Detection of Faults in Electro-Hydrostatic Actuators Using Feature Extraction Methods and an Artificial Neural Network

Ghanbari, Maryam; Kinsner, W.; Sepehri, Nariman

doi:10.1109/aiiot54504.2022.9817236

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…The coupling effects between the different components make real-time hydraulic system fault diagnosis a challenging task. To address this issue, data-driven fault diagnostic methods have attracted considerable attention from both industry and academia [ 4 , 5 , 6 , 7 , 8 , 9 ]. Models that are able to abstract valuable information from historical data are the key for these data-driven fault diagnostic methods [ 10 ], which have been widely investigated in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning (ML) models are used widely in data-driven fault diagnosis tasks. Many machine learning models, including support vector machines (SVMs) [ 7 , 11 ], neural networks (NNs) [ 4 , 5 ], random forest [ 12 ], and the extreme learning machine (ELM) [ 13 ], have been developed and applied to fault detection and classification. In these methods, the diagnostic accuracy is strongly dependent on the features that are fed to the classifier.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network

Tao,

Jia,

Wang

et al. 2024

Sensors

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This paper proposed a real-time fault diagnostic method for hydraulic systems using data collected from multiple sensors. The method is based on a proposed multi-sensor convolutional neural network (MS-CNN) that incorporates feature extraction, sensor selection, and fault diagnosis into an end-to-end model. Both the sensor selection process and fault diagnosis process are based on abstract fault-related features learned by a CNN deep learning model. Therefore, compared with the traditional sensor-and-feature selection method, the proposed MS-CNN can find the sensor channels containing higher-level fault-related features, which provides two advantages for diagnosis. First, the sensor selection can reduce the redundant information and improve the diagnostic performance of the model. Secondly, the reduced number of sensors simplifies the model, reducing communication burden and computational complexity. These two advantages make the MS-CNN suitable for real-time hydraulic system fault diagnosis, in which the multi-sensor feature extraction and the computation speed are both significant. The proposed MS-CNN approach is evaluated experimentally on an electric-hydraulic subsea control system test rig and an open-source dataset. The proposed method shows obvious superiority in terms of both diagnosis accuracy and computational speed when compared with traditional CNN models and other state-of-the-art multi-sensor diagnostic methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network

Tao,

Jia,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Literature [21] develops the Matlab Simulink model for the identification of bearing fault in induction motor-based pumping systems. Literatures [22][23][24][25][26][27] use the improved machine learning algorithm to identify and diagnose the states of pump loads. Literature [22] proposes an online fault diagnosis method based on a deep transfer convolutional neural network (TCNN) framework.…”

Section: Introductionmentioning

confidence: 99%

“…In literature [26], a physics-informed domain adaptation network, termed Adaptive Fault attention Residual Network (AFARN), is proposed. Literature [27] presents a new architecture of an artificial neural network (ANN) for detecting the existence of an internal leakage fault as labelled data.…”

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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Fault diagnosis and prognosis capabilities for wind turbine hydraulic pitch systems

Dallabona,

Blanke,

Pedersen

et al. 2025

Mechanical Systems and Signal Processing

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Detection of Faults in Electro-Hydrostatic Actuators Using Feature Extraction Methods and an Artificial Neural Network

Cited by 5 publications

References 10 publications

Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network

Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network

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

Fault diagnosis and prognosis capabilities for wind turbine hydraulic pitch systems

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