Noise-robust self-adaptive support vector machine for residual oxygen concentration measurement

He, Jianjun; Song, Cao; Luo, Qiwu; Lan, Lijuan; Yang, Chunhua; Gui, Weihua

doi:10.1109/tim.2020.2987049

Cited by 40 publications

(26 citation statements)

References 26 publications

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“…In any case, such a layout is very costly and never used at industrial level, where it is more likely to find a maximum of six probes [8][9][10]. As a matter of fact, the measurement system complexity and cost increases significantly with increases in the number of sensors, so that, in many cases, the OPR probe (i.e., the key phase sensor) is removed [11][12][13]. Finally, in order to solve the problem of sub-Nyquist BTT sampling rate, many signal reconstruction algorithms were proposed for recovering non-aliased spectra from the under-sampled signal and applied the algorithms both on simulated and experimental data [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…The robustness of BTT algorithms with respect to the error in retrieving the blade tip vibration amplitude has recently gained interest, as a quantitative analysis can be very helpful in comparing different BTT schemes [13,[19][20][21]. In particular, the main error sources in the BTT measurement chain are recognized to be:…”

Section: Introductionmentioning

confidence: 99%

“…Of particular interest is the work in [13], where BTT is implemented on a large-scale industrial centrifugal compressor. Three important considerations arise.…”

Section: Introductionmentioning

confidence: 99%

“…Starting from these considerations, the research challenge is that of proposing a novel diagnostic system for real-time online monitoring derived as an improvement to the algorithm in [13] (i.e., single probe tip timing, without root and OPR sensors), able to solve the issue of non-uniform error for the different blades, while featuring a reduced computational burden. The traditional and the novel improved BTT algorithm are described in the next section.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Key-Phase-Free Blade Tip-Timing for Nonstationary Test Conditions: An Improved Algorithm for the Vibration Monitoring of a SAFRAN Turbomachine from the Surveillance 9 International Conference Contest

Daga

Garibaldi

et al. 2021

Machines

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A turbomachine is a fundamental engineering apparatus meant to transfer energy between a rotor and a fluid. Turbomachines are the core of power generation in many engineering applications such as electric power generation plants, aerospace, marine power, automotive etc. Their relevance makes them both mission critical and safety critical in many fields. To foster reliability and safety, then, continuous monitoring of the rotor is more than desirable. One promising monitoring technique is, with no doubt, the Blade Tip-Timing, which, being simple and non-invasive, can be easily implemented on many different rotors. Blade Tip-Timing is based on the recording of the time of arrival of the blades passing in front of a probe located at a fixed angular position. The non-contact nature of the measurement prevents influences on the measured vibration, that can be recovered for all the blades simultaneously, possibly even online. In this regard, a novel algorithm is presented in this paper for obtaining a good estimate of the vibration of the blades with minimum system complexity (i.e., only one Blade Tip-Timing probe) and minimum computational effort, so to create a simple vibration monitoring system, potentially implementable online. The methodology was tested on a dataset from a SAFRAN turbomachine made available during the Surveillance 9 international conference for a diagnostic contest.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Of particular interest is the work in [13], where BTT is implemented on a large-scale industrial centrifugal compressor. Three important considerations arise.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Key-Phase-Free Blade Tip-Timing for Nonstationary Test Conditions: An Improved Algorithm for the Vibration Monitoring of a SAFRAN Turbomachine from the Surveillance 9 International Conference Contest

Daga

Garibaldi

et al. 2021

Machines

View full text Add to dashboard Cite

show abstract

“…Once the salient features are selected, they are fed into a classifier to identify the system condition. Due to its high performance classification and less requirement on sample data input, the support vector machine (SVM) proposed by Cortes and Vapnik [ 7 ] has been successfully applied to signal processing [ 8 ], regression analysis [ 9 ], pattern recognition [ 10 ], and bearing fault diagnosis [ 11 ]. However, the original SVM classifier provides high computational burden due to the method used to solve the quadratic programming problem in the SVM [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier

Van

Hoang

2020

Sensors

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Bearing is one of the key components of a rotating machine. Hence, monitoring health condition of the bearing is of paramount importace. This paper develops a novel particle swarm optimization (PSO)-least squares wavelet support vector machine (PSO-LSWSVM) classifier, which is designed based on a combination between a PSO, a least squares procedure, and a new wavelet kernel function-based support vector machine (SVM), for bearing fault diagnosis. In this work, bearing fault classification is transformed into a pattern recognition problem, which consists of three stages of data processing. Firstly, a rich information dataset is built by extracting the features from the signals, which are decomposed by the nonlocal means (NLM) and empirical mode decomposition (EMD). Secondly, a minimum-redundancy maximum-relevance (mRMR) method is employed to determine a subset of feature that can provide an optimal performance. Thirdly, a novel classifier, namely LSWSVM, is proposed with the aid of a PSO, to provide higher classification accuracy. The key innovative science of this work is to propropose a new classifier with the aid of an new wavelet kernel type to increase the classification precision of bearing fault diagnosis. The merit features of the proposed approach are demonstrated based on a benchmark bearing dataset and a comprehensive comparison procedure.

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Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system

Eswaramoorthi¹,

Moses²,

Hameed

et al. 2022

Trans Emerging Tel Tech

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The core requirements are generated for sixth generation (6G) wireless communication with low‐latency and ultra‐high speeds to increase the count of ultra scale intelligent factors, like smart cars, mobile root users. The advancement of 6G communication can lead the interference exploitation. To manage the exploitation of uplink multiuser massive (UMM), the multiple‐input and multiple‐output (MIMO) is very difficult to detect the mechanisms, particularly, quadrature amplitude modulation (QAM) signals. To overcome these issues, a novel deep graph neural network optimized with fertile field algorithm based detection model (DGNNO‐FFA) is proposed in this article for uplink multiuser massive MIMO System. The proposed DGNNO‐FFA approach minimizes the channel estimation errors under low signal to noise ratio (SNR) with better bit error rate (BER). Finally, the proposed DGNNO‐FFA approach attains 11.02%, 12.22%, and 25.27% lower BER value, 14.55%, 18.66%, and 29.49% higher energy efficiency, 15.59%, 19.06%, and 29.59% lower NMSE, and 15.59%, 19.06%, and 29.59% lower computational complexity compared with other existing approaches, like deep neural network based semi definite relaxation (DNN‐SDR), QR based zero forcing algorithms (QR‐ZF), and QAM based 2‐dimensional double successive projection model (QAM‐2D‐DSP).

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Noise-robust self-adaptive support vector machine for residual oxygen concentration measurement

Cited by 40 publications

References 26 publications

Key-Phase-Free Blade Tip-Timing for Nonstationary Test Conditions: An Improved Algorithm for the Vibration Monitoring of a SAFRAN Turbomachine from the Surveillance 9 International Conference Contest

Key-Phase-Free Blade Tip-Timing for Nonstationary Test Conditions: An Improved Algorithm for the Vibration Monitoring of a SAFRAN Turbomachine from the Surveillance 9 International Conference Contest

Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier

Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system

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