Dual reduced kernel extreme learning machine for aero-engine fault diagnosis

Lü, Feng; Jiang, Jipeng; Huang, Jinquan; Qiu, Xiaojie

doi:10.1016/j.ast.2017.10.024

Cited by 29 publications

(7 citation statements)

References 24 publications

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“…Combined with machine learning and pattern-recognition techniques, the GPA approach can be an efficient tool to diagnose complex and hidden engine faults. Many machine-learning techniques have been employed for gas turbine diagnostics, for example, support vector machines (SVM) [8], genetic algorithms [9], fuzzy logic [10] and neuro-fuzzy inference systems [11], multi-layer perceptron (MLP) [12], probabilistic neural network (PNN) [13], and extreme learning machines (ELM) [14,15].…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

2021

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“…3) Besides, in a faulty system with TLF, some special external disturbances can be brought into the SLV control system due to the imbalance between different thrusts and the skewing of the SLV's centroid, which will exert tremendous disturbances on the attitude tracking control system. 4) The last relevant point worth mentioning is that, as the actuating component of the control system, the rocket engine is more of a crucial component of the power subsystem of SLV, therefore there are many independent and elaborate researches on the fault detection and diagnosis (FDD) technology of the aeroengine [22], [23], which pay more attention to the data-driven methods (statistical analysis or neural networks, etc.) rather than the model-based methods (state or parameter estimation, etc.)…”

Section: Introductionmentioning

confidence: 99%

Attitude Tracking Control Reconfiguration for Space Launch Vehicle With Thrust Loss Fault

Zhang

Cheng

et al. 2019

IEEE Access

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The thrust loss fault of the space launch vehicle is quite different from that of ordinary actuator efficiency loss. Focusing on the attitude tracking control problem of the launch vehicle with thrust loss fault, the special impacts of that fault on the control system are explored, and a two-stage control reconfiguration strategy based on the cascaded pseudo-inverse allocation method and the neuron adaptive gain scheduling method is proposed for faults with different severity. Meanwhile, a practical reconfigurability analysis for the faulty system based on the control state reachability is presented for some representative fault scenarios. Based on the nonlinear dynamical model of the faulty system, lots of numerical simulations under various fault scenarios are finally carried out. The results indicate that the designed control reconfiguration strategy not only can effectively deal with the thrust loss fault with high practicability but also is easy to implement. INDEX TERMS Space launch vehicle, thrust loss fault, control reconfiguration strategy, cascaded pseudo-inverse allocation, neuron adaptive gain scheduling, reconfigurability analysis.

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“…The health parameters are unmeasurable and represent engine gas-path health, containing indicators of fan efficiency SE1, fan flow SW1, compressor efficiency SE2, compressor flow SW2, HPT efficiency SE3, HPT flow SW3, LPT efficiency SE4, and LPT flow SW4. The available measurements are used to calculate health parameters, and they are low-pressure spool speed NL, high-pressure spool speed The data are generated from the numerical engine model [33,34] to evaluate the involved methods in the steady behavior of the maximum power operation and transient behavior including acceleration and deceleration. The involved engine parameters are reported in Table 3.…”

Section: Irkpca-hmm Based Engine Gas-path Fault Diagnosismentioning

confidence: 99%

“…The engine station numbers in Figure 3 are as follows: inlet exit marked by 2, compressor inlet by 22, compressor exit by 3, HPT entrance by 43, LPT entrance by 5, and LPT exit by 6. The data are generated from the numerical engine model [33,34] to evaluate the involved methods in the steady behavior of the maximum power operation and transient behavior including acceleration and deceleration. The involved engine parameters are reported in Table 3.…”

Section: Irkpca-hmm Based Engine Gas-path Fault Diagnosismentioning

confidence: 99%

An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis

et al. 2018

Self Cite

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To improve gas-path performance fault pattern recognition for aircraft engines, a new data-driven diagnostic method based on hidden Markov model (HMM) is proposed. A redundant sensor somewhat interferes with fault diagnostic results of the HMM, and it also increases the computational burden. The contribution of this paper is to develop an iterative reduced kernel principal component analysis (IRKPCA) algorithm to extract fault features from original high-dimension observation without large additional calculation load and combine it with the HMM for engine gas-path fault diagnosis. The optimal kernel features are obtained by iterative sequential forward selection of the IRKPCA, and the features with lower dimensions are contracted through a trade-off between the fault information and modeling data scale in reduced kernel space. The similarity degree is designed to simplify the HMM modeling data using fault kernel features. Test results show that the proposed methodology brings a significant improvement in diagnostic confidence and computational efforts in the applications of a turbofan engine fault diagnosis during its steady and dynamic process.

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Dual reduced kernel extreme learning machine for aero-engine fault diagnosis

Cited by 29 publications

References 24 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

Attitude Tracking Control Reconfiguration for Space Launch Vehicle With Thrust Loss Fault

An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis

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