Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine

Liu, Jie; Xu, Huoyao; Peng, Xingxing; Wang, Junlang; He, Chaoming

doi:10.1016/j.ress.2023.109178

Cited by 26 publications

(13 citation statements)

References 31 publications

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“…While their method achieves good results in multi-classification, it does not consider different fault levels in the presence of multiple faults. Liu et al [8] incorporated a multi-output strategy into a hybrid kernel extreme value learning machine (HKELM), enabling simultaneous output of fault level states for multiple components and facilitating Mao et al [31] Fast Mahalanobis 92 multi-output fault diagnosis. However, their feature extraction process relies on experiential time-domain feature extraction and subsequent dimensionality reduction using LDA.…”

Section: Comparison With Published Articlesmentioning

confidence: 99%

“…Jin et al [7] proposed wavelet packet transform feature extraction methods, which achieved some results in diagnosing hydraulic system leakage and hydraulic cylinder piston seal wear faults, respectively. Liu et al [8] proposed a multiclassification model for leakage fault diagnosis, employing knearest neighbors with confidence decay and linear discriminant analysis (LDA), along with a multi-channel data selection method. Furthermore, they presented a hybrid kernel limit learning machine technique for multi-output hydraulic system fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Comparison With Published Articlesmentioning

confidence: 99%

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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“…For Wang et al (2022), the digitalization and hydraulic transmission have become the key to intelligent implementation in the industry. However, it is difficult to make a reliable diagnosis for failure reasons and evaluation for the remaining lifetime of a hydraulic system (Liu et al, 2023). By collecting the output values in real-time of the operation of the hydraulic system, each parameter can be predicted and warned by a predictive model of the fault state.…”

Section: Hydraulic Systemsmentioning

confidence: 99%

Action research of lean 4.0 application to the maintenance of hydraulic systems in steel industry

Torre,

Bonamigo

2024

JQME

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PurposeMaintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.Design/methodology/approachThe maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.FindingsThe main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.Originality/valueThis research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.

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“…In recent years, with the increasing intelligence of mechanical systems, data-driven fault diagnosis harnessing substantial operational data for fault characteristic extraction has emerged as a viable strategy [12]. Among these, methods based on machine learning (ML) have received considerable attention and shown impressive results [13,14]. Nevertheless, the effectiveness of traditional ML methods can be hampered by their dependency on labor-intensive processes involving expert knowledge and manual feature extraction, potentially impacting diagnostic accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis for electro-hydrostatic actuator based on multisource information convolutional residual network

Liu,

Hu,

Zhu

et al. 2024

Meas. Sci. Technol.

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The electro-hydrostatic actuator (EHA), known for its advantages such as minimal throttling loss, high efficiency, and a significant volume-to-power ratio, has found extensive application in the fields of aeronautics and astronautics. However, ensuring the safety of aircraft that utilize EHAs requires efficient fault diagnosis due to the demanding operational conditions and prolonged usage. Traditional diagnostic approaches face challenges such as intricate fault modeling, complex multi-channel monitoring data, and a limited number of fault samples within the electro-hydraulic system. To overcome these challenges, we propose an intelligent diagnosis method based on a multi-source information convolutional residual network. Specifically, a multis-cale kernel is implemented to capture features at different scales, enhancing model expressiveness. The efficiency channel attention mechanism dynamically focuses on relevant channel features to improve feature learning ability. The residual network adaptively recalibrates features at each layer to facilitate fault feature learning. Additionally, the activate or not activation function is introduced to selectively activate shallow features, thereby improving the feature representation and generalization capability of the model. Experimental data from the EHA system validates the superiority of the proposed method, demonstrating a significant enhancement in the diagnostic accuracy of EHAs with limited samples.

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Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine

Cited by 26 publications

References 31 publications

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

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

Action research of lean 4.0 application to the maintenance of hydraulic systems in steel industry

Intelligent fault diagnosis for electro-hydrostatic actuator based on multisource information convolutional residual network

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