Abstract
<div class="section abstract"><div class="htmlview paragraph">The noise tests of electric motor systems serve as the foundation for studying their acoustic issues. However, there is currently a lack of corresponding method for identifying key noise characteristics, such as the noise frequency range that needs to be considered, the significant noise order, and the resonance band worth paying attention to, based on experimental test data under actual operating conditions. This article proposes a method for identifying the noise characteristics of an electric motor system based on tests conducted under distinct operating conditions, which consists of three parts: identifying the primary frequency range, the significant order, and the important resonance band.</div><div class="htmlview paragraph">Firstly, in order to extract noise with the primary energy, the concept of noise contribution is introduced. The primary frequency range and the significant order under a specific operating condition can be obtained after extraction. Then, for the purpose of identifying the important resonance band, a method called assumed order scanning is proposed derived from the characteristic that noise orders reach the local maximum when they are passing through resonance bands. Finally, noise characteristics identification is performed on the experimental test of an electric sit-stand desk, which validates the effectiveness of the proposed method.</div><div class="htmlview paragraph">In contrast with the usual way to get noise characteristic from experimental data, the method presented in this article effectively utilizes data to acquire valuable information, facilitating quantitative analysis and batch processing. Furthermore, this approach can offer guidance for noise control requirements in engineering practice. This method is applicable to noise tests related to electric motors and provides a reference for data processing in such tests.</div></div>