Vibration Reliability Evaluation of Main Fan Spindle Bearings

Pang, Xiaoxu; Qiu, Ming; Liang, Yilong; Chen, Lihai

doi:10.1155/2019/8182530

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…8 In practical applications, the initial parameters of the BP neural network tend to cause the network to converge slowly and fall into local extremum. 9 Although some improved algorithms can refrain from the local minimum value and greatly improve the predicted precision, 10,11 many parameters need to be set in advance, and the low convergence speed results in the long training time of the model.…”

Section: Introductionmentioning

confidence: 99%

A health management system for large vertical mill

Han

Wang

et al. 2020

International Journal of Distributed Sensor Networks

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The large vertical mill has complicated structure and tens of thousands of parts, which is a critical grinding equipment for slag and cinder. As large vertical mill always works in severe conditions, the on-line monitoring, timely fault diagnosis, and trend prediction are very important guarantees for the safe service and saving maintaining costs. To address this issue, the health management system for large vertical mill is developed. More specifically, in order to manage reservoirs of state-related running data, the intrinsic physic data, and diagnosis knowledge base, an entity-relationship-model-based database is first constructed. Based on the fault diagnosis reasoning of experts, the fault tree is developed and the fault diagnosis rules are derived. Especially, a hybrid condition prognosis method based on backtracking search optimization algorithm and neural network is developed, and in comparison with traditional back propagation neural network and ant colony neural network, the developed backtracking search optimization algorithm and neural network gets superior hybrid prediction performance in prediction accuracy and training efficiency. Finally, the health management system, including the functions of condition monitoring, fault diagnosis, and trend prediction for large vertical mill is implemented using Microsoft Visual Studio C # and Microsoft SQL Server.

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Section: Introductionmentioning

confidence: 99%

A health management system for large vertical mill

Han

Wang

et al. 2020

International Journal of Distributed Sensor Networks

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Grey Relation Evaluation and Truth Value Fusion of Rolling Bearing Multisource Residual Stress

Pang

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

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Bearing ring residual stress test data obtained by µ-360 s residual stress analyzer have the characteristics of unknown probability distribution and limited samples. For this problem, this study introduces the uncertainty and proposes a grey relation method to estimate the true value of the bearing ring residual stress. Based on poor information theory (incomplete and insufficient information for the characteristic presented in the subject investigated) and by fusing the membership function method, maximum membership method, rolling mean method, and bootstrap method, the true value sequence of the residual stress is obtained. On this basis, true value fusion is implemented again using the grey bootstrap method, and the estimated true value of the residual stress for the bearing ring was obtained. The results show that the residual stresses of bearing rings are fused by multiple methods, and the overall estimated true value of the residual stress of the bearing ring is −578 MPa. Owing to different processing techniques, the true value of the residual stress varies in different parts of the ring as follows: −918 MPa on the large end surface of the ring, −673 MPa on the small end face of the ring, −228 MPa at the vertical test point on the outer surface of the ring, and −231 MPa at the parallel test point on the outer surface of the ring. The error between the estimated true value obtained using the grey relation method and that obtained through the fusion of multiple methods is generally 10% or less, thereby confirming the effectiveness of the grey relation method.

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A review of intelligent methods of health assessment technology

Liu¹,

Li²,

Zhao³

2023

Intell Robot

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The core technology of prognostics and health management, a key technology that detects system anomalies, is health assessment, which analyzes and diagnoses the current system working status and quantitatively assesses the health of the system. This paper reviews the development of health assessment technology in recent years from three aspects: health definition, health assessment indicators, and health assessment approaches. In terms of health definition, this paper summarizes three common definition methods. Health assessment indicators are reviewed from four levels: process variables, data features, residuals, and fusion indicators. Finally, health assessment approaches are divided into model-based, data-driven, and fusion approaches. Concerning the data-driven approach, rapidly developing health assessment research based on an intelligent approach is discussed. The paper also compares various approaches and identifies the current challenges and development prospects of this technology.

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Vibration Reliability Evaluation of Main Fan Spindle Bearings

Cited by 3 publications

References 15 publications

A health management system for large vertical mill

A health management system for large vertical mill

Grey Relation Evaluation and Truth Value Fusion of Rolling Bearing Multisource Residual Stress

A review of intelligent methods of health assessment technology

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