A Coupling Prediction Algorithm for Gas Turbine Remaining Useful Life Based on Health Degree

2019 ◽  
Author(s):  
Yunpeng Cao ◽  
Pan Hu ◽  
Kehui Zeng ◽  
Shuying Li ◽  
Bo He ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Useful Life

Fuzzy Analytic Hierarchy Process Evaluation Method of Gas Turbine Based on Health Degree

2018 ◽  
Author(s):  
Yunpeng Cao ◽  
Pan Hu ◽  
Qingcai Yang ◽  
Yinghui He ◽  
Shuying Li ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Evaluation Method ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Health State ◽  
Analytic Hierarchy ◽  
Data Set ◽  
Useful Life ◽  
Hierarchy Process ◽  
Evaluation Parameters

In this paper, a gas turbine fuzzy analytic hierarchy process based on health degree is proposed to determine the current health state and remaining useful life of the gas turbine. The concept of health degree is introduced to quantitatively represent the health state of gas turbine and its components and parameters. The probability density function is used to calculate the health degree of the evaluation parameters to avoid the complexity of evaluation caused by different orders of magnitude. This paper proposes the weights hiding method that reflects the inhomogeneity of the evaluation parameters and proposes a remaining useful life prediction algorithm based on the health degree. Finally, the training data set from the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) simulator is used to validate the proposed health evaluation method and the remaining useful life prediction algorithm. The results show that the gas turbine health degree obtained by the method in this paper can be used to accurately predict the degradation trend of gas turbine, and the predicted remaining useful life coincides with the result of the test data set, thereby demonstrating the validity and practicability of the proposed method of using health degree to describe the gas turbine health state.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

scholarly journals Centralized Maintenance Time Prediction Algorithm for Freight Train Wheels Based on Remaining Useful Life Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hongmei Shi ◽  
Jinsong Yang ◽  
Jin Si
Keyword(s):  
Wiener Process ◽  
Process Model ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Monitoring Data ◽  
Prediction Algorithm ◽  
Model Parameters ◽  
Freight Train ◽  
Fixed Group ◽  
Useful Life

Many freight trains for special lines have in common the characteristics of a fixed group. Centralized Condition-Based Maintenance (CCBM) of key components, on the same freight train, can reduce maintenance costs and enhance transportation efficiency. To this end, an optimization algorithm based on the nonlinear Wiener process is proposed, for the prediction of the train wheels Remaining Useful Life (RUL) and the centralized maintenance timing. First, Hodrick–Prescott (HP) filtering algorithm is employed to process the raw monitoring data of wheel tread wear, extracting its trend components. Then, a nonlinear Wiener process model is constructed. Model parameters are calculated with a maximum likelihood estimation and the general deterioration parameters of wheel tread wear are obtained. Then, the updating algorithm for the drift coefficient is deduced using Bayesian formula. The online updating of the model is realized, based on individual wheel monitoring data, while a probability density function of individual wheel RUL is obtained. A prediction method of RUL for centralized maintenance is proposed, based on two set thresholds: “maintenance limit” and “the ratio of limit-arriving.” Meanwhile, a CCBM timing prediction algorithm is proposed, based on the expectation distribution of individual wheel RUL. Finally, the model is validated using a 500-day online monitoring data on a fixed group, consisting of 54 freight train cars. The validation result shows that the model can predict the wheels RUL of the train for CCBM. The proposed method can be used to predict the maintenance timing when there is a large number of components under the same working conditions and following the same path of degradation.

scholarly journals Determination of Remaining Useful Life of Gas Turbine Blade

2016 ◽  
Vol 38 ◽  
pp. 01011
Author(s):  
Mior Azman Meor Said ◽  
Muhammad Hafizuddin Osman ◽  
Puteri Sri Melor Megat Yusoff ◽  
Shaharin Anwar Sulaiman ◽  
Syed M Afdhal Syed Ahmad Ghazali
Keyword(s):  
Gas Turbine ◽  
Turbine Blade ◽  
Remaining Useful Life ◽  
Gas Turbine Blade ◽  
Useful Life

scholarly journals An Adaptive Model-Based Framework for Prognostics of Gas Path Faults in Aircraft Gas Turbine Engines

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Ogechukwu Alozie ◽  
Yi-Guang Li ◽  
Xin Wu ◽  
Xingchao Shong ◽  
Wencheng Ren
Keyword(s):  
Gas Turbine ◽  
Fault Isolation ◽  
Performance Model ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Sensor Data ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Degradation Models ◽  
Useful Life

This paper presents an adaptive framework for prognostics in civil aero gas turbine engines, which incorporates both performance and degradation models, to predict the remaining useful life of the engine components that fail predominantly by gradual deterioration over time. Sparse information about the engine configuration is used to adapt a performance model, which serves as a baseline for implementing optimum sensor selection, operating data correction, fault isolation, noise reduction and component health diagnostics using nonlinear Gas Path Analysis (GPA). Degradation models, which describe the progression of faults until failure, are then applied to the diagnosed component health indices from previous run-to-failure cases. These models constitute a training library from which fitness evaluation to the current test case is done. The final remaining useful life (RUL) prediction is obtained as a weighted sum of individually evaluated RULs for each training case. This approach is validated using dataset generated by the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) software, which comprises both training and testing instances of run-to-failure sensor data for a turbofan engine, some of which are obtained at different operating conditions and for multiple fault modes. The results demonstrate the capability of improved prognostics of faults in aircraft engine turbomachinery using models of system behavior, with continuous health monitoring data.

scholarly journals Towards A Model-based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Jos´e R. Celaya ◽  
Chetan S. Kulkarni ◽  
Gautam Biswas ◽  
Kai Goebel
Keyword(s):  
Kalman Filter ◽  
Critical Role ◽  
Accelerated Aging ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Component Level ◽  
Degradation Model ◽  
Operational Conditions ◽  
Electrolytic Capacitors ◽  
Useful Life

This paper presents a model-driven methodology for predicting the remaining useful life of electrolytic capacitors. This methodology adopts a Kalman filter approach in conjunction with an empirical state-based degradation model to predict the degradation of capacitor parameters through the life of the capacitor. Electrolytic capacitors are important components of systems that range from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their critical role in the system, they are good candidates for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. This paper proposes and empirical degradation model and discusses experimental results for an accelerated aging test performed on a set of identical capacitors subjected to electrical stress. The data forms the basis for developing the Kalman-filter based remaining life prediction algorithm.

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