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A paradigm shift in the standard operating procedures (SOP) is underway in the reliability and health management industry. As the community transitions from traditional preventive maintenance procedures to modern predictive or health-based management systems, areas such as efficient online monitoring and diagnosis schemes based on real-time observations have emerged as key research subjects for engineers. Most diagnostic systems require data from both healthy and faulty conditions in order to properly train their classification algorithms. However, in many situations, normal signals are acquired easily while fault samples are difficult to be gained. In this paper, we present a diagnosis scheme based on the least squares support vector machines (LS-SVM). Our modified LS-SVM algorithm is a one-class novelty detector which can differentiate between a normal and faulty condition based only on the normal samples which are easily available. We diagnose a growing crack fault on a planetary gear plate mounted aboard a UH-60 Blackhawk aircraft using this approach. Comparisons drawn with other contemporary approaches lean favorably towards the viability of the suggested novelty detector.