Experimental investigation on electro-hydraulic actuator fault diagnosis with multi-channel residuals

Miao, Jianguo; Wang, Yunlong; Wang, Dong; Miao, Qiang

doi:10.1016/j.measurement.2021.109544

Cited by 21 publications

(4 citation statements)

References 34 publications

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“…However, UAV motors present unique challenges due to their compact size, faster rotational speed, and frequency transitions compared to industrial counterparts. Moreover, conventional UAV fault diagnosis models often rely on ample training data [43,44]. Yet, UAV fault diagnosis poses a typical small sample problem.…”

Section: Data Sourcesmentioning

confidence: 99%

Fault diagnosis of drone motors driven by current signal data with few samples

Chen,

Li,

et al. 2024

Meas. Sci. Technol.

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Multirotor UAVs find extensive application across diverse domains, with their motors serving as pivotal components in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the critical need for research in UAV motor fault diag-nosis. This paper focuses on quadrotor UAVs employing DC brushless motors as the research subject. The study designs fault experiments for three key components—stator, rotor, and bear-ing—based on the structural and operational characteristics of these motors. Addressing chal-lenges such as limited installation space for UAV payloads and redundant components, difficul-ties in sensor installation, high costs, and data acquisition complexities, this research adopts the current signal as the diagnostic signal. It explores a UAV motor fault diagnostic method based on the current signal. Furthermore, the study investigates a motor fault diagnosis method based on the current signal. To overcome challenges arising from the UAV's heightened sensitivity to the health of its components and the limited availability of fault data training samples, traditional machine learning and deep learning methods face difficulties in identifying representative fea-tures under small samples, often succumbing to overfitting risks and resulting in low diagnostic accuracy. To address these issues, a hybrid neural network fault diagnosis model is proposed, integrating the width learning system and convolutional neural network. The width learning system maps the original current signal into a feature space, yielding more robust and repre-sentative sample features. Subsequently, the convolutional neural network undertakes feature extraction and classification tasks. In experiments focused on current signal data-driven small-sample fault diagnosis in UAV motors, the proposed model outperforms other models used for comparison.

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Section: Data Sourcesmentioning

confidence: 99%

Fault diagnosis of drone motors driven by current signal data with few samples

Chen,

Li,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…Huang et al [11] employed a CNN to extract features from samples of multiple-rate signals for diagnosing hydraulic system faults, effectively extracting potential features from these multi-rate signals. Miao et al [12] developed multiple observers to generate multi-channel residuals based on backpropagation neural networks and utilized a one-dimensional CNN to achieve accurate fault diagnosis using these residuals. Tongguang et al [13] proposed a solution to intelligent fault diagnostics for early identification of aero-engine hydraulic tube caliper faults, leveraging optimal variational modal decomposition and a CNN.…”

Section: Introductionmentioning

confidence: 99%

A multi-output fault diagnosis framework for hydraulic system using a CNN-SVM hierarchical learning strategy

Liang,

Yuan,

Kang

et al. 2024

Meas. Sci. Technol.

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Achieving asymptotic and concurrent fault diagnosis in hydraulic system remains a challenging endeavor due to the inherent attributes of the hidden occurrence, simultaneous manifestation, coupling, and limited sample size. To address the above issues, this paper proposes a hierarchical multi-output fault detection and diagnosis framework, namely, HMDF, based on a hierarchical learning strategy to leverage an improved convolutional neural network (CNN) and support vector machine (SVM). Both a multi-channel CNN and a multi-branch CNN are employed to extract and downscale features collected by the sensors at diverse sampling frequencies first, and then, such features are subsequently subjected to classification using SVM. The hierarchical learning strategy enables the identification of different fault states, both at the component and the intra-component level. Additionally, a modified whale optimization algorithm (WOA) is also utilized to optimize the classification process of SVM. Extensive experiments are conducted to test the proposed HMDF with the hydraulic system datasets. Results show that HMDF achieves a diagnostic accuracy of up to 98.9% for the dataset, surpassing traditional methods reliant on manual extraction of time-frequency features, and it also exhibits superior classification performances with a small sample size. The HMDF is expected to offer a generalized framework for the multi-output fault detection and diagnosis in hydraulic systems and other complex components. 

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“…In the electrohydraulic servo control system, the signal is transmitted using electronic and electric components, and the load is driven using a hydraulic transmission. To make the complete system more flexible, it can employ an electrical system for its efficiency and applicability, and a hydraulic system for its rapid response speed, large load stiffness, and accurate positioning features [4,5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Control Algorithm Sliding Mode-PID for an Electrohydraulic Servo Actuator System Based on Particle Swarm Optimization Technique

Fadel¹

2023

JESA

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The Electro-Hydraulic Servo Actuators (EHSA) use the technology of the integration between hydraulic and electrical systems. These Actuators are widely used in many modern control systems including aircraft and missile flight control systems which can produce a rapid response, high power-to-weight ratio, and large stiffness. However, the nonlinearity of the EHSA systems has an impact on their accuracy and motion control. In this paper, a hybrid control algorithm sliding mode-PID (SMCPID) controller is designed based on the Particle Swarm Optimization (PSO) technique to maintain or enhance the performance of the utilized controller and reduce the chattering phenomena. First, a detailed non-linear mathematical model of the EHSA is developed and a computer simulation program is built using MATLAB/SIMULINK package. Then, three types of control strategies are designed and studied: PID, SMC, and hybrid SMCPID controllers. The optimization of the parameters of the PID controller and the variables of the SMC and hybrid SMCPID controllers is presented by using the PSO technique. The SMC control strategy is developed from the derived dynamic equation, whose stability is demonstrated by the Lyapunov theorem. Finally, in order to ensure the effectiveness of the optimized controllers, a comparative simulation study between the three types of controllers is presented where the root mean square error (RMS) values, and overshoot percentage (O.S %) are calculated and act as the performance indexes for comparison purposes. The simulation results show that the proposed hybrid SMCPID controller tuned by the PSO technique outperforms the PID and SMC controllers in terms of overall performance.

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Experimental investigation on electro-hydraulic actuator fault diagnosis with multi-channel residuals

Cited by 21 publications

References 34 publications

Fault diagnosis of drone motors driven by current signal data with few samples

Fault diagnosis of drone motors driven by current signal data with few samples

A multi-output fault diagnosis framework for hydraulic system using a CNN-SVM hierarchical learning strategy

Hybrid Control Algorithm Sliding Mode-PID for an Electrohydraulic Servo Actuator System Based on Particle Swarm Optimization Technique

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