Inter-turn short circuit (ITSC) fault is one of the critical electrical faults in induction motors that affects the reliability of many industrial applications. Although the use of data-driven fault detection techniques have gained much interest, the main deterrent in using these approaches in detecting ITSC faults is in the generalization and robustness of the diagnosis. In this paper, a data-driven on-line fault detection framework, incorporated with multi-feature extraction/selection and multi-classifier ensemble is proposed, capable of detecting ITSC faults in induction motors (IMs) that subjected to variable operating conditions. By using the synchronous time series signals collected from the machines, multiple feature extraction/selection is explored to find the sensitive faulty features, and the different types of classification strategies is used to increase the diversity of single based models. With the increased diversity of the base learners, the fault detection accuracy is expected to be enhanced and the robustness can be guaranteed. The framework was implemented and tested using real data collected from a designed test bed, with the experimental results showing the effectiveness of the framework in detecting ITSC faults in IMs.