Condition monitoring and fault diagnostics in rotorcraft have significant effect on improving safety level and reducing operational and maintenance costs. In this paper, a new method is proposed for fault detection and diagnoses of AH-64D (Apache helicopter) tail rotor drive-shaft problems. The proposed method depends on decomposing signal into different frequency ranges using mother wavelet. The most informative part of the vibration signal is then determined by calculating Shannon entropy of each part. Bispectrum is calculated for this part to investigate quadratic nonlinearities in this segment. Then, search algorithm is used to extract minimum number of indicative features from the bispectrum, which are then fed to classification algorithms. In order to quantitatively evaluate the proposed method, six classification algorithms are compared against each other such as fine K-nearest neighbor (KNN), cubic KNN, quadratic discriminant analysis, linear support vector machine (SVM), Gaussian SVM, and neural network. Comparison criteria include accuracy, precision, sensitivity, F score, true alarm, recall, and error classification accuracy (ECA). The proposed method is verified using real-world vibration data collected from a dedicated AH-64D helicopter tail rotor drive train (TRDT) research test bed. The proposed algorithm proves its ability in finding minimum number of indicative features and classifying the shaft faults with superior performance.

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