## 1 Introduction  
Moment wheel assemblies (MWAs) and control moment gyros (CMGs) have been widely used in spacecraft attitude control and large angle slewing maneuvers over the years. High-speed rotating systems use a pair of angular contact ball bearings of different sizes and capacities. Because reliability is of paramount importance, precision class bearings are selected. As one of the critical components, failure of these bearings leads to partial and total mission failure or performance degradation of the spacecraft [1, 2]. In fact, lubrication problems and retainer instability are the major causes of failure in ball bearings used in spacecraft rotating systems [3, 4]. Therefore, long-life experiments of ball bearings on the ground become an essential and effective solution, and effective methods for monitoring the operating conditions of abnormal bearings in MWAs are required [4]. A number of techniques for the detection of abnormal conditions of rolling element bearings are currently available, which include vibration and acoustics [5, 6], acoustic emission [7], lubricatingoil monitoring [8], and temperature [9]. As a sensitive and effective method, vibration and acoustic measurements are widely used to detect the defects in bearings [10]. Numerous pieces of information on the bearings can be derived from the acoustic signals [11]; thus, the health condition or faults of bearings can be obtained by analyzing the change of acoustic properties. Acoustic signals can be easily captured through non-contact measurement without additional sensor weight. Therefore, acoustic signals can be used as an early prediction and can be monitored online, and it is widely applied in the tests where vibration signals cannot be obtained [12]. Many researchers have focused on the fault diagnosis of bearings based on the acoustic measurement and analysis. [PERSON] and [PERSON] [13] studied the detectability of defects in ball bearings by the acoustic measurement method and confirmed that the acoustic measurements can detect the faults under a high rotating speed. [PERSON] and [PERSON] [14] researched the application of acoustic and vibration signals to detect defects in rolling element bearings using a statistical parameter estimation method, and the results revealed that kurtosis and crest factor from both sound and vibration signals provide better diagnostic information than the beta function parameters. [PERSON] et al. [15] presented a symmetrical dot pattern (SDP) method to diagnose bearing faults using sound signals and concluded that the SDP method is effective in detecting abnormality in bearings. [PERSON] and [PERSON] [16] extracted the descriptive statistical features from sound signals acquired from the near field area of bearings and used a decision tree to select features to diagnose faults of bearings through the machine learning approach. [PERSON] and [PERSON] [17] proposed the system of filters method to localize various degrees of gear and bearing faults using acoustic and vibration signals. [PERSON] et al. [18] proposed a synthetic detection technique for track side acoustic identification by overcoming the difficulties such as modulation of acoustic signals, Doppler effect, and weak defect frequency. From References [13; 14; 15; 16; 17; 18], it is evident that acoustic analysis is an effective method to detect the faults of bearings used in general rotor systems. However, few literatures have focused on the fault diagnosis of spacecraft bearings based on the acoustic analysis. Due to the different structural parameters and working environments, such as the composite material retainer, the light preload, and the long-life lubrication, etc., there are significant differences on the fault modes between the spacecraft bearings and the bearings used in the general rotating systems. In general bearings, the inner ring pitting, the outer ring pitting, and the ball pitting are recognized as common fault modes.