Transfer Path Analysis and Contribution Evaluation Using SVD- and PCA-Based Operational Transfer Path Analysis

Cheng, Wei; Blamaud, Diane; Chu, Yapeng; Lei, Meng; Lu, Jingbai; Basit, Wajid Ali

doi:10.1155/2020/9673838

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The existing OTPA method calculates the output signal using an individual transfer function for each frequency component (Cheng et al, 2020). Moreover, each measurement direction and point are calculated under the assumption of linear independence.…”

Section: Proposed Methodsmentioning

confidence: 99%

Deep Learning Approach for Operational Transfer Path Analysis: Case Study of Electric Vehicles

Oh,

Yoo,

et al. 2023

PHMAP_CONF

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This paper presents a new approach to fault diagnosis of the drivetrains of the electric vehicle. Most commercially available electric vehicles do not have accelerometers on electric drivetrains making it difficult to detect fault characteristics of the drivetrains of the electric vehicle, whereas accelerometers exist on the driver's seat. The proposed approach’s key idea is based on the operational transfer path analysis that determines the transfer function between the source and receiver. The transfer function is derived by training a deep learning model. The deep learning model converts the driver's seat vibration signals into drivetrains vibration signals. The validity of the proposed approach is evaluated using data from the durability test of real electric vehicles. It is anticipated that the proposed approach is effective to diagnose electric vehicle drivetrains subjected to external noise conditions.

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Section: Proposed Methodsmentioning

confidence: 99%

Deep Learning Approach for Operational Transfer Path Analysis: Case Study of Electric Vehicles

Oh,

Yoo,

et al. 2023

PHMAP_CONF

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show abstract

“…The traditional OTPA method presents errors in contribution calculation and misjudgements in the transfer function determination because of data noise and crosstalk. Therefore, it is significant to improve the accuracy of OTPA method by using SVD and PCA [10].…”

Section: Svd-and Pca-based Otpa Methodsmentioning

confidence: 99%

OTPA method-based contribution analysis of components on the vibration of fuel cell in fuel cell vehicles

Nan¹,

Behrendt²,

Lu³

et al. 2021

inter noise

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Due to the introduction of auxiliary components in fuel cell vehicle powertrains and absence of internal combustion engine, the vibration sources and transfer paths are very different from conventional vehicles. These vibrations interact on the output performance of the fuel cell system. Therefore, it is necessary to investigate the vibration characteristics of the fuel cell system under vehicle operating conditions. IPEK conducted vehicle measurements regarding different driving manoeuvres and environments. In order to quantitatively evaluate contributions of each vibration source on the total vibration of fuel cell, frequency-domain contribution was investigated based on Operational Transfer Path Analysis method with the singular value decomposition as well as principal component. The results of vibration in Z-direction in the vehicle coordinate system show that the hydrogen pump dominantly contributes to the vibration of fuel cell in a wide range of frequency in the majority of the driving manoeuvres. However, the results vary in various driving manoeuvres, environments and frequencies. The Paper will discuss in detail the vibrational contributions in X-, Y- and Z-direction.

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“…The transmission function for measuring the source to the target point requires the joint system to be disconnected and the active components to be dismantled, so efficiency is not high. Influenced by the conditional matrix values of the transmitted function and data measurement errors, the use of singular value decomposition (SVD) is generally used to solve the reverse non-adaptation problem of sound source recognition [10]. Conventional TPA has been the standard for the automotive industry's noise, vibration, harshness (NVH) analysis [11].…”

Section: Introductionmentioning

confidence: 99%

Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network

Huang,

Cao

et al. 2023

Applied Sciences

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Clarifying the noise source and the contribution of each path is essential for the system’s noise control. The auxiliary converter cabinet, which is a crucial component of rail transportation, has numerous intricate noise sources. The contribution of each path point must be inverted-solved using known transfer functions and target point test values when identifying noise sources. This article suggests a method for diagnosing noise using transfer path analysis and neural networks (TPA-NN). Firstly, the principle and scheme for analyzing the transmission path of the converter cabinet are proposed. The transfer function of each path is obtained by selecting suitable path points, reference points, and target points for air and structure acoustic vibration experiments. The external target point data are then combined with the neural network’s linear fitting function, and the contribution of each path is used as an output for network training while some path point contributions are rebuilt. The results indicate that the method’s outcomes are most accurate when the converter cabinet’s path point is 13 and the target point is 6. This approach offers an innovative technique for locating noise sources in intricate systems.

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Transfer Path Analysis and Contribution Evaluation Using SVD- and PCA-Based Operational Transfer Path Analysis

Cited by 3 publications

References 13 publications

Deep Learning Approach for Operational Transfer Path Analysis: Case Study of Electric Vehicles

Deep Learning Approach for Operational Transfer Path Analysis: Case Study of Electric Vehicles

OTPA method-based contribution analysis of components on the vibration of fuel cell in fuel cell vehicles

Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network

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