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 and is based on a distribution-free mutual information (MI) estimator. Our scheme does not require prior faulty examples and can be applied distribution-free over a large class of system models. Our core contributions are twofold. First%2C we demonstrate the connection between fault detection%2C model drift detection%2C and testing independence between two random variables. Second%2C we prove several theoretical properties of the proposed MI-based fault detection scheme: (i) strong consistency%2C (ii) exponentially fast detection of the non-faulty case%2C and (iii) control of both significance levels and power of the test. To conclude%2C we validate our theory with synthetic data and the benchmark dataset N-CMAPSS of aircraft turbofan engines. These empirical results support the usefulness of our methodology in many practical and realistic settings%2C and the theoretical results show performance guarantees that other methods cannot offer.?quality=80&w=800)
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