Evaluation and Classification of Double Bar Breakages Through Three-Axes Vibration Sensor in Induction Motors

GÖKTAŞ T.

IEEE SENSORS JOURNAL, vol.22, no.13, pp.13602-13611, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 13
  • Publication Date: 2022
  • Doi Number: 10.1109/jsen.2022.3176059
  • Journal Name: IEEE SENSORS JOURNAL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.13602-13611
  • Keywords: Double bar breakages, induction motor, fault diagnosis, neural network, vibration analysis, vibration sensor, FAULT-DIAGNOSIS, MACHINE
  • Dokuz Eylül University Affiliated: Yes

Abstract

The relative positions of broken bars can potentially change the current signature content, filter some of the components and hence lead to misleading in diagnostic process. Especially, if the positions of broken bars are in half pole pitch distance, the characteristic sidebands harmonics get lower which leads to decrease diagnostics ability of stator current based analysis. This paper proposes to analyze double bar breakages faults by monitoring the three-axes vibration (-x, -y, and -z) signals through an accelerometer in detail. The characteristics fault signatures are presented in axial -x, radial -y and gravity -z axis vibrations and a neural network-based classifier (Multi-Layer Perceptron) is utilized to classify the type of double bar breakages. The findings are verified through the experiments. It is shown that some of characteristics fault signatures such as 2sf(s), 3sf(s) and 2f(r)+/- 2f(s) at corresponding vibration spectra can provide more reliable result to detect and classify the broken bar fault in induction motors (IMs).