scholarly journals Improvement of Intake Structures in a Two-Way Pumping Station with Experimental Analysis

2020 ◽  
Vol 10 (19) ◽  
pp. 6842
Author(s):  
Yanjun Li ◽  
Rong Lu ◽  
Huiyan Zhang ◽  
Fanjie Deng ◽  
Jianping Yuan
Keyword(s):  
Frequency Domain ◽  
Great Influence ◽  
Operating Conditions ◽  
Stable Operation ◽  
Pressure Pulsations ◽  
Pump Unit ◽  
Measuring Point ◽  
Time Frequency ◽  
Pumping Station ◽  
Bell Mouth

Pumping stations are important regulation facilities in a water distribution system. Intake structures can generally have a great influence on the operational state of the pumping station. To analyze the effects of the bell mouth height of the two-way intake on the performance characteristics and the pressure pulsations of a two-way pumping station, the laboratory-sized model pump units with three different intakes were experimentally investigated. To facilitate parameterized control, ellipse and straight lines were used to construct the profile of the bell mouth. The frequency domain and time-frequency domain of the pressure pulsations on the wall of intakes were analyzed by the Welch’s power spectral density estimate and the continuous wavelet transform (CWT) methods, respectively. The results showed that the bell mouth height (H) has significant influences on the uniformity of the impeller inflow and the operation stability of the pump unit. When H = 204 mm, the data fluctuated greatly throughout the test process and the performance curves are slightly lower than the other two schemes. As the bell mouth height gradually decreases, the average pressure difference of each measuring point began to decrease, more homogeneous velocity distribution of impeller inflow can be ensured. The amplitude of blade passing frequency is obvious in the spectrum. While when (H) is more than 164 mm, the main frequency of pressure pulsations at three points fluctuates with the rotation of the impeller. When H decreases to 142 mm, pressure pulsations will be independent of the operating conditions and positions which contributes to the long-term stable operation of the pump unit.

Class-information–incorporated kernel entropy component analysis with application to bearing fault diagnosis

2020 ◽  
pp. 107754632093203
Author(s):  
Hongdi Zhou ◽  
Fei Zhong ◽  
Tielin Shi ◽  
Wuxing Lai ◽  
Jian Duan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Hebbian Learning ◽  
Component Analysis ◽  
Operating Conditions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Class Information ◽  
Entropy Component

Rolling bearings are present ubiquitously in industrial fields; timely fault diagnosis is of crucial significance in avoiding serious catastrophe. The extraction of ideal fault feature is a challenging task in vibration-based bearing fault detection. In this article, a novel method called class-information–incorporated kernel entropy component analysis is proposed for bearing fault diagnosis. The method is developed based on the Hebbian learning theory of neural network and the kernel entropy component analysis which attempts to compress the most Renyi quadratic entropy of input dataset after dimension reduction and presents a good performance for nonlinear feature extraction. Class-information–incorporated kernel entropy component analysis can take advantage of the label information of training samples to guide dimensional reduction and still follow the same simple mathematical formulation as kernel entropy component analysis. The high-dimensional feature dataset including time-domain, frequency-domain, and time–frequency domain characteristic parameters is first derived from the vibration signals. Then, the intrinsic geometric features are extracted by class-information–incorporated kernel entropy component analysis, and a classification strategy based on fusion information is applied to recognize different operating conditions of bearings. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

scholarly journals Application of the Acoustic Emission Method for Diagnosis of On-Load Tap Changer

2017 ◽  
Vol 42 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Henryk Majchrzak ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Operating Conditions ◽  
Time Frequency ◽  
Measurement Results ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Short Time

Abstract This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.

scholarly journals Application of Cyclo-Non-Stationary Indicators for Bearing Monitoring Under Varying Operating Conditions

2017 ◽  
Author(s):  
Konstantinos Gryllias ◽  
Simona Moschini ◽  
Jerome Antoni
Keyword(s):  
Frequency Domain ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Computational Cost ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Cyclic Behavior ◽  
Diagnostic Features ◽  
Time Frequency

Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time-frequency domain and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. A process x(t) is said to be nth-order cyclostationary with period T if its nth-order moments exist and are periodic with period T. Several tools, such as the Spectral Correlation Density (SCD) and the Cyclic Modulation Spectrum (CMS) can be used in order to extract interesting information concerning the cyclic behavior of cyclostationary signals. In order to measure the cyclostationarity from order 1 to 4, concise and global indicators have been proposed. However, in a number of applications such as aircraft engines and wind turbines the characteristic vibroacoustic signatures of rotating machinery depend on the operating conditions of the rotational speed and/or the load. During the last decades fault diagnostics of rotating machinery under variable speed/load has attracted a lot of interest. The classical cyclostationary tools can be used under the assumption that the speed of machinery is constant or nearly constant, otherwise the vibroacoustic signal becomes cyclo-non-stationary. In order to overcome this limitation a generalization of both SCD and CMS functions have been proposed displaying cyclic Order versus Frequency. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclostationarity in order to cover the speed varying conditions. The proposed indicators of cyclo-non-stationarity (ICNS) are expected to summarize the information at various statistical orders and at lower computational cost compared to the Order-Frequency SCD or CMS. This generalization is realized by introducing a new speed-dependent angle averaging operator. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN, in the frames of SAFRAN contest which took place at the Surveillance 8 International Conference.

Experimental Investigation of Time-Frequency Characteristics of Pressure Fluctuations in a Double-Suction Centrifugal Pump

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Zhifeng Yao ◽  
Fujun Wang ◽  
Lixia Qu ◽  
Ruofu Xiao ◽  
Chenglian He ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Flow Rate ◽  
Pressure Fluctuation ◽  
Pressure Fluctuations ◽  
Operating Conditions ◽  
Design Flow ◽  
Flow Rates ◽  
Time Frequency ◽  
Blade Passing Frequency ◽  
Design Flow Rate

Pressure fluctuation is the primary reason for unstable operations of double-suction centrifugal pumps. By using flush mounted pressure transducers in the semispiral suction chamber and the volute casing of a double-suction pump, the pressure fluctuation signals were obtained and recorded at various operating conditions. Spectral analyses were performed on the pressure fluctuation signals in both frequency domain and time-frequency domain based on fast Fourier transform (FFT) and an adaptive optimal-kernel time-frequency representation (AOK TFR). The results show that pressure fluctuations at the impeller rotating frequency and some lower frequencies dominated in the semispiral suction chamber. Pressure fluctuations at the blade passing frequency, the impeller rotating frequency, and their harmonic frequencies were identified in the volute casing. The amplitude of pressure fluctuation at the blade passing frequency significantly increased when the flow rate deviated from the design flow rate. At 107% of the design flow rate, the amplitude increased more than 254% than that at the design flow rate. The time-frequency characteristics of these pressure fluctuations were affected greatly by both operating conditions and measurement locations. At partial flow rates the pulsation had a great irregularity and the amplitudes at the investigated frequencies were much larger than ones at the design flow rate. An asymmetrical pressure fluctuation structure in the volute casing was observed at all flow rates. The pulsation behavior at the blade passing frequency was the most prominent near the volute tongue zone, and the pressure waves propagated in both the radial and circumferential directions.

scholarly journals Chaotic Characteristic Analysis of Vibration Response of Pumping Station Pipeline Using Improved Variational Mode Decomposition Method

2021 ◽  
Vol 11 (19) ◽  
pp. 8864
Author(s):  
Li Jiang ◽  
Zhenyue Ma ◽  
Jianwei Zhang ◽  
Mohd Yawar Ali Khan ◽  
Mengran Cheng ◽  
...  
Keyword(s):  
Vibration Response ◽  
Operating Conditions ◽  
Maximum Lyapunov Exponent ◽  
Variational Mode Decomposition ◽  
Measuring Point ◽  
Pumping Station ◽  
Vibration Excitation ◽  
Mode Decomposition ◽  
Vibration Responses ◽  
Pipeline Vibration

The measured vibrational responses of the pumping station pipeline in the irrigation site were chosen to confirm the chaotic characteristics of the pumping station pipeline vibration and to determine the vibrational excitation that makes it chaotic. First, the chaotic properties of the pipeline vibration responses were investigated using a saturation correlation dimension and the maximum Lyapunov exponent. The vibration excitation with chaotic features was obtained using an improved variational mode decomposition (IVMD) method to examine the multi-time-scale chaotic characteristics of the pipeline vibration responses. The results show that the vibrational responses of each measuring point of the pipeline under different operating conditions have clear chaotic characteristics, where the chaotic characteristics of the axial points and bifurcated pipe points are relatively strong. The vibration of the operating conditions and measurement points affected by the unit’s operation and flow state change is further complicated. The intrinsic mode function (IMF) produces a low-dimensional chaotic attractor after the IVMD disrupts the vibration response. Still, the vibration excitation of the remaining components on behalf of the units does not have chaotic properties, implying that water pulsation excitation makes the pumping station pipeline vibrations chaotic. The vibration excitation caused by the unit’s operation covers the chaotic characteristics of the pipeline vibration and increases its uncertainty. The outcomes of this study provide a theoretical basis for further exploration of the vibration characteristics of pumping station pipelines, and a new method of chaos analysis is proposed.

