Abstract
Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.
Highlights
Moment wheel assemblies (MWAs) and control moment gyros (CMGs) have been widely used in satellite attitude control and large angle slewing maneuvers over the years
Satellite in-orbit failure statistics show that a large part of satellite failures come from the attitude and orbit control sub-system, and more than half of these failures are caused by the bearings [4]
The envelope spectrum contains rich information, manual identification requires professional knowledge and experience of the diagnostic staff and a large workload. To solve these problems, based on the bearing vibration experimental data, this paper proposes a method for calculating the characteristic frequency ratio, which is used to quantitatively evaluate bearing failures, and automatic fault diagnosis and quantitative diagnosis processes are further proposed
Summary
Moment wheel assemblies (MWAs) and control moment gyros (CMGs) have been widely used in satellite attitude control and large angle slewing maneuvers over the years. The envelope detection (ED) method is one of the most commonly used and effective methods in vibration-based bearing fault diagnosis [14,15], which was presented by Mechanical. Tsao et al [23] introduced empirical mode decomposition to select an appropriate resonant frequency band for characterizing the characteristic frequencies of bearing faults by using the envelope analysis subsequently, and the experimental results showed that the proposed method can diagnose the bearing fault types efficiently and correctly. The envelope spectrum contains rich information, manual identification requires professional knowledge and experience of the diagnostic staff and a large workload To solve these problems, based on the bearing vibration experimental data, this paper proposes a method for calculating the characteristic frequency ratio, which is used to quantitatively evaluate bearing failures, and automatic fault diagnosis and quantitative diagnosis processes are further proposed.
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