Fault Analysis of a Dust-Removing Blower in a Sintering Plant Based on Envelope Analysis

2015 ◽  
Vol 779 ◽  
pp. 145-150
Author(s):  
Zi Wang ◽  
Yu Dong Yang ◽  
Jing Liu ◽  
Xiao Ping Qu ◽  
Yan Yang Zhou
Keyword(s):  
Hilbert Transform ◽  
Vibration Signal ◽  
Fault Analysis ◽  
Envelope Analysis ◽  
Fault Features ◽  
Diagnosis Method ◽  
The Hilbert Transform ◽  
Very High ◽  
Dynamic Unbalance ◽  
Sintering Plant

Dust-removing blower is a key equipment in sintering plants, which can provide enough wind and negative pressure. It can also improve the efficiency of dust-removing. The vibration level of a dust-removing blower in a sintering plant is very high, which is beyond its normal value. Due to the complex working condition and strong background noise, it is difficult to extract fault features from the vibration signal of the dust-removing blower. Therefore, fault analysis of the blower is very difficult. Since the modulation phenomenon existed in the vibration signal of the blower is found, the envelope analysis based on the Hilbert transform is proposed to demodulate the vibration signal. The frequency spectrum of the demodulated signal shows that the first order frequency characteristic is obvious, which can effectively reveal the dynamic unbalance of the rotor system is the main reason of the abnormal vibration of the blower. According to this diagnosis, some possible reasons for the unbalance are proposed, as well as advices regarding to the repair of the blower system. Moreover, the test and analysis are conducted on the repaired blower system. The results show that the unbalance problem is eliminated and the blower can work normally, which can validate the accuracy and reliability of the proposed diagnosis method for fault analysis of the dust-removing blower.Keywords: dynamic unbalance; modulation; dust-removing blower; Hilbert Transform

scholarly journals Instantaneous Amplitude-Frequency Feature Extraction for Rotor Fault Based on BEMD and Hilbert Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Chuanjin Huang ◽  
Haijun Song ◽  
Wenping Lei ◽  
Zhanya Niu ◽  
Yajun Meng
Keyword(s):  
Feature Extraction ◽  
Hilbert Transform ◽  
Vibration Signal ◽  
Three Dimensions ◽  
Intrinsic Mode Functions ◽  
Instantaneous Amplitude ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Fault Features ◽  
The Hilbert Transform

The vibration signals propagating in different directions from rotating machines can contain a variety of characteristic information. A novel feature extraction method based on bivariate empirical mode decomposition (BEMD) for rotor is proposed to comprehensively extract the fault features. In this work, the number of signal projection directions is determined through simulation, and the energy end condition based on the energy threshold is increased using BEMD to enhance the decomposition quality. Mixed vibration signals are generated along two orthogonal directions. Then, the acquired vibration signal can be decomposed into several intrinsic mode functions (IMFs) at the rotational speed using the BEMD method. Furthermore, the instantaneous frequency and instantaneous amplitude of the real signals and the imaginary part of the IMF signals are obtained using the Hilbert transform. The fault features along two and three dimensions can be investigated, providing more comprehensive information to aid in the fault diagnosis of rotor. Experimental results on oil film oscillation, the oil whirl, the bistability of the rotor, and looseness and rotor rubbing composite fault indicate the effectiveness of the proposed method.

Failure Analysis of a High Pressure Descaling Pump Using Envelope Analysis

2011 ◽  
Author(s):  
Yimin Shao ◽  
Wennian Yu ◽  
Qing Chen ◽  
Huifang Xiao ◽  
Xiangzhi Yu
Keyword(s):  
High Pressure ◽  
Vibration Signal ◽  
The Other ◽  
Maintenance Cost ◽  
Pump System ◽  
Envelope Analysis ◽  
Vibration Data ◽  
Alignment Technique ◽  
The Hilbert Transform ◽  
Hot Rolled

