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Modeling Propagation of Gas Path Damage
This paper describes how damage propagation can be tracked and modeled for a range of fault modes in some modules of commercial high bypass aircraft engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine (cycle deck) as a function of variations of flow and efficiency of the modules of interest. These surfaces are normalized and superimposed. Next, sensor readings are matched to those surfaces and – using an optimization approach – the corresponding flow and efficiency pair is found that best explains the sensor data. This flow and efficiency pair is then compared to previous pairs and the direction of the change as well as the rate of change is determined. The whole trajectory is then projected into the time domain. An extrapolation of the curve to the limit (which is established via operational margins) yields the remaining life. In a backward mode, the extrapolated curve is discretized and estimated future flow and efficiency pairs are retrieved. These pairs are then input to the cycle deck to produce future anticipated sensor readings as well as confirmatory trips of operational margins. Changes of the future sensor readings with real readings are used to adjust the remaining life calculations. The method is demonstrated on time series of historical engine faults.
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Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation
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This paper describes how damage propagation can be modeled within the modules of aircraft gas turbine engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine as a function of variations of flow and efficiency of the modules of interest. An exponential rate of change for flow and efficiency loss was imposed for each data set, starting at a randomly chosen initial deterioration set point. The rate of change of the flow and efficiency denotes an otherwise unspecified fault with increasingly worsening effect. The rates of change of the faults were constrained to an upper threshold but were otherwise chosen randomly. Damage propagation was allowed to continue until a failure criterion was reached. A health index was defined as the minimum of several superimposed operational margins at any given time instant and the failure criterion is reached when health index reaches zero. Output of the model was the time series (cycles) of sensed measurements typically available from aircraft gas turbine engines. The data generated were used as challenge data for the Prognostics and Health Management (PHM) data competition at PHM’08.
Transient Region Coverage in the Propulsion IVHM Technology Experiment
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Over the last several years researchers at NASA Glenn and Ames Research Centers have developed a real-time fault detection and isolation system for propulsion subsystems of future space vehicles. The Propulsion IVHM Technology Experiment (PITEX), as it is called follows the model-based diagnostic methodology and employs Livingstone, developed at NASA Ames, as its reasoning engine. The system has been tested on flight-like hardware through a series of nominal and fault scenarios. These scenarios have been developed using a highly detailed simulation of the X-34 flight demonstrator main propulsion system and include realistic failures involving valves, regulators, microswitches, and sensors. This paper focuses on one of the recent research and development efforts under PITEX – to provide more complete transient region coverage. It describes the development of the transient monitors, the corresponding modeling methodology, and the interface software responsible for coordinating the flow of information between the quantitative monitors and the qualitative, discrete representation in Livingstone.
Multiple Damage Progression Paths in Model-based Prognostics
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Model-based prognostics approaches employ do- main knowledge about a system, its components, and how they fail through the use of physics-based models. Compo- nent wear is driven by several different degradation phenom- ena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics method- ology using particle filters, in which the problem of charac- terizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is repre- sented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model- based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active.
Distributed Damage Estimation for Prognostics based on Structural Model Decomposition
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Model-based prognostics approaches capture system knowl- edge in the form of physics-based models of components that include how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decom- position approach adapted from the diagnosis community, called possible conflicts, in order to both improve the com- putational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state esti- mate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation- based experiments to demonstrate the approach.
Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications
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Sensor faults continue to be a major hurdle for sys- tems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally dif- ficult to seed particular sensor faults. Therefore, research is un- derway to better understand the different fault modes seen in sen- sors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper il- lustrates the work with data collected from an electromechanical actuator in an aerospace setting, equipped with temperature, vi- bration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network-based classi- fier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and dis- ambiguation efficacy with respect to severity of fault conditions.
Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines
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Software-in-the-loop and hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The offline testing framework is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle.
Prognostics for Ground Support Systems: Case Study on Pneumatic Valves
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Prognostics technologies determine the health (or damage) state of a component or sub- system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such infor- mation enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics method- ology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the ow of propellant, so failures may have a signi cant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle ltering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.
Application of Model Based Prognostics to Pneumatic Valves in a Cryogenic Propellant Loading Testbed
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Pneumatic-actuated valves are critical components in many applications, including cryogenic propellant loading for space operations. For these components, failures need to be predicted so that components can be repaired to ensure mission success, i.e., health monitoring and fault prognostics is required. In order to develop, test, mature, and deploy valve prognostics algorithms, we have developed a testbed for pneumatic valves used in cryogenic service for propellant loading operations, in which we can inject controlled damage pro files and observe its effects on valve operation. In this paper, we focus on the prognostics of a continuously-controlled pneumatic valve. We describe the construction of the testbed, the fault injection mechanisms, and the model-based valve prognostics algorithms. Experimental results from the testbed demonstrate successful prediction of valve failure.
Propulsion IVHM Technology Experiment Overview
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NASA researchers recently demonstrated successful real-time fault detection and isolation of a virtual reusable launch vehicle main propulsion system. Using a detailed simulation of a vehicle propulsion system to produce synthesized sensor readings, the NASA team demonstrated that advanced diagnostic algorithms, running on flight-like computers, can, in real time, successfully diagnose the presence and cause of faults. This demonstration was conducted as part of the NASA Propulsion IVHM Technology Experiment, or PITEX.
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