Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

Zhou, Haowen; Huang, Jinquan; Lü, Feng

doi:10.1016/j.ymssp.2017.03.046

Cited by 35 publications

(11 citation statements)

References 49 publications

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“…In Ref. [11], a data-driven method integrates RKRLS and HMM of a new enhanced multi-sensor prognostic algorithm to prediction RUL is developed, which has higher accuracy. In Ref.…”

Section: Related Work a Data-driven Methodsmentioning

confidence: 99%

A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management

Fan

et al. 2019

IEEE Access

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Safety accidents caused by Lithium-ion (Li-ion) batteries are numerous in recent years. Therefore, more and more attention has been drawn to the Remaining Useful Life (RUL) prediction and health status monitoring for Li-ion batteries. This paper proposes a deep learning method that combines the Forgetting Online Sequential Extreme Learning Machine (FOS-ELM) with the Hybrid Grey Wolf Optimizer (HGWO) algorithm and attention mechanism for the Prognostic and Health Management (PHM) of Li-ion battery. First, we use the Variational Mode Decomposition (VMD) to denoise the raw data before the training. Then the key parameters optimization of the FOS-ELM model based on the HGWO algorithm is introduced. Finally, we apply the attention mechanism to further improve the accuracy of the algorithm. Compared with traditional neural network methods, the method proposed in this paper has higher efficiency and accuracy. INDEX TERMSRUL prediction, variational mode decomposition (VMD), extreme learning machine (ELM), prognostic and health management (PHM), grey wolf optimizer (GWO), differential evolution (DE), attention mechanism.

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“…In Ref. [11], a data-driven method integrates RKRLS and HMM of a new enhanced multi-sensor prognostic algorithm to prediction RUL is developed, which has higher accuracy. In Ref.…”

Section: Related Work a Data-driven Methodsmentioning

confidence: 99%

A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management

Fan

et al. 2019

IEEE Access

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“…In other words, these public datasets are not accompanied by corresponding state labels, which makes it difficult to assess the SOH of these engines. In this regard, scholars have proposed some methods including equal frequency method [25], clustering method [26], piece-wise method [20], average state level [27], manually segmented [28], and other methods [21]. For more research results on the health state dividing of the C-MAPSS dataset, interested readers can refer to literature [22], [29].…”

Section: Introductionmentioning

confidence: 99%

State-of-Health Assessment for Aero-Engine Based on Density-Distance Clustering and Fuzzy Bayesian Risk

Yang

Tao

et al. 2021

IEEE Access

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State-of-health (SOH) assessment for aero-engine can effectively reduce maintenance cost and operational risk, and is also a significant part of the prognostics and health management (PHM) system. However, the current SOH assessment is usually closely coordinated with other parts of PHM to achieve specific functions. This is not conducive to generalizing the function of SOH assessment. Therefore, this paper proposes a data-driven framework of SOH assessment that mainly includes data preprocessing, pseudo label generation, weight assignment and feature selection, and assessment, which enhances the systematicness of SOH assessment. A combination model based on density-distance clustering and fuzzy Bayesian risk models is designed to generate a pseudo label, select optimal parameter subset, and assign weight. Then, two assessment indicators including state membership degree and health degree are produced based on two fuzzy models for horizontal and vertical comparisons. These two indicators expand the dimensions and perspectives of SOH measurement, which can more comprehensively characterize the health state of the engine. Finally, the correctness and effectiveness of the proposed methodology are verified by the widely used Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset.INDEX TERMS State-of-health assessment, aero-engine, state membership degree, health degree, pseudo label generation

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“…The existing diagnostic health monitoring approaches are generally classified into three major categories: physical model-based approaches, [3][4][5] datadriven approaches, [6][7][8] and hybrid approaches. [9][10][11] Physical model-based approaches require the physical understanding of the object and can be successfully applied to material-level or component-level objects.…”

Section: Introductionmentioning

confidence: 99%

“…However, complete run-to-failure data are rare, and similarity searching 19 can be time-consuming, for systems with numerous variables. Multivariate approaches 7,20,21 are newly emerging techniques that automatically divide the health degradation process into several stages using unsupervised clustering algorithms and then label the current health state by searching the nearest cluster. Compared with the former two approaches, the multivariate approaches do not need to establish a 1D SHI, predefine the thresholds of different health states, or have a large number of similar historical samples.…”

Section: Introductionmentioning

confidence: 99%

A telemetry data based diagnostic health monitoring strategy for in-orbit spacecrafts with component degradation

Wang

Cheng

et al. 2019

Advances in Mechanical Engineering

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Diagnostic health monitoring without prior knowledge is still a hard problem in the prognostic and health management field. A multivariate diagnostic health monitoring strategy is proposed based on telemetry data for in-orbit spacecrafts with component degradation. Compared with the existing univariate or direct diagnostic health monitoring methods, multivariate diagnostic health monitoring methods can avoid constructing one-dimensional synthesized health index and setting empirical thresholds for different health states. In our developed strategy, a deep forest algorithm combined with an effective feature extraction approach and fuzzy C-means clustering algorithm is proposed to achieve more accurate assessment of the current health state. First, a partitioning window is utilized to deal with the raw telemetry data and then features which have high monotonicity and trends are extracted for diagnostic health monitoring. Then, a fuzzy Cmeans algorithm is used to handle unlabeled telemetry data and determine states of degrading component. Finally, a deep forest classifier is adopted to obtain the prognostic model for online probabilistic diagnostic health monitoring. Verification results on a simulated spacecraft attitude control system can demonstrate the effectiveness and feasibility of the proposed multivariate diagnostic health monitoring strategy.

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Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

Cited by 35 publications

References 49 publications

A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management

A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management

State-of-Health Assessment for Aero-Engine Based on Density-Distance Clustering and Fuzzy Bayesian Risk

A telemetry data based diagnostic health monitoring strategy for in-orbit spacecrafts with component degradation

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