Special feature on rotating machinery condition monitoring by connecting physics-based and data-driven methods

2021 ◽  
Vol 33 (1) ◽  
pp. 010103
Author(s):  
Yaguo Lei ◽  
Xihui (Larry) Liang ◽  
Fakher Chaari
Keyword(s):  
Condition Monitoring ◽  
Rotating Machinery ◽  
Data Driven ◽  
Machinery Condition Monitoring

A New Cepstral Analysis Procedure of Recovering Excitations for Transient Components of Vibration Signals and Applications to Rotating Machinery Condition Monitoring

2001 ◽  
Vol 123 (2) ◽  
pp. 222-229 ◽  
Author(s):  
G. T. Zheng ◽  
W. J. Wang
Keyword(s):  
Condition Monitoring ◽  
Rotating Machinery ◽  
Analysis Procedure ◽  
Transient Signal ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Machinery Condition Monitoring ◽  
Cepstral Analysis ◽  
Cepstrum Analysis ◽  
Complex Cepstrum

A new cepstral analysis procedure with the complex cepstrum for recovering excitations causing multiple transient signal components from vibration signals, especially from rotor vibration signals, has been developed. Along with the problem of singularity, a major problem of the cepstrum is that it cannot provide a correct distribution of the excitations. To solve these problems, a signal preprocessing method, whose function is to provide a definition for the distribution of the excitations along the quefrency axis and remove singular points from the transform, has been added to the cepstrum analysis. With this procedure, a correct distribution of the excitations can be obtained. An example of application to the condition monitoring of rotor machinery is also presented.

Design about Monitoring and Diagnosis System Structure of Rotating Machinery Based on LabVIEW

2011 ◽  
Vol 383-390 ◽  
pp. 1792-1796 ◽  
Author(s):  
Yan Jun Lu ◽  
Ying Liu
Keyword(s):  
Condition Monitoring ◽  
Virtual Instrument ◽  
Rotating Machinery ◽  
System Structure ◽  
Diagnosis System ◽  
Main Research ◽  
Object A ◽  
Monitoring And Diagnosis ◽  
Machinery Condition Monitoring ◽  
High Degree

Rotating machinery becomes more and more large and complex, increasingly high degree of automation. Rotating machinery fault could easily lead to heavy losses. Therefore, the requirements of monitoring and diagnosis systems are increasing high. In this paper, the superiority of the application of virtual instrument on condition monitoring and diagnosis system building in the industrial production is described. And then, the rotor system as the main research object, a rotating machinery condition monitoring and diagnosis system is built by using Virtual Instrument technology. At the same time, the structure of the condition monitoring and diagnosis system is discussed. Besides, data acquisition process and fault features recognition method are discussed as well. Finally, the correctness and accuracy of fault detection are verified by means of experiments.

scholarly journals Design and Realization of Rotating Machinery Conditions Monitoring System Based on LabVIEW

2018 ◽  
Vol 2 (2) ◽  
pp. 49
Author(s):  
Qiyuan Fan
Keyword(s):  
Data Acquisition ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Virtual Instrument ◽  
Hot Spot ◽  
Nonlinear Dynamic Analysis ◽  
Rotating Machinery ◽  
Machinery Condition Monitoring ◽  
Condition Monitoring System ◽  
Machinery System

Abstract: Nonlinear dynamic analysis of rotating machinery system has always been the hot spot of the rotational dynamics research. This article sets up a rotating machinery condition monitoring system to realize the measurement of system dynamic characteristic parameters based on NI(National Instruments) virtual instruments technology. The measurement of vibration signal of rotating machinery system is achieved by using NI company general data acquisition module of NI Company. Meanwhile, by analyzing and processing the acquired data using LabVIEW 2012, the dynamic characteristics, such as .the speed of the rotating machinery system, the axis trajectory, spectrum parameters, are attained. The measurement results show that the rotating machinery condition monitoring system based on LabVIEW is easy to operate, easy to realize the function extension and maintenance, and that it can be used in the industrial engineering projects with rotation characteristics. LabVIEW as the development tools used by virtual instrument function is very powerful data acquisition software products support is one of the features of it, so using LabVIEW programming and data acquisition is simple and convenient.

Condition monitoring of IGBT modules using online TSEPs and data‐driven approach

2021 ◽  
Author(s):  
Varaha Satya Bharath Kurukuru ◽  
Ahteshamul Haque ◽  
Arun Kumar Tripathi ◽  
Mohammed Ali Khan
Keyword(s):  
Condition Monitoring ◽  
Data Driven ◽  
Igbt Modules ◽  
Data Driven Approach

Automatic Condition Monitoring and Early Fault Detection of Gearbox

2009 ◽  
Author(s):  
Dong Wang ◽  
Qiang Miao ◽  
Chengdong Wang ◽  
Jingqi Xiong
Keyword(s):  
Condition Monitoring ◽  
Wavelet Packet ◽  
Life Time ◽  
Health Condition ◽  
Evaluation Index ◽  
Dynamic Threshold ◽  
Time Data ◽  
Machinery Condition Monitoring ◽  
Maintenance Program ◽  
Gear Fault

Condition based maintenance (CBM) improves decision-making performances for a maintenance program through machinery condition monitoring. Therefore, it is a key step to trace machinery health condition for CBM. In this paper, a novel method is proposed to establish a health evaluation index named automatic evaluation index (AEI) and its corresponding dynamic threshold using Wavelet Packet Transform (WPT) and Hidden Markolv Model (HMM). In this process, WPT is used to decompose signal into detail signals and exhibits prominent gear fault features. In addition, HMM employed here is to recognize two concerned states of gear in the whole life validation, including normal gear state and early gear fault state. It is also important to build a dynamic threshold to differentiate the two states automatically. The proposed dynamic threshold not only renews by itself according to the history values of AEI but also easily and automatically detects occurrence of gear early fault. Finally, a set of whole life time data ending in gear failure is used to verify the proposed method effectively. Further, some related parameters included in this method are discussed and the obtained results show that condition monitoring performance of the proposed method is excellent in detection of gear failure.

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