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This paper presents a practical framework for autonomous monitoring of industrial equipment based on novelty detection. It overcomes limitations of current equipment monitoring technology by developing a “generic” structure that is relatively independent of the type of physical equipment under consideration. The kernel of the proposed approach is an “evolving” model based on unsupervised learning methods (reducing the need for human intervention). The framework employs procedures designed to temporally evolve the critical model parameters with experience for enhanced monitoring accuracy (a critical ability for mass deployment of the technology on a variety of equipment/hardware without needing extensive initial tune-up). Proposed approach makes explicit provision to characterize the distinct operating modes of the equipment, when necessary, and provides the ability to predict both abrupt as well as gradually developing (incipient) changes. The framework is realized as an autonomous software agent that continuously updates its decision model implementing an unsupervised recursive learning algorithm. Results of validation of the proposed methodology by accelerated testing experiments are also discussed.