Broken Rotor Bar Fault Detection in Asynchronous Machines Using Vibration Analysis

Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.

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EMD and MCSA Improved via Hilbert Transform Analysis on Asynchronous Machines for Broken Bar Detection Using Vibration Analysis

2019 IEEE Milan PowerTech ◽

10.1109/ptc.2019.8810643 ◽

2019 ◽

Cited By ~ 3

Author(s):

A. E. Treml ◽

R. A. Flauzino ◽

G. C. Brito

Keyword(s):

Hilbert Transform ◽

Vibration Analysis ◽

Asynchronous Machines ◽

Broken Bar Detection

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J0450405 Fault detection method using vibration analysis for operating ventilator jet fan

The Proceedings of Mechanical Engineering Congress Japan ◽

10.1299/jsmemecj.2014._j0450405- ◽

2014 ◽

Vol 2014 (0) ◽

pp. _J0450405--_J0450405-

Author(s):

Kenta Kawahara ◽

Kouji Yamamoto ◽

Atsushi Kobayashi ◽

Takatoshi Yamagishi ◽

Atsushi Iwasaki

Keyword(s):

Fault Detection ◽

Vibration Analysis ◽

Detection Method ◽

Jet Fan

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Fault Detection for the Scraper Chain Based on Vibration Analysis Using the Adaptive Optimal Kernel Time-Frequency Representation

Shock and Vibration ◽

10.1155/2019/6986240 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14

Author(s):

Xing Zhang ◽

Wei Li ◽

Zhencai Zhu ◽

Shanguo Yang ◽

Fan Jiang

Keyword(s):

Fault Detection ◽

Vibration Analysis ◽

Large Scale ◽

Experimental Testing ◽

Operating Conditions ◽

Mining Equipment ◽

Time Frequency ◽

Failure Patterns ◽

Frequency Representation ◽

Optimal Kernel

A scraper conveyor is a key component of large-scale mechanized coal mining equipment, and its failure patterns are mainly caused by chain jam and chain fracture. Due to the difficulties with direct measurement for multiple performance parameters of the scraper chain, this paper deals with a novel strategy for fault detection of the scraper chain based on vibration analysis of the chute. First, a chute vibration model (CVM) is applied for modal analysis, and the hammer impact test (HIT) is conducted to validate the accuracy of the CVM; second, the measuring points for vibration analysis of the chute are determined based on the modal assurance criterion (MAC); and third, to simulate the actual vibration properties of the chute, a dynamic transmission system model (DTSM) is constructed based on finite element modeling. The fixed-point experimental testing (FPET) is then conducted to indicate the correctness of simulation results. Subsequently, the DTSM-based vibration responses of the chute under different operating conditions are obtained. In this paper, the proposed strategy is employed to determine the occurrence of chain faults by amplitude comparisons, while failure patterns are distinguished by the adaptive optimal kernel time-frequency representation (AOKR).

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Broken Rotor Bar Fault Detection Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.1862 ◽

2011 ◽

Vol 383-390 ◽

pp. 1862-1866

Author(s):

Li Ling Sun ◽

Kai Bin Chen

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Induction Motor ◽

New Method ◽

Broken Rotor Bar ◽

Detection Analysis

Induction motor is widely applied to people's lives and production. This paper presents the simple and sophisticated of some methods which are used to diagnose the rotor fault. After analyzing the advantage and disadvantage of these methods, this paper tells what the key of rotor fault diagnosis of induction motor is, and put forward a new method.

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