Prognostics and Health Management Software for Gas Turbine Engine Bearings

2007 ◽  
Author(s):  
Michael J. Roemer ◽  
Carl S. Byington
Keyword(s):  
Gas Turbine ◽  
High Speed ◽  
Health Management ◽  
Gas Turbine Engine ◽  
Remaining Useful Life ◽  
Prognostics And Health Management ◽  
Turbine Engine ◽  
Model Based ◽  
Useful Life ◽  
Specific Implementation

Based on the results of a successful Phase I and II SBIR program performed by Impact Technologies, a suite of Prognostics and Health Management (PHM) algorithms have been developed for detecting incipient faults in the critical bearings associated with aircraft gas turbine engines. The component-level prognostic approach is presented that utilizes available sensor information from vibration transducers, along with material-level component fatigue models to calculate remaining useful life for the engine’s critical components. Specifically, correlation between the sensed data and fatigue-based damage accumulation models were developed to provide remaining useful life assessments for life limited components. The combination of health monitoring data and model-based techniques provides a unique and knowledge rich capability that can be utilized throughout the bearings’s entire life, using model-based estimates when no diagnostic indicators are present and using the monitored vibration features at later stages when incipient failure indications are detectable, thus reducing the uncertainty in model-based predictions. A description and specific implementation of this prognosis approach with application to high speed bearings is illustrated herein, using gas turbine engine and bearing test rig data as validation for the methods.

A Comprehensive Prognostics Approach for Predicting Gas Turbine Engine Bearing Life

2004 ◽  
Author(s):  
Rolf Orsagh ◽  
Michael Roemer ◽  
Jeremy Sheldon ◽  
Christopher J. Klenke
Keyword(s):  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Remaining Useful Life ◽  
Life Assessment ◽  
Engine Operation ◽  
Turbine Engine ◽  
Model Based ◽  
Bearing Life ◽  
Useful Life ◽  
Engine Bearing

Development of practical and verifiable prognostic approaches for gas turbine engine bearings will play a critical role in improving the reliability and availability of legacy and new acquisition aircraft engines. In addition, upgrading current United States Air Force (USAF) engine overhaul metrics based strictly on engine flight hours (EFH) and total accumulated cycles (TAC) with higher fidelity prognostic models will provide an opportunity to prevent failures in engines that operate under unusually harsh conditions, and will help avoid unnecessary maintenance on engines that operate under unusually mild conditions. A comprehensive engine bearing prognostic approach is presented in this paper that utilizes available sensor information on-board the aircraft such as rotor speed, vibration, lube system information and aircraft maneuvers to calculate remaining useful life for the engine bearings. Linking this sensed data with fatigue-based damage accumulation models based on a stochastic version of the Yu-Harris bearing life equations with projected engine operation conditions is implemented to provide the remaining useful life assessment. The combination of health monitoring data and model-based techniques provides a unique and knowledge rich capability that can be utilized throughout the bearing’s entire life, using model-based estimates when no diagnostic indicators are present and using the monitored features such as oil debris and vibration at later stages when failure indications are detectable, thus reducing the uncertainty in model-based predictions. A description and initial implementation of this bearing prognostic approach is illustrated herein, using bearing test stand run-to-failure data and engine test cell data.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

Application of Prognostics and Health Management (PHM) to Software System Fault and Remaining Useful Life (RUL) Prediction

2021 ◽  
Author(s):  
Mohammad Rubyet Islam ◽  
Peter Sandborn
Keyword(s):  
Health Management ◽  
Standard Test ◽  
Remaining Useful Life ◽  
Software Systems ◽  
Prognostics And Health Management ◽  
Performance Parameters ◽  
Statistical Validation ◽  
Useful Life ◽  
Test Beds

