Function-Based Analysis and Redesign of a Flyable Electromechanical Actuator Test Stand

2010 ◽  
Author(s):  
Michael T. Koopmans ◽  
Irem Y. Tumer
Keyword(s):  
Test Stand ◽  
Remaining Useful Life ◽  
System Level ◽  
Successful Implementation ◽  
Functional Modeling ◽  
Electromechanical Actuators ◽  
Design Changes ◽  
Effective System ◽  
Data Output ◽  
Useful Life

Critical faults prevent electromechanical actuators (EMAs) from controlling primary flight surfaces aboard commercial and military human air/spacecraft. However, the efficiency and simplicity of the EMAs makes them appealing for use. For successful implementation, diagnostic and prognostic techniques identifying these critical faults must be optimized. This paper builds the foundation for the design of a second-generation test stand whose aim is to inject known EMA faults and record the data output while onboard an aircraft. First, an overview of faults is presented. Next, functional modeling is introduced as an effective system level representation to implement early design changes. Specifically, functional modeling is proposed to isolate functions of the test stand that can affect faulted and nominal actuator data collection through violations of post-processing statistical assumptions. The data collected from the EMA test stand will be used for actuator prognostic purposes and therefore must closely represent a full-scale actuator installation. This methodology will increase experiment validity, verifiable conclusions made regarding actuator remaining useful life, and overall system reliability.

Methodologies for system-level remaining useful life prediction

2016 ◽  
Vol 154 ◽  
pp. 8-18 ◽  
Author(s):  
Hamed Khorasgani ◽  
Gautam Biswas ◽  
Shankar Sankararaman
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
System Level ◽  
Useful Life

scholarly journals System-level Prognostics Based on Inoperability Input-output Model

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Ferhat Tamssaouet ◽  
Thi Phuong Khanh Nguyen ◽  
Kamal Medjaher
Keyword(s):  
Production Systems ◽  
Health Management ◽  
Computational Effort ◽  
Point Of View ◽  
Remaining Useful Life ◽  
System Level ◽  
Input Output ◽  
New Approach ◽  
Useful Life ◽  
Traditional Approaches

Nowadays, the modern industry is increasingly demanding the availability and reliability of production systems as well as the reduction of maintenance costs. The techniques to achieving these goals are recognized and discussed under the term of Prognostics and Health Management (PHM). However, the prognostics is often approached from a component point of view. The system-level prognostics (SLP), taking into account interdependencies and multi-interactions between system components, is still an underexplored area. Inspired from the inoperability input-output model (IIM), a new approach for SLP is proposed in this paper. The inoperability corresponds to the component’s degradation, i.e. the reduction of its performance in comparison to an ideal reference state. The interactions between component degradation and the effect of the environment are included when estimating the inoperability of components and also when predicting the system remaining useful life (SRUL). This approach can be applied to complex systems involving multi-heterogeneous components with a reasonable computational effort. Thus, it allows overcoming the lack of scope and scalability of the traditional approaches used in PHM. An illustrative example is presented and discussed in the paper to highlight the performance of the proposed approach.

scholarly journals Fault Diagnosis And Prognosis Of Satellite Attitude Control System With Reaction Wheels and Control Moment Gyros

2021 ◽  
Author(s):  
Afshin Rahimi
Keyword(s):  
Fault Detection ◽  
Complex Systems ◽  
Success Rate ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
System Level ◽  
Control Moment ◽  
Fault Prognosis ◽  
Useful Life ◽  
And Control

Condition-based maintenance (CBM) and prognostics and health management (PHM), as consisting parts of diagnosis, prognosis, and health monitoring (DPHM) framework, have developed over the past decades to remedy the limitations of the traditional maintenance practices for complex systems. In space, where mass and power budget are restricted, application of CBM and PHM has become more vital to the success of a mission. Reaction wheels (RW) and Control Moment Gyros (CMG), as the most commonly used actuators onboard satellites, are prone to faults and failures. The ability to detect faults, isolate their location and severity, and estimate the remaining useful life (RUL) of the faulty unit can enhance mission success rate and reduce maintenance and damage costs extensively. Therefore, in this thesis, a model-based DPHM framework is developed and evaluated. Firstly, a novel fault detection algorithm is proposed, using Unscented Kalman filters (UKF) in conjunction with residual and innovation sequences, for detecting agile faults in RW/CMG onboard satellites. Secondly, a novel fault isolation algorithm is proposed, using UKF, Bayes’ probability and interacting multiple models (IMM), to isolate the location of the fault and its severity. Finally, a new fault prognosis approach is proposed, using UKF and particle filters (PF) to estimate the RUL of a faulty unit. Extensive simulations were conducted for each phase of the DPHM to verify advantages of the proposed techniques over the available methods in the literature. Extensive simulations were conducted to evaluate the performance of the proposed methods in each module of the framework. Regarding the proposed fault detection scheme, results showed superior performance of the proposed adaptation technique compared to the original UKF and a previously developed AUKF. The proposed fault isolation scheme was able to successfully isolate the faulty unit at multiple levels of isolation including formation level, system level, and actuator level with over 99% success rate for formation level, over 99% success rate for the RW assembly and for up to 90% success rate for the CMG assembly in the system level. For the CMG assembly, due to direct estimation of the fault parameters, it was possible to determine the severity of the faults as well as their location. Finally, the proposed fault prognosis approach provided RUL estimates with errors as low as 1.5% compared to the actual remaining useful life. Overall, the proposed framework can be regarded as a promising tool for fault detection, isolation and identification, and prognosis of the complex nonlinear systems. Furthermore, the proposed framework can be extended to other complex systems in space including multi-agent formation systems and other areas where the model of the system under study is available.

