Fourier transformation (FT) and Multiple Signal Classification (MUSIC) method suffer the insufficient ability in diagnosing broken rotor bar (BRB) fault using short-time data. Theoretical and simulation analyses show that the Optimum Resolution of Prescient Direction (ORPD) algorithm has the best frequency resolution performance due to a priori knowledge. The main objective of this paper is to detect BRB faults in induction machines using a condition monitoring architecture based on ORPD algorithm. In the proposed application, the ORPD algorithm with the best frequency resolution performance is used to estimate the fault-sensitive frequencies in the stator current signature. The prior information of BRB fault characteristic distribution is used to construct a weighting matrix in the ORPD algorithm, for acquiring the lowest signal-to-noise ratio resolution threshold. Once frequencies are estimated, their corresponding amplitudes are obtained by using the least squares estimator. The proposed methods were tested using experimental induction motors with different fault severity under the effect of several load levels or supply frequencies. Two types of power supply modes are considered: main and inverter. The results show that compared with traditional method which uses FT and MUSIC algorithm for fault diagnosing, the method based on ORPD algorithm has a higher frequency resolution and identification ability with short-time data, and still has good diagnostic performance even under light loading and lower supply frequencies.