Fault Diagnosis and Isolation of an Electro-Pump using Neural Data Fusion

Jorkesh, Saeid; Poshtan, Javad; Poshtan, Majid

doi:10.1109/ecce.2019.8912970

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Such variables are considered independent and non-Gaussian, and are understood to be independent components of the observed data. Here, being autonomous means that no knowledge about each other is exposed by independent components [19][20][21].…”

Section: Independent Component Analysismentioning

confidence: 99%

Fault diagnosis of an induction motor using data fusion based on neural networks

Jorkesh

Poshtan

2021

IET Science Measure & Tech

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In this paper, neural network-based data fusion is used to detect fault and isolate stator winding short circuit, outer bearing race, and broken rotor bar defects in an induction motor. In addition, the robustness of the proposed method against the disturbance introduced by the coupled pump's unbalanced power source and dry running is investigated. First, three-phase current and voltage signals are separated by means of independent component analysis (ICA), then extracted features are combined by adopting neural networks, and finally, the system's health condition is evaluated. Experimental results indicate that data fusion based on neural networks can evaluate with high reliability the system's health condition and provide better robustness in the presence of disturbances.

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Section: Independent Component Analysismentioning

confidence: 99%

Fault diagnosis of an induction motor using data fusion based on neural networks

Jorkesh

Poshtan

2021

IET Science Measure & Tech

Self Cite

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“…In literature [7], the end-ring wear detection through a multicomponent approach is researched through simulation, laboratory results, and the diagnosis of two-field motors showing that new fault alarm levels need to be defined. Literatures [8][9][10][11][12] present pump health state recognition methods based on data fusion, machine learning and probability. Literature [8] proposes a decision fusion method based on Bayesian probability formula, and obtains the effective evaluation result of pump state.…”

Section: Introductionmentioning

confidence: 99%

“…Literature [10] introduces a multi-sensor prognostics approach which merges highly predictable statistical features from vibrational and pressure sensor measurements. In literature [11], results indicate that neural data fusion method is a reliable non-intrusive diagnostic motor testing under normal loading. A multi-sensor data fusion method based on adaptive weighting strategy using analytic hierarchy process algorithm and cross-correlation function fusion algorithm is proposed in literature [12].…”

Section: Introductionmentioning

confidence: 99%

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Liu

Zhu

et al. 2023

Electronics

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Main circulation pump is the only high-speed rotating equipment in primary loop of nuclear power plant. Its function is to ensure the normal operation of primary loop system by controlling the circulating flow of reactor coolant. In order to ensure long-term healthy operation of nuclear power main circulating pump, a method for identifying the health states of nuclear power main circulating pump based on ensemble empirical mode decomposition (EEMD) and support vector machine optimized by optimized quantum genetic algorithm (OQGA-SVM) is proposed. Vibration signal of main circulating pump is decomposed by EEMD. Vibration signal characteristics of nuclear power main circulating pump in healthy state and different fault states are analyzed and target characteristic indexes are put forward. Then, health state identification model of main circulation pump of OQGA-SVM is established, and target characteristic indexes are used as input parameter of the model. Finally, combined with experimental data, the model analysis and validation show that the health state identification method of nuclear power main circulating pump based on EEMD-OQGA-SVM can accurately and effectively identify the states of main circulation pump, has a higher identification accuracy than EEMD-SVM method and is more efficient and accurate than EEMD-QGA-SVM method.

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Intelligent Solenoid Pump Fault Detection based on MFCC Features, LLE and SVM

Akpudo

Hur

2020

2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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Fault Diagnosis and Isolation of an Electro-Pump using Neural Data Fusion

Cited by 4 publications

References 10 publications

Fault diagnosis of an induction motor using data fusion based on neural networks

Fault diagnosis of an induction motor using data fusion based on neural networks

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Intelligent Solenoid Pump Fault Detection based on MFCC Features, LLE and SVM

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