An automated faults classification method based on binary pattern and neighborhood component analysis using induction motor. (15th January 2021)

• Record Type: Journal Article
• Authors: Yaman, Orhan
• Is Part Of: Measurement. Volume 168(2021)
• Publication Date: 2021-01-15
• Subjects: Acoustic signal -- Fault classification -- Binary pattern -- Neighborhood component analysis -- Broken rotor bar -- Bearing, Multilevel feature extraction; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108323
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: In this study, motor faults were detected with sound signals. Novel sound datasets were collected and comprehensively results were calculated. A high accurate acoustical fault classification method was presented. The proposed method achieved higher than 99% accuracies for the collected datasets. This situation shown effectiveness of the proposed BP and NCA based method. Abstract: Induction motors, which are widely used in industrial applications, are indispensable tools of the industry. Induction motors work in almost every part of the industry, such as production, packaging, and service. In this study, an acoustic-based method is proposed for the detection of the rotor and bearing faults of three-phase induction motors. In the first stage, two fault sound datasets were collected and these datasets are called near and far. For extracting features from these sounds, a multilevel feature generation method is presented and this method uses Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) methods together. Neighborhood Component Analysis (NCA) method was used to select the most informative features. Selected features are utilized as the input of SVM (Support Vector Machine) and KNN (K Nearest Neighborhood) classification algorithms. 99.8% classification success was achieved as a result of the SVM algorithm and the KNN algorithm reached 99.9% classification accuracy.