Monitoring the condition, detecting faults, and modeling the degradation of industrial equipment are important challenges in Prognostics and Health Management (PHM) field. Our solution to the challenge demonstrated a multi-stage approach for detecting faults in a group of identical industrial equipment, composed of four identical interconnected components, that have been deployed to the evolving environment with changes in operational and environmental conditions. In the first stage, a stacked ensemble of heterogeneous classifiers was applied to predict the state of each component of the equipment individually. In the second stage, a low pass filter was applied to smoothen the predictions cast by stacked ensembles, utilizing temporal information of the prediction sequence. © ESREL2020-PSAM15 Organizers. Published by Research Publishing, Singapore.