# Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network
## 1 Introduction
[PERSON] [15] conducted an analysis of the contribution to the driver's indoor noise by using the operational TPA method at a speed of 130 km/h for the shift to the shelf region, and the results showed that the internal noise of the driver is primarily derived from structural stimuli in the shifting to the shaft region, while the air stimulus only contributes to the noise in the cabin in the high frequency band [16]. The OPAX method requires the acquisition of working status data, supplemented by a small amount of system frequency function data, to identify the workload on the basis of a parametric load recognition model [17]. The method is time-efficient, and analytical accuracy is higher than the working condition TPA, but there is still a gap with the traditional TPA. At the same time, the OPAX method requires researchers to have a clear understanding of the test data or model used, thereby determining the accuracy of the OPAX model established [18]. [PERSON] and others [19] use the OPAX method for dynamic rigidity recognition of the suspension of a full-time quad-drive SUV, and use the recognition results to establish the NVH performance simulation model of the drive system. From the above analysis of the characteristics of various methods of transmission path analysis, it can be seen that the choice of the TPA method is based on the combined consideration of the study object, the study objective, and other multiple factors. Computer science has driven the development of neural networks [20; 21; 22; 23; 24], and networks of various structures have been widely used [25; 26; 27], such as convolutional neural network (CNN) [28; 29], recurrent neural network (RNN) [30; 31], and variational autoencoder (VAE) [32; 33]. Neural networks have powerful linear alignment capabilities that can quickly and accurately align data. Each neuron in a neural network has weights and biases, and they are arranged in various layers. The network updates weights and bias using backpropagation errors, which lowers the loss function's value. The algorithm determines the weight gradient and loss function bias, then modifies the parameters in accordance with the gradient descent principle. After a sufficient number of training iterations, the neural network will be able to accurately output the predicted results. During the training process, the neural network will gradually adjust the weights and biases to minimize the loss function. Measuring the system's transfer function and collecting data on different operating conditions are necessary in order to ascertain the path points' contribution in the TPA problem. There will be an ill-posed problem in reverse solving when the number of unknowns is greater than the number of equations, and the solution for the path point contribution obtained is not unique when solving the path contribution from the target data in reverse when the number of target points is less than the number of path points. Therefore, by fusing neural networks with conventional TPA testing, we suggest a noise source diagnosis approach for TPA-NN. This article's primary contribution is as follows: (1) Choose appropriate reference points and path points based on the rail transit converter cabinet's characteristics. Apply conventional TPA techniques, such as the reciprocity of the transfer function and the inverse matrix method. Conduct experiments to verify the converter cabinet system's target point signal and transfer function. (2) Under representative working conditions of the converter cabinet, forward solve the accurate contribution of each path point in the following order: reference point, path point, target point. (3) Integrate the neural network and the TPA technique, then feed the acquired contribution data into the neural network to begin training. Finally, the contribution of the neural network reconstruction path was compared with the actual contribution results in order to confirm the viability and accuracy of the TPA-NN method.