Continuous wavelet transform and neural network for condition monitoring of rotodynamic machinery

Kaewkongka, Tonphong; Au, Yhj; Rakowski, R.T.; Jones, BE

doi:10.1109/imtc.2001.929543

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Dynamic optimization is based on the change in running status of the hoist and repeated optimization, it's the fundamental points which is different from the traditional predictive control optimal control. In addition, predictive control adopts multiple steps optimization [4] , compared with the traditional way of one step optimization, it can obtain more dynamic parameters that can reflect the running state trend of the hoist , thus it can accurately overcome the effects of various interference so that predictive control can get good results in the application of vibration risk predictive control of the hoist [6] .…”

Section: The Predictive Control Principle Based On Vibration Signal Amentioning

confidence: 99%

Research on Vibration Risk Predictive Control of Auxiliary Shaft Hoist based on MCGS

Liu¹,

Li²,

Meng³

et al. 2015

Advances in Computer Science Research

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For the existing problems of security risks when the amplitude of auxiliary shaft hoist vibration is too large, we use the vibration sensors to collect the vibration signals of auxiliary shaft hoist and display it in the monitoring software MCGS, then research on the vibration risk predictive control according to the analysis of the collecting signals integrated into signal processing, automatic control technology, and using PLC to control the converter so as to adjust the running state of the hoist., finally realizing the online monitoring of the hoist running status and vibration risk predictive control of auxiliary shaft hoist.

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Section: The Predictive Control Principle Based On Vibration Signal Amentioning

confidence: 99%

Research on Vibration Risk Predictive Control of Auxiliary Shaft Hoist based on MCGS

Liu¹,

Li²,

Meng³

et al. 2015

Advances in Computer Science Research

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show abstract

“…Comparative studies of various vibration and AE signal acquisition methods were reported in [3][4][5]. Previous studies [6][7][8] confirmed that AE monitoring is more reliable than vibration monitoring, as the former can detect the subsurface crack growth, whereas the latter can at best detect a defect only when it emerges on the surface of a structure. Yashioka [9] described an AE system for source location.…”

Section: Introductionmentioning

confidence: 98%

Proposing a New Acoustic Emission Parameter for Bearing Condition Monitoring in Rotating Machines

Niknam

Songmené

2013

Transactions of the Canadian Society for Mechanical Engineering

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Bearings are important machine parts and their condition is often critical to success of an operation or process, hence there is a great need for periodic knowledge of their performance. According to reported research works in the past several years, it is believed that the extracted information from acoustic emission (AE) signals can be used for bearing condition monitoring. In this work, a novel parameter based on using the ratio of AE mean (μ) and AE standard deviation (σ), formulated as μ/σ is proposed to distinguish between lubricated and dry bearings. A heavy duty test rig was used in experimental work. Various levels of radial loads and rotational speed (ω) were applied to rotating shaft, which is connected to rolling element bearings. It was found that, except few cases, regardless of various levels of radial loads used, at higher levels of rotational speed, dry and lubricated bearings can be clearly distinguished when using proposed parameter.

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“…The vibration signal reaches the vibration sensor through the complex transmission path of the mechanical system, and the fault signal is inevitably disturbed in the transmission process. The uncertain fault diagnosis information after transmission through the complex path is very easy to cause false diagnosis [2]. The occurrence of false diagnosis will lead to excessive maintenance or false maintenance, and even lead to major safety accidents [3].…”

Section: Introductionmentioning

confidence: 99%

Fault feature extraction and recognition of vibration signal based on CWD-SK and HMM

Fan

Zhu

2022

J. Phys.: Conf. Ser.

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Aiming at the problem of rolling bearing fault diagnosis caused by interference information in the process of vibration transmission, this paper puts forward the theory and method of vibration signal fault diagnosis based on Choi Williams distribution spectral kurtosis (SK) combined with hidden Markov model (HMM). The fault location method of local mean decomposition (LMD) is used to screen the PF component with obvious fault characteristic information, CWD-SK is calculated and set as the characteristic parameter to realize the feature extraction of the initial fault of the vibration signal. CWD-SK is input into HMM as the feature vector to classify and identify the fault features of the vibration signal. Finally, the effectiveness of the algorithm is verified by the simulation of the actual data. It provides a robust theoretical and methodological basis for the establishment of fault diagnosis and classification recognition technology suitable for initial rolling mechanical vibration signals.

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Continuous wavelet transform and neural network for condition monitoring of rotodynamic machinery

Cited by 7 publications

References 8 publications

Research on Vibration Risk Predictive Control of Auxiliary Shaft Hoist based on MCGS

Research on Vibration Risk Predictive Control of Auxiliary Shaft Hoist based on MCGS

Proposing a New Acoustic Emission Parameter for Bearing Condition Monitoring in Rotating Machines

Fault feature extraction and recognition of vibration signal based on CWD-SK and HMM

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