Internal pump leakage detection of the hydraulic systems with highly incomplete flow data

Chen, Xirui; Liu, Hui; Nikitas, Nikolaos

doi:10.1016/j.aei.2023.101974

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…The performance of a hydraulic pump directly affects the operational efficiency and accuracy of the powered mechanical equipment. Therefore, continuous monitoring of the operating status of the hydraulic pump is of significant practical importance and can help operators detect and solve hydraulic pump failures in a timely manner, ensuring the safe operation of the equipment [4][5][6]. However, in many real-world engineering applications, real-time monitoring through direct sensing is often impractical due to factors such as harsh working environments, the prohibitive cost of monitoring equipment, and difficulty in sensor installation.…”

Section: Introductionmentioning

confidence: 99%

A Soft Sensor Model for Predicting the Flow of a Hydraulic Pump Based on Graph Convolutional Network–Long Short-Term Memory

Ji,

Li,

Wang

et al. 2024

Actuators

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The hydraulic pump plays a pivotal role in engineering machinery, and it is essential to continuously monitor its operating status. However, many vital signals for monitoring cannot be directly obtained in practical applications. To address this, we propose a soft sensor approach for predicting the flow signal of the hydraulic pump based on a graph convolutional network (GCN) and long short-term memory (LSTM). Our innovative GCN-LSTM model is intricately designed to capture both spatial and temporal interdependencies inherent in complex machinery, such as hydraulic pumps. We used the GCN to extract spatial features and LSTM to extract temporal features of the process variables. To evaluate the performance of GCN-LSTM in predicting the flow of a hydraulic pump, we construct a real-world experimental dataset with an actual hydraulic shovel. We further evaluated GCN-LSTM on two public datasets, showing the effectiveness of GCN-LSTM for predicting the flow of hydraulic pumps and other complex engineering operations.

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Section: Introductionmentioning

confidence: 99%

A Soft Sensor Model for Predicting the Flow of a Hydraulic Pump Based on Graph Convolutional Network–Long Short-Term Memory

Ji,

Li,

Wang

et al. 2024

Actuators

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Small data challenges for intelligent prognostics and health management: a review

Li,

Feng

et al. 2024

Artif Intell Rev

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Prognostics and health management (PHM) is critical for enhancing equipment reliability and reducing maintenance costs, and research on intelligent PHM has made significant progress driven by big data and deep learning techniques in recent years. However, complex working conditions and high-cost data collection inherent in real-world scenarios pose small-data challenges for the application of these methods. Given the urgent need for data-efficient PHM techniques in academia and industry, this paper aims to explore the fundamental concepts, ongoing research, and future trajectories of small data challenges in the PHM domain. This survey first elucidates the definition, causes, and impacts of small data on PHM tasks, and then analyzes the current mainstream approaches to solving small data problems, including data augmentation, transfer learning, and few-shot learning techniques, each of which has its advantages and disadvantages. In addition, this survey summarizes benchmark datasets and experimental paradigms to facilitate fair evaluations of diverse methodologies under small data conditions. Finally, some promising directions are pointed out to inspire future research.

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A data-driven simulation and Gaussian process regression model for hydraulic press condition diagnosis

Jankovič,

Šimic,

Herakovič

2024

Advanced Engineering Informatics

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Internal pump leakage detection of the hydraulic systems with highly incomplete flow data

Cited by 5 publications

References 32 publications

A Soft Sensor Model for Predicting the Flow of a Hydraulic Pump Based on Graph Convolutional Network–Long Short-Term Memory

A Soft Sensor Model for Predicting the Flow of a Hydraulic Pump Based on Graph Convolutional Network–Long Short-Term Memory

Small data challenges for intelligent prognostics and health management: a review

A data-driven simulation and Gaussian process regression model for hydraulic press condition diagnosis

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