A high pressure descaling pump was used to remove scale in a hot rolled band furnace. This pump was the key piece of equipment in this process which maintained the surface quality of the hot rolled band steel. Over a period of three years one pump continued to work normally, but the other pump vibrated vigorously. The motor, pump and the other system equipment were changed repeatedly, but the abnormal vibration was not eliminated. Vibration data from the two pumps was obtained, the modulation phenomenon existed in the vibration signal caused by the gear coupling misalignment was found, thus the envelope analysis based on the Hilbert transform was presented to demodulate the vibration signal. The frequency spectrum of the demodulated signal showed that the second order frequency characteristic was more obvious, which effectively revealed the fault information related with the gear coupling misalignment. It was found that the abnormal vibration was caused by coupling misalignment between the motor and the pump. After applying a more advanced alignment technique to thoroughly eliminate the misalignment in the coupling, the vibration was considerably reduced and the pump could work normally. This convincingly verified our analysis results and would dramatically reduce the ongoing maintenance cost for the descaling pump system.

scholarly journals An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1041 ◽  
Author(s):  
Yang Liu ◽  
Lixiang Duan ◽  
Zhuang Yuan ◽  
Ning Wang ◽  
Jianping Zhao
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Vibration Signal ◽  
Intelligent Fault Diagnosis ◽  
Traditional Methods ◽  
Learning Abilities ◽  
Fault Features ◽  
Correlation Criterion ◽  
Long Time ◽  
Diagnosis Method

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

scholarly journals Application of a Novel Adaptive Med Fault Diagnosis Method in Gearboxes

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1106
Author(s):  
Wenhua Du ◽  
Xiaoming Guo ◽  
Xiaofeng Han ◽  
Junyuan Wang ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Fitness Function ◽  
Vibration Signal ◽  
Ar Model ◽  
Fluctuation Analysis ◽  
Minimum Entropy ◽  
Fault Features ◽  
Filter Size ◽  
Diagnosis Method ◽  
Reduction Effect

Minimum entropy deconvolution (MED) is not effective in extracting fault features in strong noise environments, which can easily lead to misdiagnosis. Moreover, the noise reduction effect of MED is affected by the size of the filter. In the face of different vibration signals, the size of the filter is not adaptive. In order to improve the efficiency of MED fault feature extraction, this paper proposes a firefly optimization algorithm (FA) to improve the MED fault diagnosis method. Firstly, the original vibration signal is stratified by white noise-assisted singular spectral decomposition (SSD), and the stratified signal components are divided into residual signal components and noisy signal components by a detrended fluctuation analysis (DFA) algorithm. Then, the noisy components are preprocessed by an autoregressive (AR) model. Secondly, the envelope spectral entropy is proposed as the fitness function of the FA algorithm, and the filter size of MED is optimized by the FA algorithm. Finally, the preprocessed signal is denoised and the pulse enhanced with the proposed adaptive MED. The new method is validated by simulation experiments and practical engineering cases. The application results show that this method improves the shortcomings of MED and can extract fault features more effectively than the traditional MED method.

scholarly journals A Bearing Fault Diagnosis Method Based on PAVME and MEDE

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1402
Author(s):  
Xiaoan Yan ◽  
Yadong Xu ◽  
Daoming She ◽  
Wan Zhang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Whale Optimization ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
Mode Extraction ◽  
Parameter Adaptive

When rolling bearings have a local fault, the real bearing vibration signal related to the local fault is characterized by the properties of nonlinear and nonstationary. To extract the useful fault features from the collected nonlinear and nonstationary bearing vibration signals and improve diagnostic accuracy, this paper proposes a new bearing fault diagnosis method based on parameter adaptive variational mode extraction (PAVME) and multiscale envelope dispersion entropy (MEDE). Firstly, a new method hailed as parameter adaptive variational mode extraction (PAVME) is presented to process the collected original bearing vibration signal and obtain the frequency components related to bearing faults, where its two important parameters (i.e., the penalty factor and mode center-frequency) are automatically determined by whale optimization algorithm. Subsequently, based on the processed bearing vibration signal, an effective complexity evaluation approach named multiscale envelope dispersion entropy (MEDE) is calculated for conducting bearing fault feature extraction. Finally, the extracted fault features are fed into the k-nearest neighbor (KNN) to automatically identify different health conditions of rolling bearing. Case studies and contrastive analysis are performed to validate the effectiveness and superiority of the proposed method. Experimental results show that the proposed method can not only effectively extract bearing fault features, but also obtain a high identification accuracy for bearing fault patterns under single or variable speed.

