## 3 Diagnosis and Verification Analysis of Fault Signal Based on Acoustic Testing of MWA
Nevertheless, each MWA displays different health statuses during its operation. In order to get the real-time status of each MWA, it is essential to monitor the various kinds of abnormal states and even the unknown faults effectively. Due to different parameters sensitive to different failures, faults might not be identified completely by using a single parameter. As a result, a monitoring approach of the acoustic state is put forward in the paper based on the clustering fusion of numerous acoustic parameters. In the light of every acoustic parameter's physical meaning, different parameters present a distinct sensitivity and stability to different failure modes [29]. For example, root mean square (RMS) is sensitive to the failure of wear [30], while kurtosis is sensitive to failure of impact [31]. Additionally, as studied earlier on, the MWA might enhance the acoustic signal in the range of frequency that human ears are sensitive to. Hence, the sharpness reflected in the sound quality of MWA is introduced as an acoustic parameter in this paper, which can be used to characterize the sensitivity and stability for different kinds of faults. In this paper, the acoustic parameters obtained from normal MWAs, including RMS, kurtosis, and sharpness, are applied for health monitoring, and the threshold for the abnormal state is constructed by information fusion. Considering the lack of actual fault samples and the deviation of the operating parameters of different flywheels, a flywheel condition assessment method based on normal sample clustering is proposed in this paper. The method is to determine the normal boundary and the safety domain of the parameter under normal conditions based on the sample data of the normal flywheel operation. The boundary is then used to evaluate the deviation of the subsequent monitoring samples to obtain information on the condition of the flywheel. A single parameter has sensitivity to certain faults. Therefore, it is necessary to comprehensively utilize multiple parameter fusions to monitor the flywheel anomalies. Information fusion includes clustering, fuzzy theory, neural network, support vector machine, and so on. In this paper, the clustering method with a high robustness and fast calculation speed is selected to fuse the flywheel condition parameters. Among many clustering methods, K-Medoids clustering is widely used due to its strong robustness, anti-noise ability, and its ability to handle abnormal values [30]. From the perspective of engineering applications, K-Medoids clustering also has a good convergence and time complexity, and the effect obtained in global searches is very good. In general, K-medoids is applicable to small sample clustering analysis and is effective in reducing disturbance of noise and outliers [32]. Therefore, the K-medoids clustering idea was adopted here. This paper clusters the normal flywheel data of the same model to obtain the safety domain of the flywheel operation, which is used as the standard for fault identification. The calculation processes are as follows:  
(1). Preprocessing of Noise Data  
The distance between the microphone and the flywheel is 0.9 m in this paper. There is a certain instability during the test due to the large wavelength of the low frequency noise. Therefore, the waves with a half wavelength greater than 0.9 m are filtered out, and the signal is subjected to high-pass filtering of 200 Hz. (2). Normalization of DataIn this paper, the condition of the flywheel is described by the root-mean-square value \(R\), kurtosis \(K\), and sharpness \(S\). However, the magnitudes of these three parameters are not consistent. Hence, the data is normalized prior to clustering. Figure 6:1  
where \(X_{max}=max\{x_{i}\}\), \(X_{min}=min\{x_{i}\}\). It is worth noting that the parameters of the test sample that are used for evaluation also need to be corrected according to Equation (7) by using the corresponding normal samples \(X_{max}\) and \(X_{min}\).