Fault diagnosis of single-phase induction motor based on acoustic signals
By A. Głowacz. Mechanical systems and signal processing, 2019
320 citations recorded by the source corpus.
Abstract The paper presents description of bearing, stator and rotor fault diagnostic methods of a single-phase induction motor. The presented methods use acoustic signals. Five states of the single-phase induction motor were analysed: healthy motor, motor with shorted coils of auxiliary winding and main winding, motor with shorted coils of auxiliary winding, motor with broken rotor bar and faulty ring of squirrel-cage, motor with faulty bearing. A method of feature extraction of acoustic signals – SMOFS-22-MULTIEXPANDED (Shortened Method of Frequencies Selection Multiexpanded) was developed and implemented. The SMOFS-22-MULTIEXPANDED was implemented as feature extraction method of acoustic signals. Classification step was performed using the NN (the Nearest Neighbour) classifier. The proposed methods had good results for diagnosis of bearing, stator and rotor faults of the single-phase induction motor. The developed approach can find applications for fault diagnosis of other types of rotating machines.
Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/ad443c052e38f2c3d8e70ceaf8a24ae9be34250f
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In Control and Systems Engineering
In Mechanical Engineering
In Mechanics of Materials
Citations in the corpus
- Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning (2021, methodology): “Wang et.al [23] proposed a preliminary DT reference model for rotating machinery.”