###### Abstract  
The aim of this study was to examine the health diagnosis classification method of quadcopter structures with different mixed faults. The loosening of the motor mount, damage to the propeller, and the loosening of the arm mount were the main fault conditions investigated. Data were first acquired under non-fault conditions and the conditions of the three types of fault. Then, the features of the vibration and pulse width modulation signals were extracted by root mean square, standard deviation, and sample entropy. Moreover, the features of the audio signal were extracted by wavelet scattering, which contains time-frequency domain information that provides significant power for classification. In this paper, we propose a simple machine learning method, based on the k-Nearest Neighbor (kNN), not only for classification but also demonstrating the efficacy of the features. To test the limits of accuracy, different configurations of kNN parameters are deployed, in addition to the features. In summary, as a result of the highly efficacious features, despite mixed fault conditions, the accuracy reached 90.73%. This method improves the accuracy of mixed faults' classification and maintains a certain level of classification effectiveness.  
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For quadcopter diagnosis, [PERSON] et al. (2019) used the signal collected from the flight controller to diagnose the health of drones. In addition to the non-fault condition, a loose motor screw and damaged propeller were used to develop a diagnosis system. A self-organizing map [4] and kNN were applied to the classification and the diagnosis of the fault level [5]. [PERSON] et al. (2018) integrated a drone, flight controller, Raspberry Pi, and temperature sensors to detect whether the temperatures of motors were too high, and, if so, the system landed automatically. In addition, the temperature assessment system integrated reinforcement learning to ensure the reliability of the decisions [6].  
In summary of the above review, we found that it is almost mandatory to add additional sensors to elevate the rate of classification because the recording of intrinsic sensors requires computation power, and the level of sensors may not be as good as required. In addition to sensors, melded faults have rarely been considered by other researchers. Therefore, the purpose of this research was to improve the quadcopter health diagnosis system, especially regarding combined faults and the use of different sensors. Flight and noise data were collected for analysis and to extract features. After processing the data, we established a machine learning algorithm that was used for classification, for which the classification accuracy was expected to be more than 80-90% for combined faults. The aim of this system was not only to shorten the time required for people to check quadcopters, but also to detect potential and invisible damage to the quadcopters' components.  
The remainder of the article is outlined as follows. In Section 2, several statistics methods used to extract the features are introduced. The equipment and experiment are outlined in Section 3, which also explains flight test in detail. In Section 4, the processes of signal and feature extraction are explained, and the discrepancies between different states are demonstrated. The results are presented and discussed in Section 5, and conclusions are presented in Section 6.