Noise elimination in current signals of three-phase induction motors, considered as energy systems for electromechanical conversion, is a critical preprocessing step for reliable condition monitoring and fault diagnosis. However, conventional wavelet-based denoising approaches often treat noise suppression as a generic filtering task, which may distort diagnostically relevant spectral components and inter-phase relationships. To address this limitation, this paper presents a physically constrained denoising framework that integrates the Clarke transformation with dyadic wavelet analysis to enable diagnostic-safe noise attenuation. The proposed method explicitly preserves frequency bands associated with supply harmonics, mechanical phenomena, and fault-related sidebands, while enforcing inter-phase coherence and zero-sequence stability in the Clarke domain. Wavelet parameters are selected through a diagnostic-oriented multi-criteria framework that jointly balances disturbance attenuation, harmonic fidelity, coherence retention, zero-sequence stability, and time-domain waveform integrity. Experimental validation using real three-phase induction motor current measurements under steady-state conditions shows that the proposed framework achieves noise reduction ratios of approximately 8–10 dB, while preserving the amplitudes of the main harmonic components with deviations below ${10}^{-3}$ dB. These results demonstrate that the proposed method provides a robust and physically consistent preprocessing stage for current-based monitoring of three-phase AC machines.