scholarly journals Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1868
Author(s):  
Yuanyuan Huang ◽  
Shuiguang Tong ◽  
Zheming Tong ◽  
Feiyun Cong
Keyword(s):  
Genetic Algorithm ◽  
Online Monitoring ◽  
Mechanical Systems ◽  
Vibration Signal ◽  
Experimental Results ◽  
Ar Model ◽  
Signal Identification ◽  
Vibration Signals ◽  
Sparse Decomposition ◽  
Auto Regression

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences.

Compound fault diagnosis of rolling element bearings using multipoint sparsity–multipoint optimal minimum entropy deconvolution adjustment and adaptive resonance-based signal sparse decomposition

2020 ◽  
pp. 107754632093819
Author(s):  
Ji Fan ◽  
Yongsheng Qi ◽  
Xuejin Gao ◽  
Yongting Li ◽  
Lin Wang
Keyword(s):  
Search Algorithm ◽  
Harmonic Signal ◽  
Vibration Signal ◽  
Cuckoo Search ◽  
Permutation Entropy ◽  
Rolling Element Bearings ◽  
Minimum Entropy ◽  
Vibration Signals ◽  
Sparse Decomposition ◽  
Rolling Element

The rolling element bearings used in rotating machinery generally include multiple coexisting defects. However, individual defect–induced signals of bearings simultaneously arising from multiple defects are difficult to extract from measured vibration signals because the impulse-like fault signals are very weak, and the vibration signal is commonly affected by the transmission path and various sources of interference. This issue is addressed in this study by proposing a new compound fault feature extraction scheme. Vibration signals are first preprocessed using resonance-based signal sparse decomposition to obtain the low-resonance component of the signal, which contains the information related to the transient fault–induced impulse signals, and reduce the interference of discrete harmonic signal components and noise. The objective used for adaptively selecting the optimal resonance-based signal sparse decomposition parameters adopts the ratio of permutation entropy to the frequency domain kurtosis, as a new comprehensive index, and the optimization is conducted using the cuckoo search algorithm. Subsequently, we apply multipoint sparsity to the low-resonance component to automatically determine the possible number of impulse signals and their periods according to the peak multipoint sparsity values. This enables the targeted extraction and isolation of fault-induced impulse signal features by multipoint optimal minimum entropy deconvolution adjustment. Finally, the envelope spectrum of the filtered signal is used to identify the individual faults. The effectiveness of the proposed scheme is verified by its application to both simulated and experimental compound bearing fault vibration signals with strong interference, and its advantages are confirmed by comparisons of the results with those of an existing state-of-the-art method.

scholarly journals Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Sun-Hee Kim ◽  
Christos Faloutsos ◽  
Hyung-Jeong Yang
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Regression Model ◽  
Random Coefficients ◽  
Experimental Results ◽  
Ar Model ◽  
Forecasting Method ◽  
Eeg Data ◽  
Future Value ◽  
Auto Regression

Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity. Therefore, it is important to find a suitable forecasting method that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique of random coefficients, which forcefully adjusts the coefficients with−1and 1. The fractal dimension is used to determine the order of the CA-AR model. We applied the CA-AR method reflecting special characteristics of data to forecast the future value of epilepsy EEG data. Experimental results show that when compared to previous methods, the proposed method can forecast faster and accurately.

Gearbox Condition Estimation Using Cyclo-Stationary Properties of Vibration Signal

2009 ◽  
Vol 413-414 ◽  
pp. 471-478 ◽  
Author(s):  
Radoslaw Zimroz ◽  
Walter Bartelmus
Keyword(s):  
Classical Method ◽  
Mechanical Systems ◽  
Mining Industry ◽  
Vibration Signal ◽  
Normal Operation ◽  
Diagnostic Feature ◽  
Spectral Correlation ◽  
Vibration Signals ◽  
Multi Stage ◽  
Complex Mechanical Systems

