An Alarm Method for a Loose Parts Monitoring System

In the paper, a fault feature extraction method for rotor system is proposed based on Hilbert marginal spectrum. Compared with the spectrum analysis method via Fourier transformation, it is more effective for the rotating machinery vibrating signal analysis. Extracting the rotor system fault feature frequency from Hilbert marginal spectrum can not only enhance the frequency resolution, but also remove other unrelated frequency component, so as to make the spectrum peak of the fault feature frequency more obviously, and the analysis diagnosis results more accurately. This method result is applied to the fault feature extraction and diagnosis of the rotor system, and the analysis results of the experiment signal verify the validity of this method.

show abstract

“…(1) The integrated EMD noise reduction disposal is made on the rotor system vibration signal [4], gaining the relatively independent IMF{…}.…”

Section: Extraction Methods Of Rotor System Fault Featurementioning

confidence: 99%

A Rotor Fault Feature Extraction Method Based on the Hilbert Marginal Spectrum

Wang

Zou

2012

AMM

View full text Add to dashboard Cite

show abstract

“…Among them, vibration monitoring for primary coolant and secondary loop systems including the reactor, steam generator, pressurizer, heat exchanger, turbine, and generator is critical. Enhanced vibration monitoring systems such as an acoustic leak monitoring system (ALMS), reactor coolant pump vibration monitoring system (RCPVMS), internal vibration monitoring system (IVMS), and loose parts monitoring system (LPMS) thus far have been developed using acceleration, acoustic emission (AE), and displacement sensors during the past few decades [4][5][6][7][8][9]. In addition, many fault diagnoses of machinery using high vibration analysis have been proposed [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Patch-Type Vibration Visualization (PVV) Sensor System Based on Triboelectric Effect

Kim

Seol

Chung

et al. 2021

Sensors

View full text Add to dashboard Cite

Self-powered wireless sensor systems have emerged as an important topic for condition monitoring in nuclear power plants. However, commercial wireless sensor systems still cannot be fully self-sustainable due to the high power consumption caused by excessive signal processing in a mini-electronic computing system. In this sense, it is essential not only to integrate the sensor system with energy-harvesting devices but also to develop simple data processing methods for low power schemes. In this paper, we report a patch-type vibration visualization (PVV) sensor system based on the triboelectric effect and a visualization technique for self-sustainable operation. The PVV sensor system composed of a polyethylene terephthalate (PET)/Al/LCD screen directly converts the triboelectric signal into an informative black pattern on the LCD screen without excessive signal processing, enabling extremely low power operation. In addition, a proposed image processing method reconverts the black patterns to frequency and acceleration values through a remote-control camera. With these simple signal-to-pattern conversion and pattern-to-data reconversion techniques, a vibration visualization sensor network has successfully been demonstrated.

show abstract

“…Empirical Mode Decomposition (EMD) [2] as a new signal analysis theory be used to many other areas,in signal tendency item extraction in more and more importance [3,4] .However, EMD because of serious endpoint effect and modal aliasing phenomenon affect the accuracy of analytical results.many scholars are to force in the endpoint effect [5,6] . In the endpoint effect and modal aliasing phenomenon, Zhaohua Wu,Norden E. [7] put forward Ensemble Empirical Mode Decomposition(EEMD),a kind of new white noise auxiliary data analysis method in recent years.The EEMD is considered as a important achievements of the improving EMD [8] ,and is a new signal analysis technology.…”

Section: Introductionmentioning

confidence: 99%

Improved EEMD on the Application Research of Signal Trend Analysis

Zhang

Wang

Cao

et al. 2012

AMR

View full text Add to dashboard Cite

The trend of the measured signal can not only reflect the influence of the external environment, and also reflect the performance of the machine itself mutations. Therefore, removing and extracting tendency item is the necessary link in signal pretreatment.In order to eliminate endpoint effect and modal aliasing phenomenon in EMD and EEMD, based on EEMD,improved EEMD is put forward and the improved EEMD in the application of the signal trend analysis is researched in this paper.In the measured signals to join in a ramp signals,With the improved EEMD decomposition extracted residual items,and the residual items with the original slope signal similarity analysis,the similarity is 0.975.compared to EMD extracted residual items similarity 0.898, EEMD extracted residual items similarity 0.961,the improved EEMD extracted residual item can more accurately reflect the trend of signal.

show abstract

An Alarm Method for a Loose Parts Monitoring System

Cited by 10 publications

References 4 publications

A Rotor Fault Feature Extraction Method Based on the Hilbert Marginal Spectrum

A Rotor Fault Feature Extraction Method Based on the Hilbert Marginal Spectrum

Patch-Type Vibration Visualization (PVV) Sensor System Based on Triboelectric Effect

Improved EEMD on the Application Research of Signal Trend Analysis

Contact Info

Product

Resources

About