# Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network
## 1 Introduction  
Low noise performance has grown in importance as a key indicator of product quality with the booming urban rail transportation sector [1]. Transformers, resistances, and centrifuges are only a few of the various sources of motivation that are integrated internally as part of the railway vehicle's core equipment. In order to ensure the ventilation effect of the electrical components, the higher power fan will ineluctably produce noises such as wind noise caused by the wind path and windshield rotation. In addition, cabinet vibration will produce noises such as vibrational radiation and electromagnetic noise while the transformer, resistor, and other electrical components are operating [2]. Multiple excitations that arrive at the target position via various transmission paths combine to form mechanical systems' vibration and noise. A thorough analysis of the condition of every excitation and transmission path is required to diagnose and optimize vibration and noise more effectively. Decomposing the contribution of path points to target points using transmission path analysis (TPA) is a useful technique. TPA quantifies the contribution of various sound streams and their paths to vibration and noise. To preserve noise and vibration within preset target values, TPA can be used to identify the dominant transmission pathways for vibration and noise. These pathways can then be improved and controlled. The transfer path analysis method has evolved with the signal processing method of the multi-input, multi-output system. After many years of development, theTPA method has formed many different types of sub-methods, according to the basic theory, mainly divided into conventional TPA (CTPA) [3; 4], operational TPA (OTPA) [5; 6], operational-X TPA (OPAX) [7; 8; 9], and various variants of TPA. Conventional TPA stimulates the transmission function of the direct measurement of the path through the force hammer and the reverse matrix method is used to calculate the load. 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]. For example, TPA contribution analysis for low-frequency vibration problems on bus steering wheels by [PERSON] and others has shown that exhaust systems are linked to the steer transmission function as the main cause of steer vibration [12]. To increase test effectiveness, the researchers proposed the operational TPA method [13], which represents the target point response as a combination form of the load position response, capturing only the work condition data, without the need for the transmission function measurement, thus greatly reducing testing and modeling time. During the TPA test process, all stimulus points and target response points are collected simultaneously, so that the stimulus point and target response point matrix are interconnected. The presence of interference between the inputs can cause the matrix to reverse the value problem, so that it is not possible to accurately solve the transmission function of the system. The accuracy of the method is too dependent on mathematical methods and is vulnerable to noise signal interference [14]. [PERSON] and others [5] detailed the basic theory of the operational TPA method and proposed the odd value decomposition method for the disconnection between input signals.