Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

Jiao, Jing; Yue, Jianhai; Di, Pei

doi:10.1088/1757-899x/241/1/012034

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…is paper systematically studies the relationship between the marketing strategy, service quality and cost of online retail enterprises and the purchase intention of online consumers and analyzes the influencing factors of online consumers' purchase behavior from different perspectives. Reasonable website design, construction of excellent network facilities, service quality, and standardized operation and management are important factors for online retail enterprises to attract online consumers [7]. It is pointed out that the key factors influencing consumers in the network environment include product quality, product price, comprehensive online shopping experience, and risk perception [8].…”

Section: Related Workmentioning

confidence: 99%

Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

Liao

Tsai

2021

Mathematical Problems in Engineering

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The comprehensive B2C online marketing is analyzed, and the current situation and shortage of comprehensive B2C online marketing strategies are summarized. Then, based on the relevant theories of consumer behavior and online marketing, the model of influencing factors in the purchasing decision-making process of online consumers is preliminarily constructed, the online purchasing behavior of consumers is studied by means of questionnaire survey, and the model is revised and improved through data collection and verification. Finally, based on the model, the online marketing strategy is discussed from the aspects of comprehensive B2C online marketing construction, product positioning, price strategy, channel construction, website design, and so on. It has important guiding significance to comprehensive B2C online marketing practice. Aiming at the B2C online marketing problem of multimodel fusion with multiobservation samples, a new multimodel fusion B2C online marketing algorithm based on LS-SVM is proposed, which is suitable for multiobservation samples. In each B2C online marketing of multimodel fusion, the mode of B2C online marketing to be multimodel fusion is represented by the multiobservation sample set. Firstly, the label of the multiobservation sample set is assumed, and this assumption condition is taken as the constraint condition of the optimization problem in LS-SVM. Thus, the B2C online marketing error of multimodel fusion is obtained. The category of multiobservation samples was determined by comparing the B2C online marketing errors of multimodel fusion under two assumptions. The B2C network marketing prediction method, stacking integrated learning method based on multimodel fusion, is adopted to build a multimachine learning algorithm embedded into the stacking integrated learning B2C network marketing prediction model. Through verification, it shows that the lower the correlation degree, the better the model prediction effect. Compared with the traditional single-model prediction, the B2C network marketing prediction method based on multimodel fusion stacking integrated learning method has higher prediction accuracy. The model prediction effect is better.

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Section: Related Workmentioning

confidence: 99%

Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

Liao

Tsai

2021

Mathematical Problems in Engineering

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“…Therefore, to prevent potential dangers, actuator fault detection and isolation and fault tolerant control have been studied by many researchers (Ermeydan and Kiyak, 2017; Kilic and Unal, 2021). At present, the main methods of system fault diagnosis are based on analytical model (Li et al , 2017), signal processing (Jiao et al , 2017) and knowledge (Wen et al , 2018). Fault diagnosis methods based on analytical model mainly include equivalent space method, state estimation method and parameter estimation method.…”

Section: Introductionmentioning

confidence: 99%

Fault detection based on extended state observer and interval observer for UAVs

Cao

Wang

et al. 2022

AEAT

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Purpose The purpose of this paper is to present an actuator fault detection method for unmanned aerial vehicles (UAVs) based on interval observer and extended state observer. Design/methodology/approach The proposed algorithm has very little model dependency. Therefore, a six-degree-of-freedom linear equation of UAVs is first established, and then, combined with actuator failure and external disturbances in flight control, a steering gear model with actuator failure (such as stuck bias and invalidation) is designed. Meanwhile, an extended state observer is designed for fault detection. Moreover, a fault detection scheme based on interval observer is designed by combining fault and disturbances. Findings The method is testified on the extended state observer and the interval observer under the failure of the steering gear and bounded disturbances. The simulation results show that the two types of fault detection schemes designed can successfully detect various types of faults and have high sensitivity. Originality/value This research paper studies the failure detection scheme of the UAVs’ actuator. The fault detection scheme in this paper has better performance on actuator faults and bounded disturbances than using regular fault detection schemes.

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“…Hoang et al observed that the convolutional neural network processed multiple signal sources simultaneously and was constrained by sensor functions [17]. Jiao et al proposed a new bearing fault diagnosis method based on the least squares support vector machine for feature-level fusion and the Dempster-Shafer (DS) evidence theory for decision-level fusion [18]. Song et al proposed a multisensor data fusion classification method [19] based on linear discriminant analysis for feature classification.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Zhang

2021

Symmetry

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The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

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Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

Cited by 7 publications

References 6 publications

Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

Fault detection based on extended state observer and interval observer for UAVs

Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

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