A Benchmarking Analysis of a Data-Driven Gas Turbine Diagnostic Approach

Loboda, Igor; Pérez-Ruiz, Juan; Yepifanov, Sergiy

doi:10.1115/gt2018-76887

Cited by 10 publications

(29 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the difference in comparison with SVM remains small (0.86% for RELM and 1.13% for RELM-SRC). In this case, the increase of the number of training samples (from 760 to 1900 engines) does not produce a significant improvement in P since there is a general increase of 0.69% without considering PNN (the method was excluded for further analysis in [23]). In the third configuration, the hybrid approach wins by 1.31% compared to SVM.…”

Section: Comparison Of Diagnostic Frameworkmentioning

confidence: 99%

“…The models are assessed quantitatively with coefficient determination and then applied to the ProDiMES turbofan engine fleet to perform anomaly detection. Using different diagnosis analysis and validation data, Loboda et al [23] benchmarked a data-driven gas turbine diagnostic methodology based on three well-known methods: (1) multi-layer perceptron, (MLP) whose learning process consists of looking for such weight and bias coefficients that minimize the error through the backpropagation algorithm. The output is a measure of the closeness of an input sample and a class.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aircraft Engine Gas-Path Monitoring and Diagnostics Framework Based on a Hybrid Fault Recognition Approach

2021

Self Cite

View full text Add to dashboard Cite

Considering the importance of continually improving the algorithms in aircraft engine diagnostic systems, the present paper proposes and benchmarks a gas-path monitoring and diagnostics framework through the Propulsion Diagnostic Methodology Evaluation Strategy (ProDiMES) software developed by NASA. The algorithm uses fleet-average and individual engine baseline models to compute feature vectors that form a fault classification with healthy and faulty engine classes. Using this classification, a hybrid fault-recognition technique based on regularized extreme learning machines and sparse representation classification was trained and validated to perform both fault detection and fault identification as a common process. The performance of the system was analyzed along with the results of other diagnostic frameworks through four stages of comparison based on different conditions, such as operating regimes, testing data, and metrics (detection, classification, and detection latency). The first three stages were devoted to the independent algorithm development and self-evaluation, while the final stage was related to a blind test case evaluated by NASA. The comparative analysis at all stages shows that the proposed algorithm outperforms all other diagnostic solutions published so far. Considering the advantages and the results obtained, the framework is a promising tool for aircraft engine monitoring and diagnostic systems.

show abstract

Section: Comparison Of Diagnostic Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aircraft Engine Gas-Path Monitoring and Diagnostics Framework Based on a Hybrid Fault Recognition Approach

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…As reported by Litt, Parker, and Chatterjee (2003), the degradation trajectories tend to become more linear as the engine ages. Loboda et al, (2018) employed polynomial functions to plot measurement deviation trends for fleet engines. Differently, we applied an AANN to predict gradual deterioration in terms of isentropic efficiency and flow capacity indices.…”

Section: Short-term Fault Diagnostics Using Aannmentioning

confidence: 99%

“…Instead, as shown in Figure 7, a MLP was applied to estimate IPC and HPC bleed valve leakage faults for the target turbofan engine. MLP is one of the most commonly applied ANN methods in gas turbine diagnostics (Loboda et al, 2018). The engine gas path measurements and two parameters related to magnitude of the two bleed valve leakages were input and output of the structure, respectively.…”

Section: Bleed Valve Leakage Identification Using Mlpmentioning

confidence: 99%

“…The performance of six different diagnostic methods to deal with compressor, sensor, and actuator faults in a fleet of deteriorated twoshaft turbofan engines was evaluated by Koskoletos, Aretakis, Alexiou, Romesis, and Mathioudakis (2018). Loboda, Pérez-Ruiz, and Yepifanov (2018) also compared three popular data-driven techniques, namely multilayer perceptron (MLP), PNN, and SVM for fleet monitoring.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics

Fentaye

Zaccaria

Kyprianidis

2021

IJPHM

View full text Add to dashboard Cite

Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine life-cycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry.

show abstract

Convolutional Neural Network Denoising Auto-Encoders for Intelligent Aircraft Engine Gas Path Health Signal Noise Filtering

Zhao

Sampath

2023

Journal of Engineering for Gas Turbines and Power

View full text Add to dashboard Cite

Removing noise from health signals is critical in gas path diagnostics of aircraft engines. An efficient noise filtering/denoising method should remove noise without using future data points, preserve important changes, and promote accurate diagnostics without time delay. Machine Learning (ML)-based methods are promising for high fidelity, accuracy, and computational efficiency under the motivation of Intelligent Engines. However, previous ML-based denoising methods are rarely applied in actual engineering practice because they cannot accommodate time series and cannot effectively capture important changes or are limited by the time delay problem. This paper proposes a Convolutional Neural Network Denoising Autoencoder (CNN-DAE) method to build a denoising autoencoder structure. In this structure, a convolutional operation is used to accommodate time series, and causal convolution is introduced to solve the problem of using future data points. The proposed denoising method is evaluated against NASA's Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) software. It has been proved that the proposed method can accommodate time series, remove noise for improved denoising accuracy and preserve the important changes for enhanced diagnostic information. NASA's blind test case results show that Kappa Coefficient of a common diagnostic method using the processed data is 0.731 and is at least 0.046 higher than the other diagnostic methods in the open literature. Processing health signals using the proposed method would significantly promote accurate diagnostics without time delay. The proposed method could support intelligent condition monitoring systems by exploiting historical information for improved denoising and diagnostic performance.

show abstract

A Benchmarking Analysis of a Data-Driven Gas Turbine Diagnostic Approach

Cited by 10 publications

References 0 publications

Aircraft Engine Gas-Path Monitoring and Diagnostics Framework Based on a Hybrid Fault Recognition Approach

Aircraft Engine Gas-Path Monitoring and Diagnostics Framework Based on a Hybrid Fault Recognition Approach

Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics

Convolutional Neural Network Denoising Auto-Encoders for Intelligent Aircraft Engine Gas Path Health Signal Noise Filtering

Contact Info

Product

Resources

About