This study addresses early fault detection challenges in electromechanical equipment through multidimensional analysis of heterogeneous diagnostic data. We propose a Principal Component Analysis (PCA)-based methodology integrating vibration and current signal features including spectral amplitudes at shaft rotational frequencies, statistical moments (skewness, kurtosis, variance), and envelope characteristics. The approach enables intuitive state visualization via loading and scores plots while achieving robust fault classification despite ambiguous individual parameter responses through context-dependent feature significance analysis. Experimental validation via bench tests demonstrates differential parameter sensitivity, where features like current kurtosis show fault-specific importance. Results highlight the critical role of retaining seemingly weak parameters in diagnostic vectors and confirm PCA’s efficacy in building reference state spaces for predictive maintenance. The methodology bridges technical diagnostics with sociotechnical decision-making frameworks.