Induction motors, widely used in electromechanical equipment of mining enterprises, are susceptible to failure due to frequent starts, overloads, and wear, leading to accidents and economic losses. Induction motors are one of the main sources of kinetic energy in industry and agriculture. Motor failure leads to shutdown of the technological process and reduced efficiency, requiring regular monitoring. Traditional diagnostic methods based on the analysis of individual signals and classic machine learning with manual feature selection are insufficiently reliable under variable operating conditions and are highly susceptible to human factor. This paper proposes an approach to diagnosing induction motor faults based on a deep residual network using signal analysis, deep and transfer learning, and information fusion. Various three-phase current input strategies are implemented, and a model capable of automatically extracting informative deep features from the current signal is constructed. The experimental results confirm that the proposed deep learning-based model provides higher diagnostic accuracy compared to traditional machine learning algorithms.