Analysis of Time, Frequency and Wavelet Based Features of Vibration and Current Signals for Fault Diagnosis of Induction Motors Using SVM

2017 ◽  
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Induction Motors ◽  
Classification Performance ◽  
Operating Conditions ◽  
Support Vector ◽  
Time Frequency ◽  
Mechanical Faults ◽  
Severity Levels ◽  
Electrical Faults

This paper presents a comparative analysis of the time, frequency and time-frequency domain based features of the vibration and current signals for identifying various faults in induction motors (IMs) using support vector machine (SVM). Four mechanical faults (bearing fault, unbalanced rotor, bowed rotor and misaligned rotor), and three electrical faults (broken rotor bars, stator winding fault with two severity levels and phase unbalance with two severity levels) are considered in the present study. The proposed fault diagnosis consists of three steps. In the first step, the vibration in three orthogonal directions and the current in three phases are acquired from the healthy and faulty motors using a machine fault simulator (MFS). In second step, useful statistical features are extracted from the time, frequency and time-frequency domain (continuous wavelet transform (CWT)) of the signal. For the effective fault diagnosis, SVM parameters are optimally selected based on the grid-search method along with 5-fold cross-validation, and the effective fault features are selected based on the wrapper model. Finally, the fault diagnosis of IM is performed using optimal SVM parameters and effective features as input to the SVM. The classification performance of all methodologies developed in three domains is compared for various operating conditions of IMs. The test results showed that the developed methodology could isolate ten IM fault conditions successfully based on features from all three domains at all IM operating conditions; however, time-frequency features give the best results.

Wavelet-Based Modal Parameter Identification through Measurements for Damage Detection

2007 ◽  
Vol 353-358 ◽  
pp. 1195-1198 ◽  
Author(s):  
Y.B. Chen ◽  
J.G. Han ◽  
D.Q. Yang
Keyword(s):  
Modal Analysis ◽  
Frequency Domain ◽  
Operating Conditions ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Time Frequency ◽  
Stochastic Subspace Identification ◽  
Damping Coefficients ◽  
Identification Technique

Structural operating conditions may significantly differ from those applied during laboratory tests where the structure is well known, well installed and properly excited. For structures under their natural loading conditions, or excited by random forces, excitations cannot be measured and are usually non stationary. Hence, an improvement operational modal analysis is a useful complement to the traditional modal analysis approach. The aim of this paper is to present the application of a new identification procedure, named wavelet-based identification technique of structural modal parameters. Wavelet-based identification that works in time-frequency domain is used to identify the dynamic characteristics of the structural system in terms of natural frequencies, damping coefficients and mode shapes. The paper has shown how the amplitude and the phase of the wavelet transform of operational vibration measurements are related to eigenfrequencies and damping coefficients, and the wavelet-based spectrum analysis is used to identify the mode shapes of the structure. Those modal parameters can be used to detect damage of structures. A simulation example has demonstrated that current identified results are comparable with those previously obtained from the peak pick method in frequency domain and stochastic subspace identification in time domain.

Detection of inappropriate working conditions for the timing belt in internal-combustion engines using vibration signals and data mining

2016 ◽  
Vol 231 (3) ◽  
pp. 418-432 ◽  
Author(s):  
Meghdad Khazaee ◽  
Ahmad Banakar ◽  
Barat Ghobadian ◽  
Mostafa Agha Mirsalim ◽  
Saeid Minaei ◽  
...  
Keyword(s):  
High Temperature ◽  
Frequency Domain ◽  
High Load ◽  
Operating Conditions ◽  
Transform Method ◽  
Time Frequency ◽  
Normal Operating ◽  
Timing Belt ◽  
Temperature Operating ◽  
Abnormal Operating Conditions