scholarly journals Fault Diagnosis And Prognosis Of Satellite Attitude Control System With Reaction Wheels and Control Moment Gyros

2021 ◽  
Author(s):  
Afshin Rahimi
Keyword(s):  
Fault Detection ◽  
Complex Systems ◽  
Success Rate ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
System Level ◽  
Control Moment ◽  
Fault Prognosis ◽  
Useful Life ◽  
And Control

Condition-based maintenance (CBM) and prognostics and health management (PHM), as consisting parts of diagnosis, prognosis, and health monitoring (DPHM) framework, have developed over the past decades to remedy the limitations of the traditional maintenance practices for complex systems. In space, where mass and power budget are restricted, application of CBM and PHM has become more vital to the success of a mission. Reaction wheels (RW) and Control Moment Gyros (CMG), as the most commonly used actuators onboard satellites, are prone to faults and failures. The ability to detect faults, isolate their location and severity, and estimate the remaining useful life (RUL) of the faulty unit can enhance mission success rate and reduce maintenance and damage costs extensively. Therefore, in this thesis, a model-based DPHM framework is developed and evaluated. Firstly, a novel fault detection algorithm is proposed, using Unscented Kalman filters (UKF) in conjunction with residual and innovation sequences, for detecting agile faults in RW/CMG onboard satellites. Secondly, a novel fault isolation algorithm is proposed, using UKF, Bayes’ probability and interacting multiple models (IMM), to isolate the location of the fault and its severity. Finally, a new fault prognosis approach is proposed, using UKF and particle filters (PF) to estimate the RUL of a faulty unit. Extensive simulations were conducted for each phase of the DPHM to verify advantages of the proposed techniques over the available methods in the literature. Extensive simulations were conducted to evaluate the performance of the proposed methods in each module of the framework. Regarding the proposed fault detection scheme, results showed superior performance of the proposed adaptation technique compared to the original UKF and a previously developed AUKF. The proposed fault isolation scheme was able to successfully isolate the faulty unit at multiple levels of isolation including formation level, system level, and actuator level with over 99% success rate for formation level, over 99% success rate for the RW assembly and for up to 90% success rate for the CMG assembly in the system level. For the CMG assembly, due to direct estimation of the fault parameters, it was possible to determine the severity of the faults as well as their location. Finally, the proposed fault prognosis approach provided RUL estimates with errors as low as 1.5% compared to the actual remaining useful life. Overall, the proposed framework can be regarded as a promising tool for fault detection, isolation and identification, and prognosis of the complex nonlinear systems. Furthermore, the proposed framework can be extended to other complex systems in space including multi-agent formation systems and other areas where the model of the system under study is available.

Reliability Prediction

2016 ◽  
pp. 120-158
Keyword(s):  
Block Diagram ◽  
Remaining Useful Life ◽  
System Level ◽  
Reliability Prediction ◽  
Component Level ◽  
Reliability Modeling ◽  
Reliability Models ◽  
Development Cycle ◽  
Useful Life

It is important to have an expectation about useful life of a system before its construction or even its remaining useful life during its operation. Reliability prediction is a tool for this goal. Reliability is the probability of performing adequately to achieve the desired aim of the system. In this chapter, probability calculation is used to predict failure rate of the converter. The formulation of these calculations are based on the concepts of failure factors which were described in the previous chapter. Some detailed examples are presented to show the power of probability tool for analyzing the behavior of complex systems. This chapter covers the methods for reliability calculation from component to system level. Some standards of reliability are presented. One can use the information from a reliability prediction to guide design decisions throughout the development cycle. MIL-HDBK-217 is described in details as a well-known standard for reliability prediction at component level. Reliability modeling is introduced for calculating the reliability at system level. Difference between system block diagram and reliability model is presented. The reliability models of various static and rotary power converters are expressed. Some examples are presented to demonstrate the procedure of calculations for a simple converter with its auxiliary components. This chapter gives a quantitative view to reader about evaluation of reliability and it can be used in the next chapters for reliability improvement.

Dynamic Modeling and Wear-Based Remaining Useful Life Prediction of High Power Clutch Systems

2005 ◽  
Vol 48 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Matthew Watson ◽  
Carl Byington ◽  
Douglas Edwards ◽  
Sanket Amin
Keyword(s):  
Dynamic Modeling ◽  
High Power ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life
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