Abstract
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.
Highlights
In recent years, the development of new systems for monitoring the condition of rotating machines has become an important issue for different fields such as academia and industry
system on a chip (SoC) solution for condition monitoring of induction motors since the resources used in the field programmable gate array (FPGA) do not exceed the 25% of the available ones; a smaller FPGA could be used
This paper presents the FPGA implementation for the complete ensemble empirical mode decomposition (CEEMD) method and its use as a SoC
Summary
The development of new systems for monitoring the condition of rotating machines has become an important issue for different fields such as academia and industry. The presence of a fault in an induction motor can lead to setbacks and substantial economic losses, early detection of faults becomes an important task. The problem with the broken bar fault is that the motor can keep operating with apparent normality, this fault can cause different problems: changes in the current consumption, unwanted vibrations, and damages to other bars [3,4,5]. A great number of methods are available in the literature for condition monitoring of induction motors, and many of them have been focused on early fault detection through vibration analysis and motor current signature analysis (MCSA) [6]. In order to analyze non-stationary signals, short-time Fourier transform (STFT) can be employed; yet, its frequency resolution depends mainly on the selected time window, which in some cases cannot be adequate for transient signals
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