Condition-based monitoring (CBM), with its tremendous scope in improving operational cost efficiency, has become an important component in most industrial machine maintenance frameworks. While many strategies already exist for CBM, this paper presents a novel pattern analysis framework, which focuses on improving reliability of real-time fault detection. The introduced framework is based upon a novel feature selection method, which selects good, reliable, and consistent features. Graphical indices are proposed in this paper which try and quantify the goodness of features and accordingly rank them for the feature selection procedure. The presented algorithm also takes into account the possibility of corrupt data creeping in during data collection, and takes necessary steps to discard them. Additionally, novel methods for organizing training data and a method for sensitive position identification for placing sensor(s) have also been introduced, for further improving the quality of selected features. To validate the framework, experiments have been performed on an air compressor for real-time detection of leakage inlet valve fault and leakage outlet valve fault, and also on an induction motor for detecting presence of faulty bearings. The findings clearly show substantial improvement in fault detection performance and confirm the effectiveness of proposed framework.