometric model of the upright was then created in accordance with the hardpoints obtained. Static structural analysis was performed on the preliminary upright design and design modifications were done to reduce the weight. Further, a suitable material was selected for the upright based on a comparison of values of stress, deformation, safety factor and mass for different upright materials. In addition, fatigue analysis was performed to compute the life of the upright. The manufactured uprights were assembled with other suspension assembly components and were tested under different on road conditions to observe for any failure.
    Keywords: steering upright; suspension hardpoints; kinematic simulations; wheel alignment angles; finite element analysis.
    DOI: 10.1504/IJVNV.2023.10061184
     
  • Intelligent fault detection of spark plugs using vibration signal analysis and artificial neural networks   Order a copy of this article
    by Ihsan Baqer 
    Abstract: The primary concern pertaining to spark plug use lies in their ignition efficiency and longevity. To address this issue, an intelligent and practical approach must be devised to serve as an indicator for determining the optimal time to replace a spark plug. In light of this, the present study is dedicated to the problem of fault detection in automobile engines, employing a sophisticated artificial neural network. The research uses Time-Domain Signal Analysis as a means for extracting insightful characteristics, including RMS, kurtosis, skewness, and mean, from the data of vibration. Through the construction of an ANN model, distinct operating conditions simulating both healthy and faulty spark plugs are discerned. The vibration data acquisition is carried out using an accelerometer (ADXL335) interfaced with an Arduino Mega, which serves as the data acquisition device. The results demonstrate that the designed system exhibits an exceptional 100% overall accuracy in identifying faulty spark plug conditions.
    Keywords: spark plug; diagnosis; maintenance; vibration; artificial neural network; ANN.
    DOI: 10.1504/IJVNV.2023.10061231
     
  • Assessing the efficiency of TPI-NSI and NSI-TPI in isolating the vibration of driver's seat   Order a copy of this article
    by Zhang Lingling, Qiao Maohua 
    Abstract: This study proposes two models for seat isolation using TPI-NSI and NSI-TPI based on the isolating performance of the negative stiffness isolation (NSI) in the vertical direction and three parallel isolations (TPI) in the pitch and roll directions to improve the driver comfort. To assess the isolating performance of TPI-NSI and NSI-TPI, their parameters are then analysed and optimised via the seat's dynamic model. Investigation result shows that both NSI-TPI and TPI-NSI similarly improve the driver's vertical comfort, while TPI-NSI strongly improves the seat's pitch and roll angles in comparison with NSI-TPI. Moreover, with parameters of TPI-NSI optimised, seat acceleration in the vertical, pitch, and roll directions are obviously decreased by 20.1%, 20.0%, and 33.3% compared with the TPI-NSI's designed parameters. From the results of this theoretical study, the seat's TPI-NSI can be a potential new isolation approach to improve the comfort of vehicles.
    Keywords: seat isolation system; seat dynamic model; NSI-TPI; TPI-NSI; driver comfort.
    DOI: 10.1504/IJVNV.2023.10061232
     
  • Preview control of the random response of a full car vehicle model traversing a rough road   Order a copy of this article
    by L.V.V. Gopala Rao, V.S.V. Satyanarayana, Rakesh Chandmal Sharma, Srihari Palli, Anuj Raturi, Ashwani Kharola, Ashwini Sharma 
    Abstract: This paper considers a full-vehicle model with preview control travelling on a random road. The road roughness is modelled as the PSD of the random road irregularity provided by integrated white noise which is estimated by a deterministic step input. The response of the vehicle model is optimised to improve the vehicle performance given by a performance index which is the weighted integral of suspension working space, tyre deflection and control force. The RMS values of control force, suspension space and tyre deflection are computed using the method of spectral decomposition. The results show that there is a substantial improvement in the vehicle performance in the case of linear-quadratic regulator with preview control compared with the linear-quadratic regulator without preview control.
    Keywords: full vehicle model; random road; active suspension; preview control; spectral decomposition.
    DOI: 10.1504/IJVNV.2023.10062219
     
  • Machine learning based progressive crack fault monitoring on spur gear using vibration analysis   Order a copy of this article
    by Manoj Gangwar, Neelesh Sahu, Rajendra Kumar Shukla, Chitresh Nayak 
    Abstract: The ability to diagnose progressive crack fault on spur gear using signal processing and machine learning (ML) techniques. Experiments are performed on three different conditions of spur gear, healthy as well as crack at tooth root (50%, 90%). Time-domain and frequency-domain signal processing methods as well as machine learning techniques have been used to process and analyse the acquired signals. This study is motivated by the process of spur gear fault diagnosis by machine learning algorithms as J48 decision tree and support vector machine (SVM). Noise level is also considered during the meshing of gears. The results of this investigation revealed that J48 decision tree outperforms from SVM with 93.3% accuracy. It has been noted that both sides’ signals and noise levels must be analysed in order to improve gear health condition monitoring. The proposed method can be used to diagnose the fault on different gears and other elements.
    Keywords: spur gear; fault diagnosis; condition monitoring; signal processing and machine learning; support vector machine; SVM.
    DOI: 10.1504/IJVNV.2024.10063194