Hybrid model-driven and data-driven approach for the health assessment of axial piston pumps

Chao, Qun; Xu, Zi; Shao, Yuechen; Tao, Jianfeng; Liu, Chengliang; Ding, Shuo

doi:10.1504/ijhm.2023.129123

Cited by 17 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, prolonged exposure to high-intensit y working conditions makes mechanical systems susceptib le to a range of failure modes. Therefore, research into adv anced fault diagnosis techniques is imperative to mitigate economic losses and enhance production safety [3][4][5]. Base d on the current research work, further works are carried o ut in this paper.…”

Section: A Background Researchmentioning

confidence: 98%

A New Lightweight Fault Diagnosis Framework Towards Variable Speed Rolling Bearings

Liu,

Zhang,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Under the variable speed condition, the rolling bearing vibration signal shows non-smooth characteristics such as frequency modulation and amplitude modulation, which makes it difficult to effectively extract the fault characteristics by relying on the analysis method of traditional signals based on the smooth assumption, as well as the excessive speed change and the excitation response caused by the failure of the equipment components will lead to the change of the detected vibration signal. In this paper, a lightweight fault diagnosis framework called variable speed fault diagnosis framework (VSFD-Net) is proposed to address the above challenges. Firstly, a separable multi-scale convolutional block is proposed to achieve the extraction of multi-local sensory field features, thus reducing the learning parameters and computational effort during the training process. Secondly, the development of a broadcast self-concern block is introduced to achieve capturing the critical fine-grained global extent of the signal while avoiding complex operations such as matrix multiplication and multi-dimensional exponentials. Through experiments on three mechanical systems, it is demonstrated that the framework has significant advantages over existing neural network models, possessing lightweight and robustness.

show abstract

Section: A Background Researchmentioning

confidence: 98%

A New Lightweight Fault Diagnosis Framework Towards Variable Speed Rolling Bearings

Liu,

Zhang,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To tackle this challenge, industries are rapidly adopting predictive maintenance (PdM) solutions [5,6]. PdM is a proactive maintenance technique that utilizes asset data (realtime and historical) in order to determine whether the asset will fail in the future.…”

Section: Introductionmentioning

confidence: 99%

Predictive Maintenance Framework for Fault Detection in Remote Terminal Units

Lekidis,

Georgakis,

Dalamagkas

et al. 2024

Forecasting

View full text Add to dashboard Cite

The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, which may diminish its performance or even lead to its breakdown, rendering it non-operational. Lately, predictive maintenance methods have been considered for industrial systems, such as power generation stations, as a proactive measure for preventing failures. Such methods use data gathered from industrial equipment and Machine Learning (ML) algorithms to identify data patterns that indicate anomalies and may lead to potential failures. However, industrial equipment exhibits specific behavior and interactions that originate from its configuration from the manufacturer and the system that is installed, which constitutes a great challenge for the effectiveness of ML model maintenance and failure predictions. In this article, we propose a novel method for tackling this challenge based on the development of a digital twin for industrial equipment known as a Remote Terminal Unit (RTU). RTUs are used in electrical systems to provide the remote monitoring and control of critical equipment, such as power generators. The method is applied in an RTU that is connected to a real power generator within a Public Power Corporation (PPC) facility, where operational anomalies are forecasted based on measurements of its processing power, operating temperature, voltage, and storage memory.

show abstract

“…Hence, it is difficult to satisfy the requirements in modern industrial systems [14]. In summary, data-driven methods are more suitable than the traditional spectrum analysis methods based on vibration signals considering the requirements of modern industrial systems [15].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Multi-Local Model Bearing Fault Diagnosis Method Using Small Sample Fusion

Zhou,

Li,

Han

2023

Sensors

View full text Add to dashboard Cite

It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor operations in a reliable and intelligent way. However, most of the work is mainly suitable for situations where there is sufficient monitoring data of the bearings. In industrial systems, only a small amount of monitoring data can be collected by the bearing sensors due to the harsh monitoring conditions and the short time of the signals of some special motor bearings. To solve the issue above, this paper introduces a transfer learning strategy by focusing on the multi-local model bearing fault based on small sample fusion. The algorithm mainly includes the following steps: (1) constructing a parallel Bi-LSTM sub-network to extract features from bearing vibration and current signals of industrial motor bearings, serially fusing the extracted vibration and current signal features for fault classification, and using them as a source domain fault diagnosis model; (2) measuring the distribution difference between the source domain bearing data and the target bearing data using the maximum mean difference algorithm; (3) based on the distribution differences between the source domain and the target domain, transferring the network parameters of the source domain fault diagnosis model, fine-tuning the network structure of the source domain fault diagnosis model, and obtaining the target domain fault diagnosis model. A performance evaluation reveals that a higher fault diagnosis accuracy under small sample fusion can be maintained by the proposed method compared to other methods. In addition, the early training time of the fault diagnosis model can be reduced, and its generalization ability can be improved to a great extent. Specifically, the fault diagnosis accuracy can be improved to higher than 80% while the training time can be reduced to 15.3% by using the proposed method.

show abstract

Hybrid model-driven and data-driven approach for the health assessment of axial piston pumps

Cited by 17 publications

References 0 publications

A New Lightweight Fault Diagnosis Framework Towards Variable Speed Rolling Bearings

A New Lightweight Fault Diagnosis Framework Towards Variable Speed Rolling Bearings

Predictive Maintenance Framework for Fault Detection in Remote Terminal Units

An Intelligent Multi-Local Model Bearing Fault Diagnosis Method Using Small Sample Fusion

Contact Info

Product

Resources

About