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Online monitoring of rotary machines, like induction motors, can effectively diagnosis electrical and mechanical faults. The origin of most recurrent faults in rotary machines is in the components: bearings, stator, rotor and others. Different methodologies based on current and vibration monitoring have been proposed using FFT and wavelet analysis for preventive monitoring of induction motors resulting in countless techniques for diagnosing specific faults, arising the necessity for a generalized technique that allows multiple fault detection. This work presents a novel methodology and its FPGA implementation for multiple fault online detection analyzing the current and vibration signals of an induction motor combining FFT and wavelet processing during its startup transient and steady state, precisely performing the identification of different faults like misalignment, unbalance, outer-race bearing defects and broken bars. The results obtained using the proposed methodology show its effectiveness providing a precise diagnosis of the induction motor condition.