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.Mathematics 2019, 7, 783 2 of 19 been presented [8][9][10][11]. Wavelet transform (WT) is a windowing technique with variable regions in both time and frequency. It increases time resolution at higher frequencies and frequency resolution at lower frequencies [12][13][14]. Although promising results have been obtained, several aspects have to be taken into account; for instance, WT performance can be affected under noisy conditions and adequate selection or choice of the level of decomposition and the mother wavelet have to be performed according to the input signal in order to carry out a suitable analysis [15].In order to avoid the configuration of different parameters in WT, recent works have presented the use of the empirical mode decomposition (EMD) method [16][17][18][19], which is an adaptive decomposition algorithm or method capable of evaluating non-linear and non-stationary signals. Due to the potential of the EMD method as a signal processing technique, several works have tried to make a hardware implementation for online processing. In [20], the EMD in a C program is processed by a field programmable gate array (FPGA). Another hardware implementation of EMD by fusing an FPGA and a digital signal processor (DSP) is proposed by [21]. The FPGA implementation of EMD using sawtooth transform instead of the spline cubic is presented in [22]. The EMD method using the spline cubic interpolation into an FPGA is presented in [23]. Recently,in [24], another design for EMD on an FPGA platform is presented, where the user can change different aspects of the implementation (e.g., data lengths, extrema extraction methods, envelope generation methods, and stopping criterion methods).Despite the great capabilities of the EMD method, several studies have shown that this method suffers from a problem named mode mixing [25], compromising the correct analysis of the modes of a signal. To overcome this problem, the ensemble empirical mode decomposition (EEMD) and, subsequently, an improved te...