Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors

Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

Download Full-text

Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2014.2373513 ◽

2015 ◽

Vol 64 (5) ◽

pp. 1118-1128 ◽

Cited By ~ 48

Author(s):

Ricardo Valles-Novo ◽

Jose de Jesus Rangel-Magdaleno ◽

Juan Manuel Ramirez-Cortes ◽

Hayde Peregrina-Barreto ◽

Roberto Morales-Caporal

Keyword(s):

Empirical Mode Decomposition ◽

Induction Motors ◽

Decomposition Analysis ◽

Squirrel Cage ◽

Mode Decomposition ◽

Broken Bar Detection

Download Full-text

Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Mathematics ◽

10.3390/math7090783 ◽

2019 ◽

Vol 7 (9) ◽

pp. 783 ◽

Cited By ~ 5

Author(s):

Martin Valtierra-Rodriguez ◽

Juan Amezquita-Sanchez ◽

Arturo Garcia-Perez ◽

David Camarena-Martinez

Keyword(s):

Feature Extraction ◽

Empirical Mode Decomposition ◽

Condition Monitoring ◽

Induction Motors ◽

A Priori ◽

Digital Signal ◽

Decomposition Methods ◽

Ensemble Empirical Mode Decomposition ◽

Processing Technique ◽

Mode Decomposition

Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.

Download Full-text