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Given servo feedback information, such as torque, velocity, acceleration, and power, one can predict the likelihood of an issue with the servo. By knowing when the servo is likely to fail, one can reduce downtime and prevent potential damage to the system from a faulty servo. By applying a simple Gaussian probability density function to a set of trained features, the classification of an anomalous servo can be determined with little computational cost.

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