Abstract Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of an RUL (and its associated confidence) enables decisions to be made about how and when to maintain the system. PHM is generally applied to hardware systems in the electronics and non-electronics application domains. The application of PHM (and RUL) concepts has not been explored for application to software. Today, software (SW) health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental to the operation of the system. Relevant areas such as SW defect prediction, SW reliability prediction, predictive maintenance of SW, SW degradation, and SW performance prediction, exist, but all represent static models, built upon historical data — none of which can calculate an RUL. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, we wish to address how PHM can be used to make decisions for SW systems such as version update, module changes, rejuvenation, maintenance scheduling and abandonment. This paper presents a method to prognostically and continuously predict the RUL of a SW system based on usage parameters (e.g., numbers and categories of releases) and multiple performance parameters (e.g., response time). The model is validated based on actual data (on performance parameters), generated by the test beds versus predicted data, generated by a predictive model. Statistical validation (regression validation) has been carried out as well. The test beds replicate and validate faults, collected from a real application, in a controlled and standard test (staging) environment. A case study based on publicly available data on faults and enhancement requests for the open-source Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to SW systems and RUL can be calculated to make decisions on software version update or upgrade, module changes, rejuvenation, maintenance schedule and total abandonment.

New Model Based Gas Turbine Fault Diagnostics Using 1D Engine Model and Nonlinear Identification Algorithms

2009 ◽  
Author(s):  
Seyyed Hamid Reza Hosseini ◽  
Hiwa Khaledi ◽  
Mohsen Reza Soltani
Keyword(s):  
Gas Turbine ◽  
Diagnostic System ◽  
Gas Turbine Engine ◽  
Fault Model ◽  
Fault Identification ◽  
Identification System ◽  
Turbine Engine ◽  
Engine Model ◽  
Exhaust Temperature ◽  
Model Based

Gas turbine fault identification has been used worldwide in many aero and land engines. Model based techniques have improved isolation of faults in components and stages’ fault trend monitoring. In this paper a powerful nonlinear fault identification system is developed in order to predict the location and trend of faults in two major components: compressor and turbine. For this purpose Siemens V94.2 gas turbine engine is modeled one dimensionally. The compressor is simulated using stage stacking technique, while a stage by stage blade cooling model has been used in simulation of the turbine. New fault model has been used for turbine, in which a degradation distribution has been considered for turbine stages’ performance. In order to validate the identification system with a real case, a combined fault model (a combination of existing faults models) for compressor is used. Also the first stage of the turbine is degraded alone while keeping the other stages healthy. The target was to identify the faulty stages not faulty components. The imposed faults are one of the most common faults in a gas turbine engine and the problem is one of the most difficult cases. Results show that the fault diagnostic system could isolate faults between compressor and turbine. It also predicts the location of faulty stages of each component. The most interesting result is that the fault is predicted only in the first stage (faulty stage) of the turbine while other stages are identified as healthy. Also combined fault of compressor is well identified. However, the magnitude of degradation could not be well predicted but, using more detailed models as well as better data from gas turbine exhaust temperature, will enhance diagnostic results.

Development of a High-Speed Reverse Gear for Marine Gas Turbine

1965 ◽  
Author(s):  
D. M. Croker ◽  
T. P. Psichogios
Keyword(s):  
Gas Turbine ◽  
High Speed ◽  
Gas Turbine Engine ◽  
Design Features ◽  
Turbine Engine ◽  
Development History ◽  
Marine Applications ◽  
Reverse Gear ◽  
Engine Development

This paper describes the operation and salient design features of a high-speed reversing gear used with the Solar 1100-hp Saturn gas-turbine Engine. Development history leading to successful marine applications is reviewed.

scholarly journals A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

2019 ◽  
Vol 9 (3) ◽  
pp. 613
Author(s):  
Bangcheng Zhang ◽  
Yubo Shao ◽  
Zhenchen Chang ◽  
Zhongbo Sun ◽  
Yuankun Sui
Keyword(s):  
Kalman Filter ◽  
Health Management ◽  
Remaining Useful Life ◽  
Degradation Model ◽  
Time Prediction ◽  
Model Based ◽  
Stochastic Parameters ◽  
Deterioration Process ◽  
Useful Life ◽  
Posterior Estimation

Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

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