Abnormal operating conditions for the timing belt can lead to cracks, fatigue, sudden rupture and damage to engines. In this study, an intelligent system was developed to detect and classify high-load operating conditions and high-temperature operating conditions for timing belts. To achieve this, vibration signals in normal operating conditions, high-load operating conditions and high-temperature operating conditions were collected. Time-domain signals were transformed to the frequency domain and the time–frequency domain using the fast Fourier transform method and the wavelet transform method respectively. In the data-mining stage, 25 statistical features were extracted from different signal domains. The improved distance evaluation method was adopted to select the best features and to reduce the input space for the classifier. Then, the signal features from the time domain, the frequency domain and the time-frequency domain were fed into an artificial neural network to evaluate the accuracy of this designed procedure for detecting inappropriate operating conditions for the timing belt. Based on all these features extracted from the signals in the time, frequency and time–frequency domains, the artificial neural network classifier detected and classified normal operating conditions, high-load operating conditions and high-temperature operating conditions with accuracies of 73.3%, 85% and 89.2% respectively. The classification accuracies using features selected by improved distance evaluation in the signals from the time, frequency and time–frequency domains were found to be 85%, 95.8% and 95% respectively. The results showed that the developed system was capable of detecting and classifying both the normal operating conditions and abnormal operating conditions for the timing belt. The results also suggested that a combination of signal processing and feature selection can significantly enhance the classification accuracy.

scholarly journals Application of Cyclo-Nonstationary Indicators for Bearing Monitoring Under Varying Operating Conditions

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Konstantinos Gryllias ◽  
Simona Moschini ◽  
Jerome Antoni
Keyword(s):  
Frequency Domain ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Aircraft Engine ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Diagnostic Features ◽  
Time Frequency ◽  
Acceleration Signal

Condition monitoring assesses the operational health of rotating machinery, in order to provide early and accurate warning of potential failures such that preventative maintenance actions may be taken. To achieve this target, manufacturers start taking on the responsibilities of engine condition monitoring, by embedding health-monitoring systems within each engine unit and prompting maintenance actions when necessary. Several types of condition monitoring are used including oil debris monitoring, temperature monitoring, and vibration monitoring. Among them, vibration monitoring is the most widely used technique. Machine vibro-acoustic signatures contain pivotal information about its state of health. The current work focuses on one part of the diagnosis stage of condition monitoring for engine bearing health monitoring as bearings are critical components in rotating machinery. A plethora of signal processing tools and methods applied at the time domain, the frequency domain, the time–frequency domain, and the time-scale domain have been presented in order to extract valuable information by proposing different diagnostic features. Among others, an emerging interest has been reported on modeling rotating machinery signals as cyclo-stationary, which is a particular class of nonstationary stochastic processes. The goal of this paper is to propose a novel approach for the analysis of cyclo-nonstationary signals based on the generalization of indicators of cyclo-stationarity (ICNS) in order to cover the speed-varying conditions. The effectiveness of the approach is evaluated on an acceleration signal captured on the casing of an aircraft engine gearbox, provided by SAFRAN.

scholarly journals Analysis of S Characteristics and Pressure Pulsations in a Pump-Turbine With Misaligned Guide Vanes

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Hui Sun ◽  
Ruofu Xiao ◽  
Weichao Liu ◽  
Fujun Wang
Keyword(s):  
Water Pressure ◽  
Renewable Energy Sources ◽  
Pressure Fluctuations ◽  
Low Flow ◽  
Experimental Investigations ◽  
Stable Operation ◽  
Pressure Pulsations ◽  
Pump Turbine ◽  
Guide Vanes ◽  
Time Frequency

Growing environmental concerns and the need for better power balancing and frequency control have increased attention in renewable energy sources such as the reversible pump-turbine which can provide both power generation and energy storage. Pump-turbine operation along the S-shaped curve can lead to difficulties in loading the rejection process with unusual increases in water pressure, which lead to machine vibrations. Pressure fluctuations are the primary reason for unstable operation of pump-turbines. Misaligned guide vanes (MGVs) are widely used to control the stability in the S region. There have been experimental investigations and computational fluid dynamics (CFD) simulations of scale models with aligned guide vanes and MGVs with spectral analyses of the S curve characteristics and the pressure pulsations in the frequency and time-frequency domains at runaway conditions. The course of the S characteristic is related to the centrifugal force and the large incident angle at low flow conditions with large vortices forming between the guide vanes and the blade inlets and strong flow recirculation inside the vaneless space as the main factors that lead to the S-shaped characteristics. Preopening some of the guide vanes enables the pump-turbine to avoid the influence of the S characteristic. However, the increase of the flow during runaway destroys the flow symmetry in the runner leading to all asymmetry forces on the runner that leads to hydraulic system oscillations. The MGV technique also increases the pressure fluctuations in the draft tube and has a negative impact on stable operation of the unit.

Sign in / Sign up

Export Citation Format

Share Document