A high-speed D-CART online fault diagnosis algorithm for rotor systems

Deng, Huaxia; Diao, Yifan; Wu, Wenzhuo; Jin, Zhang; Zhong, Xiang

doi:10.1007/s10489-019-01516-2

Cited by 29 publications

(7 citation statements)

References 34 publications

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“…Lu et al [62] put forward a genetic algorithm based on dynamic search strategy, which showed good feature extraction and selection ability in two kinds of fault experiments of rotor unbalanced vibration and bearing damage. Deng et al [63] presented an improved classification and regression tree (CART) algorithm, namely D-CART algorithm, which can quickly locate the rotor system fault and identify the fault type on the premise of ensuring the accuracy.…”

Section: Feature Extraction Based On Intelligent Algorithmmentioning

confidence: 99%

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

Pan

Huo

et al. 2020

Chin. J. Mech. Eng.

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The high-end equipment represented by high-end machine tools and aero-engines is the core component of the national intelligent manufacturing plan, and the mass unbalance is the main reason for its excessive vibration, that seriously impacts the operation efficiency and running life of the equipment. In order to change the traditional way that the fault of equipment can only be repaired by human, the self-recovery mechanism of human and animal are given to the equipment in this paper, which forms the self-recovery regulation (SR) system for unbalance vibration of high-end equipment. The system can online generate the self-recovery force to restrain the unbalance vibration of the equipment in operation, which is an important direction for the development of the equipment to the advanced intelligent stage. Based on the basic principles of SR technique, the typical engineering application cases of this technique in the field of aeroengine and high-end machine tools are introduced, and four related studies promoting the development of this technique are summarized and analyzed in turn. It includes feature extraction, imbalance location, regulation method and balancing actuator. Self-recovery Regulation (SR) Technique is an important way to realize intelligent manufacturing and intelligent maintenance. Relevant research can lay a technical foundation for the development of high-end equipment with self-health function.

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Section: Feature Extraction Based On Intelligent Algorithmmentioning

confidence: 99%

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

Pan

Huo

et al. 2020

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…e approach for reliably monitoring the rotor system health is to create a real bearing load from the rotor system in its undamaged state [8]. However, in the actual work site, the acquisition system to determine the real bearing load may be affected by the noise in the environment or the uncertain factors caused by the manufacturing and service environment of the rotating machinery structure [9].…”

Section: Introductionmentioning

confidence: 99%

A Proposed Bearing Load Identification Method to Uncertain Rotor Systems

Mao

Zhang

Feng

et al. 2021

Shock and Vibration

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Bearings are considered as important mechanical components in rotating machines. Bearing load is used as an indication of monitoring rotor system health, but there are interval and probability uncertain parameters in the process of obtaining bearing load from the rotor system. A bearing load strip enclosed by two bounding distributions is then formed, rather than a single distribution that we usually obtain through the load identification method for a deterministic rotor system. In this paper, a computational inverse approach that combines the interval and perturbation analysis method with regularization is presented to stably identify bearing load strip. Using an interval analysis method, a calculated transient response of the rotor structure only subjecting to the bearing load can be approximated as a linear function of the interval parameters in the rotor system. The perturbation analysis method based on Taylor expansion is used to transform the problem of the bearing load identification involving in probability parameters into two kinds of certain inverse problem, namely, the bearing load identification combining the mean value of uncertain parameters with calculated transient response function and the sensitivity identification of bearing load to each probability parameter. Regularization is used to overcome ill-posedness of bearing load identification arising from the noise-contaminated observed response. A rotor system with two bearings is investigated to demonstrate the effectiveness and accuracy of the presented method.

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“…machine learning) is used which categorises the images into different types. In the past decades, automatic image recognition has brought many benefits along with the improvement of digital camera and the increase in computational capacity, which has been applied in many fields including plant disease detection [9, 10], food analysis [11], medical image processing [12, 13], biometrics [14], intelligent manufacturing [15], among others [16–18] and has achieved excellent results. The machine learning for image recognition includes the processes of feature extraction and image classification, in which feature extraction is crucial, and its quality directly determines the final effect of image recognition.…”

Section: Introductionmentioning

confidence: 99%

Intelligent monitoring method of water quality based on image processing and RVFL‐GMDH model

et al. 2020

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The water quality, contaminant migration characteristics, and emissions quantity of pollutants in the basin would have a great impact on aquatic creatures, agricultural irrigation, human life, and so on. In the aquaculture industry, because water colour can reflect the species and number of phytoplankton in the water, the water quality type can be obtained by analysing the colour of the aquaculture water using image processing techniques. Therefore, this study proposes an intelligent monitoring approach for water quality. The critical features of water colour images are extracted, and then using the machine learning methods, an intelligent system for water quality monitoring is established based on the fused random vector functional link network (RVFL) and group method of data handling (GMDH) model. The proposed approach presents a superior performance relative to other state‐of‐the‐art methods, and it achieves an average predicting accuracy of 96.19% on the feature dataset. Experimental findings demonstrate the validity of the proposed approach, and it is accomplished efficiently for the monitoring of water quality.

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A high-speed D-CART online fault diagnosis algorithm for rotor systems

Cited by 29 publications

References 34 publications

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

A Proposed Bearing Load Identification Method to Uncertain Rotor Systems

Intelligent monitoring method of water quality based on image processing and RVFL‐GMDH model

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