An Extension Sample Classification‐Based Extreme Learning Machine Ensemble Method for Process Fault Diagnosis

Xu, Yuan; Chen, Yanjing; Zhu, Qizhen

doi:10.1002/ceat.201300622

Cited by 15 publications

(7 citation statements)

References 50 publications

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“…Artificial intelligence (AI) or machine learning (ML) is a technical expression for those smart paradigms that can be used for even the most complicated phenomena [22][23][24]. They have been already successfully employed for feature ranking and reduction [25], multivariable regression [26,27], pattern classification [10], and so on.…”

Section: Research Article 3 Neuro-based Approachmentioning

confidence: 99%

Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

Esmaeili-Faraj

Vaferi

Bolhasani³

et al. 2021

Chem Eng & Technol

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A neuro-based computing technique is used for simulation of olefin plants at industrial scale. Artificial neural networks are applied to estimate the flow rate of the main products of the olefin unit from available information in terms of flow rate of feed streams and operating condition of furnaces. The structure of the smart model is determined through a trial-and-error procedure taking the real plant information over four successive years. The proposed paradigm estimates the tonnage of the product streams by an absolute average relative deviation in the range of 0.9 % for methane to 3.14 % for propylene. Results confirmed that this smart simulation not only presents accurate predictions, but is easy to use, straightforward, and can be simply employed for optimization and control of the unit.

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Section: Research Article 3 Neuro-based Approachmentioning

confidence: 99%

Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

Esmaeili-Faraj

Vaferi

Bolhasani³

et al. 2021

Chem Eng & Technol

View full text Add to dashboard Cite

show abstract

“…Compared to individual ELM, this method can obtain better performance. Xu et al 10 have presented an extension sample classification-based extreme learning machine ensemble (ESC-ELME) method to achieve 1 higher accuracy and faster response in the complex process of fault diagnosis. Yet, essentially, ESC-ELME is an integration of multi-ELMs.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine

Wang

2018

Advances in Mechanical Engineering

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Aiming at connatural limitations of extreme learning machine in practice, a new fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine is proposed. On one hand, the presented method can extract the more efficient features using the wavelet packet-energy entropy method, and on the other hand, the sample fuzzy membership degree matrix U, weight matrix W which is used to describe the sample imbalance, and the kernel function are introduced to construct the fuzzy kernel extreme learning machine model with high accuracy and reliability. The experimental results of rolling bearing and check valve are obtained and analyzed in MATLAB 2010b. The results show that the proposed fuzzy kernel extreme learning machine method can obtain fairly or slightly better classification performance than the traditional extreme learning machine, kernel extreme learning machine, back propagation, support vector machine, and fuzzy support vector machine.

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“…Fault prediction is crucial for ensuring system reliability and preventing unwanted large deviations due to malfunctions before they happen [1]. Recently, single-fault prediction [2] has been extensively studied, but few studies have been proposed for multi-fault prediction, which may be more relevant in industrial processes.…”

Section: Introductionmentioning

confidence: 99%

Time Series Extended Finite‐State Machine‐Based Relevance Vector Machine Multi‐Fault Prediction

Zhou

Zhu

2017

Chem Eng & Technol

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Fault prediction means to detect faults that can occur in the future. While most studies focus on predicting one fault at a time, multi-fault prediction is more practical for industrial processes as multiple faults can cause much more damage than a single one. A time series extended finite-state machine (TS-EFSM)-based relevance vector machine (RVM) approach is proposed for multi-fault prediction. Time lags and correlation coefficients between the process variables and process states are determined. Then, a variable and a state dependence diagram based on the correlation coefficients is established with the EFSM. Furthermore, the RVM is applied to identify parameters for the sake of better prediction accuracy and shorter testing times. With the prediction parameters, faults can be predicted using the aforementioned TS-EFSM state transitions.

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An Extension Sample Classification‐Based Extreme Learning Machine Ensemble Method for Process Fault Diagnosis

Cited by 15 publications

References 50 publications

Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine

Time Series Extended Finite‐State Machine‐Based Relevance Vector Machine Multi‐Fault Prediction

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