A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

Abstract

Abstract Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods. To address this challenge, this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals. For this purpose, a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands. Then, the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band (ILFB) containing fault features from the divided subfrequency bands. Furthermore, an improved grasshopper optimization algorithm‐based stochastic resonance (SR) system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost. Finally, the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis. Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method. The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner. Furthermore, the advantages of the proposed method are highlighted by comparison with other methods.

Keywords

• fault diagnosis
• frequency band segmentation
• adaptive stochastic resonance
• improved grasshopper optimization algorithm
• synthetic quantitative index