Ontology-Based Noise Source Identification and Key Feature Selection: A Case Study on Tractor Cab

Abstract

This paper proposes an ontology-based noise source identification method, establishes an ontology knowledge expression model in the field of noise, vibration, and harshness (NVH), and provides an extensible framework for sharing noise diagnosis knowledge in the field. Based on the key features extracted from the noise and vibration signals at different positions and the prior knowledge, mechanical engineers can construct an ontology rule and locate noise sources by identifying the intrinsic relationship between signal characteristics through ontology reasoning. A case study is conducted to demonstrate the effectiveness of the proposed method in resolving the problem of integrating multisource heterogeneous knowledge and exchanging noise diagnosis knowledge information in the field of NVH for agricultural machines. Thus, our study facilitates the sharing and reuse of knowledge and advances the development of intelligent noise diagnosis expert systems to a certain extent.