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The paper presents a novel approach to solving problems involved in the application of a genetic algorithm to determine the optimal tyre pitch sequence to reduce the tyre air-pumping noise which is generated by the repeated compression and expansion of the air cavity between the tyre pitch and the road surface. The genetic algorithm was used to determine the optimal tyre pitch sequence with a low level of tyre air-pumping noise using the image-based air-pumping model. In the genetic algorithm used in previous studies, there are a number of problems related to the encoding structure and the selection of an objective function. This paper proposes a single encoding element with five integers, a divergent objective function based on an evolutionary process, and the optimal evolutionary rate based on the Shannon entropy in an attempt to solve the problems. The results of the proposed genetic algorithm with an evolutionary process are compared with those of a randomized algorithm. The randomized algorithm is a traditional method used to obtain the tyre pitch sequence. It was confirmed that the genetic algorithm more effectively reduces the peak value of the predicted tyre air-pumping noise. The consistency and cohesion of the obtained simulation results are also improved.

The sound quality for the axle-gear whine sound in a sports utility vehicle was investigated in terms of psychoacoustics. Aure's tonality was considered as the sound metric for the expression of the tonality of gear whine sound in a previous research. However, it did not prove possible to use Aure's tonality successfully as a sound metric for the tonal impression. Aure's tonality did not express well the tonal impression of the gear whine sound as the whine sound is a nonstationary signal with both frequency and amplitude modulation. In this study, the new method for the tonality evaluation for a non-stationary signal is presented. It is developed based on the prominence ratio, tonality impression function and lower threshold level. It improves the accuracy and reliability of the sound quality index being used for the sound quality evaluation of the axle-gear whine sound.

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As wheels are important components of train operation, diagnosing and predicting wheel faults are essential to ensure the reliability of rail transit. Currently, the existing studies always separately deal with two main types of wheel faults, namely wheel radius difference and wheel flat, even though they are both reflected by wheel radius changes. Moreover, traditional diagnostic methods, such as mechanical methods or a combination of data analysis methods, have limited abilities to efficiently extract data features. Deep learning models have become useful tools to automatically learn features from raw vibration signals. However, research on improving the feature-learning capabilities of models under noise interference to yield higher wheel diagnostic accuracies has not yet been conducted. In this paper, a unified training framework with the same model architecture and loss function is established for two homologous wheel faults. After selecting deep residual networks (ResNets) as the backbone network to build the model, we add the squeeze and excitation (SE) module based on a multichannel attention mechanism to the backbone network to learn the global relationships among feature channels. Then the influence of noise interference features is reduced while the extraction of useful information features is enhanced, leading to the improved feature-learning ability of ResNet. To further obtain effective feature representation using the model, we introduce supervised contrastive loss (SCL) on the basis of ResNet + SE to enlarge the feature distances of different fault classes through a comparison between positive and negative examples under label supervision to obtain a better class differentiation and higher diagnostic accuracy. We also complete a regression task to predict the fault degrees of wheel radius difference and wheel flat without changing the network architecture. The extensive experimental results show that the proposed model has a high accuracy in diagnosing and predicting two types of wheel faults.