Prognostic and Health Management System for Fly-by-wire Electro-hydraulic Servo Actuators for Detection and Tracking of Actuator Faults

Macaluso, Andrea; Jacazio, Giovanni

doi:10.1016/j.procir.2016.09.016

Cited by 17 publications

(4 citation statements)

References 4 publications

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“…The hydraulic pump of erecting device is main noise source, there noises are harsh, will affect the normal use of hydraulic system, and reduce the life of hydraulic system leading to paralysis of the whole erecting devices. The reasons of causing abnormal noise are as follows: the oil tank respirator and oil inlet pipeline are blocked, the oil pump is empty, the hydraulic oil is unqualified, and the mechanical faults [4].…”

Section: Abnormal Noisementioning

confidence: 99%

Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

Zheng

Xue

Zhang³

2022

TJFP

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In order to improve the fault diagnosis effectiveness of hydraulic system in erecting devices, the fuzzy neural neural network is applied to carry out fault diagnosis of hydraulic system. Firstly, the main faults of hydraulic system of erecting mechanism are summarized. The main faults of hydraulic system of erecting devices concludes abnormal noise, high temperature of hydraulic oil of hydraulic system, leakage of hydraulic system, low operating speed of hydraulic system, and the characteristics of different faults are analyzed. Secondly, basic theory of fuzzy neural network is studied, and the framework of fuzzy neural network is designed. The inputting layer, fuzzy layer, fuzzy relation layer, relationship layer after fuzzy operation and outputting layer of fuzzy neural network are designed, and the corresponding mathematical models are confirmed. The analysis procedure of fuzzy neural network is established. Thirdly, simulation analysis is carried out for a hydraulic system in erecting device, the BP neural network reaches convergence after 600 times iterations, and the fuzzy neural network reaches convergence after 400 times iterations, fuzzy neural network can obtain higher accuracy than BP neural network, and running time of fuzzy neural network is less than that of BP neural network, therefore, simulation results show that the fuzzy neural network can effectively improve the fault diagnosis efficiency and precision. Therefore, the fuzzy neural network is reliable for fault diagnosis of hydraulic system in erecting devices, which has higher fault diagnosis effect, which can provide the theory basis for healthy detection of hydraulic system in erecting devices.

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Section: Abnormal Noisementioning

confidence: 99%

Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

Zheng

Xue

Zhang³

2022

TJFP

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“…All subsequent hydralic-valve degradation modes can be referenced with Figure 2. As identified by (Macaluso, 2016) and (Mornacchi, Vachtsevanos, & Jacazio, 2015), possible GCV degradation modes include the following:…”

Section: Hydraulic-valve Degradationmentioning

confidence: 99%

Method and System for Predicting Hydraulic Valve Degradation on a Gas Turbine

D’Amato

Patanian

2016

PHM_CONF

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This paper examines the development of a data-driven anomaly detection methodology for servo-actuated hydraulic valves installed in a gas turbine fuel delivery system. Degraded operation of these valves is a leading cause of unavailability for gas turbine driven power plants. Nearlyeighty potential features were generated from the limited raw sensors and control system signals through a combination of domain expertise, statistical feature extraction, and insight gains from prior physics-based simulations. Important features were down-selected by examining the behavior of the features using several years of operating data in conjunction with known field failures. Univariate statistical techniques were used to eliminate candidate features with limited capability to distinguish healthy from abnormal operation. A final machine learning model was generated using a process of recursive feature elimination. This paper will also touch on the practical implications of deploying a machine learning model in a real-time production environment.

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“…Kosova [ 13 ] presented a feature extraction method based on linear discriminant analysis (LDA) to reduce the dimension of features. Macaluso [ 14 ] proposed a PHM health assessment system based on critical factor analysis and detection (FMECA), which enabled accurate assessment of system status with fewer sensors. Duan [ 15 ] presented a fuzzy comprehensive evaluation (FCE) health assessment method based on enhanced risk coefficient optimization, which reduced subjective judgment errors caused by expert experience and improved the accuracy of health assessment.…”

Section: Introductionmentioning

confidence: 99%

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network

Wang,

Chen,

Liu

et al. 2024

Heliyon

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Prognostic and Health Management System for Fly-by-wire Electro-hydraulic Servo Actuators for Detection and Tracking of Actuator Faults

Cited by 17 publications

References 4 publications

Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

Method and System for Predicting Hydraulic Valve Degradation on a Gas Turbine

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network

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