Experimental damping ratio evaluation using Hilbert transform in filament-wound composite plates

2021 ◽  
pp. 096739112110020
Author(s):  
Enzo Costamilan ◽  
Alexandre Marks Löw ◽  
Marcos Daniel de Freitas Awruch ◽  
Sandro C Amico ◽  
Herbert Martins Gomes
Keyword(s):  
Hilbert Transform ◽  
Damping Ratio ◽  
Composite Plates ◽  
Vibration Signal ◽  
Element Model ◽  
Filament Winding ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Filament Wound Composite ◽  
The Hilbert Transform

The aim of this work is the evaluation of damping ratio in composite materials with orthogonal fiber orientation based on experimental and numerical techniques. In this study, the logarithmic decrement and the envelope techniques calculated using Hilbert transform are used. Carbon fiber/epoxy composites manufactured by filament winding are dynamically tested in free vibration. Post-processing and data analysis are performed with the developed codes. These comprise the use of a band-pass filter to isolate the first fundamental frequency from the other modes of vibration and noise present in the acquired signal. Then, the Hilbert transform is used to estimate the envelope of the vibration signal and the exponential curve is adjusted to obtain the envelope, in order to evaluate the structural damping ratio. Comparisons with a fitted finite element model are used for validation. The results revealed that damping varied proportionally with the number of layers, the ply orientation and, less evidently, with the length of the samples.

scholarly journals Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 593 ◽  
Author(s):  
Guiji Tang ◽  
Bin Pang ◽  
Yuling He ◽  
Tian Tian
Keyword(s):  
Fault Diagnosis ◽  
Energy Density ◽  
Dynamic Time Warping ◽  
Vibration Signal ◽  
Time Warping ◽  
Efficient Operation ◽  
Fault Features ◽  
Diagnosis Method ◽  
Dynamic Time ◽  
Instantaneous Energy

The accurate fault diagnosis of gearboxes is of great significance for ensuring safe and efficient operation of rotating machinery. This paper develops a novel fault diagnosis method based on hierarchical instantaneous energy density dispersion entropy (HIEDDE) and dynamic time warping (DTW). Specifically, the instantaneous energy density (IED) analysis based on singular spectrum decomposition (SSD) and Hilbert transform (HT) is first applied to the vibration signal of gearbox to acquire the IED signal, which is designed to reinforce the fault feature of the signal. Then, the hierarchical dispersion entropy (HDE) algorithm developed in this paper is used to quantify the complexity of the IED signal to obtain the HIEDDE as fault features. Finally, the DTW algorithm is employed to recognize the fault types automatically. The validity of the two parts that make up the HIEDDE algorithm, i.e., the IED analysis for fault features enhancement and the HDE algorithm for quantifying the information of signals, is numerically verified. The proposed method recognizes the fault patterns of the experimental data of gearbox accurately and exhibits advantages over the existing methods such as multi-scale dispersion entropy (MDE) and refined composite MDE (RCMDE).

The Demodulating Principle of Autocorrelation-Envelope and its Application to Gearbox Diagnosis

2013 ◽  
Vol 380-384 ◽  
pp. 1029-1034 ◽  
Author(s):  
Zhan Dong Bi ◽  
Yong Chen ◽  
Zhi Zhao Peng ◽  
Yu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Modulation Frequency ◽  
Vibration Signal ◽  
Health Condition ◽  
Vibration Signals ◽  
Fault Features ◽  
Diagnosis Method ◽  
Simulation Results ◽  
Modulated Signals ◽  
Periodic Components

As the most important transmission system of vehicles, the gearbox has a high fault rate, so it is meaningful to evaluate and diagnose its health condition and faults accurately. Autocorrelation -envelope analysis is a fault diagnosis method that can suppress the noise and reserve the periodic components of vibration signals. A conclusion has been deduced: amplitude modulated, frequency modulated, or amplitude& frequency modulated signals can be transformed into amplitude modulated signals with the same modulation frequency through autocorrelation processing. Therefore, the aucorrelation-envelope technique is suitable for extracting the fault features of gearbox from its vibration signal with the coexistence of amplitude modulation and frequency modulation. The simulation results verify the validity of the conclusion and the experiment of vehicle gearbox diagnosis indicates the effectiveness of this method.

Sign in / Sign up

Export Citation Format

Share Document