Microvibrations originating from onboard disturbance sources can lead to a range of issues, including a decrease in satellite pointing accuracy, image distortion and blurring. Therefore, reaction wheels emerge as the primary sources of disturbance noise. This paper employs an experimental approach based on the real dynamics of rotating reaction wheel assembly, closely simulating on-orbit configurations to measure noise responses transferred to the satellite structure. An assessment of noise response behaviour, incorporating a comprehensive understanding of the factors influencing the levels, was conducted on a proto-flight satellite for three reaction wheels. Initially, reaction wheel assemblies underwent individual iterative balancing to reduce mass deviations. Subsequently, amplitude-time responses at different rotational speeds of reaction wheel assemblies (RWA) disturbance noise were measured. The experimental results demonstrate that each individually balanced reaction wheel generates independent perturbation noise level due to manufacturing imperfections. Hence, the necessity of wheels testing for accurate prediction and mitigation of disturbance levels is crucial, especially for payloads sensitive to microvibrations. Furthermore, increasing wheel speeds proportionally amplify disturbance noise levels. Therefore, implementing an optimised mission attitude control profile with lower rotation speeds of reaction wheels effectively reduces microvibration levels which minimises risks to payload performance and reduce power consumption.

Techniques for machine condition monitoring and diagnostics are gaining acceptance in various industrial sectors. They have proved to be effective in predictive or proactive maintenance and quality control. Along with the fast development of computer and sensing technologies, sensors are being increasingly used to monitor machine status. In recent years, the fusion of multisensor data has been applied to diagnose machine faults. In this study, multisensors are used to collect signals of rotating imbalance vibration of a test rig. The characteristic features of each vibration signal are extracted with an auto-regressive (AR) model. Data fusion is then implemented with a Cascade-Correlation (CC) neural network. The results clearly show that multisensor data-fusion-based diagnostics outperforms the single sensor diagnostics with statistical significance.

Objective: A basic issue in randomized controlled trials (RCTs) is whether we can safely assume comparability between groups at baseline with respect to all potentially important prognostic factors. In other words, did randomization work sufficiently well? In small trials balanced allocation procedures are employed, whereas in large-scale trials simple randomization will do. The question is: When should balancing be considered?

Methods: We performed a simulation study in which we varied the number of categories in the prognostic factors and the number of patients.

Results: Simulation showed that, in all instances, a balancing procedure almost always led to perfect or almost perfect balance, while the imbalance with simple randomization was larger. To study the effect of balanced and random allocation on subgroup analyses in our OME trial, we compared the quotient of the width of the confidence intervals (CI). The widest CI in random allocation over the 13 hospitals was on average 13% wider than in balanced allocation.

Conclusion: Investigators should always consider balanced allocation, especially in categories with a low number of patients and when subgroup analysis over many categories is requested.