The paper explores the cyclo-stationary properties of vibration signals for estimation of gearbox condition. The advantage of such approach may be clearly seen especially for so called multi-faults problem, i.e. for more than one faults that occurred in the system. In complex mechanical systems like multistage gearboxes, such situation may be often seen. Although this approach becomes more and more popular, it has been noticed that there is difficult to find examples highlighting its potential, especially for real industrial situations. In order to fill partially the gap, the paper deals with the multi fault detection in complex mechanical systems like multi-stage gearboxes: fixed axis and planetary. It has been discussed that during the operation in such machines many faults may appear simultaneously and the classical method like envelope analysis is difficult to use. The paper presents the use of cyclo-stationary properties of signals to identify and characterize sources of modulation. From Spectral Correlation Density Map or more precisely Spectral Coherence Map have been observed the number of sources with different properties of modulation. It is shown that the number of harmonics is important for a kind of fault extraction and interpretation. This approach has been applied to two, three and five stage gearboxes used in mining industry. Vibration signals received in industrial environment during normal operation of objects are considered. It has been also proposed the simple diagnostic feature to estimate the changes of condition with application to a planetary stage in a 5-stage gearbox.

Application of Wavelet Packet Energy Spectrum in Coal-Rock Interface Recognition

2011 ◽  
Vol 474-476 ◽  
pp. 1103-1106 ◽  
Author(s):  
Bao Ping Wang ◽  
Zeng Cai Wang ◽  
Yun Xia Li
Keyword(s):  
Energy Spectrum ◽  
High Frequency ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Experimental Results ◽  
Hydraulic Support ◽  
Vibration Signals ◽  
Feature Based ◽  
Rock Interface ◽  
Coal Rock

The recognition of coal-rock interface in the top caving was investigated via the vibration signals of the tail beam of the hydraulic support. Wavelet packet transform was used to process the vibration signals. A newly feature based on wavelet packet energy spectrum was proposed to identify the coal-rock interface in top coal caving. The interface was determined by comparing the features in the two cases of coal dropping and rock dropping. The experimental results show that the energy proportion of vibration signal in high frequency bands when rock dropping is greater than that when coal dropping.

Fault Diagnosis of Elevator Based AR Bi-Spectrum

2012 ◽  
Vol 468-471 ◽  
pp. 1743-1748
Author(s):  
Jing Yu Yi ◽  
Yi Jian Huang
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Gaussian White Noise ◽  
Vibration Signal ◽  
Ar Model ◽  
Vibration Signals ◽  
Working Status ◽  
Non Gaussian ◽  
Analysis Of Time Series

According to the characteristics of the elevator fault vibration signals, proposing a Based on analysis of time series AR bi-spectrum elevator fault diagnosis. When the zero-mean, non-Gaussian white noise elevator device, the vibration signal using sampling to establish time series autoregressive model (AR model), resulting in AR bi-spectrum. Bi-spectrum signal processing is a new, powerful signal processing technology, which can be described the nonlinear coupling, suppression Gaussian noise and retention of phase information, you can get the elevator working status of the different dynamic characteristics. The results show that bi-spectral analysis with AR elevator failure is feasible and effective.

scholarly journals Rolling Bearing Fault Diagnosis Based on CEEMD and Time Series Modeling

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Time Series ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Test System ◽  
Ar Model ◽  
Model Parameters ◽  
Time Series Modeling ◽  
Intrinsic Mode Functions ◽  
Vibration Signals

Accurately identifying faults in rolling bearing systems by analyzing vibration signals, which are often nonstationary, is challenging. To address this issue, a new approach based on complementary ensemble empirical mode decomposition (CEEMD) and time series modeling is proposed in this paper. This approach seeks to identify faults appearing in a rolling bearing system using proper autoregressive (AR) model established from the nonstationary vibration signal. First, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of intrinsic mode functions (IMFs) by means of the CEEMD method. Second, vibration signals are filtered with calculated filtering parameters. Third, the IMF which is closely correlated to the filtered signal is selected according to the correlation coefficient between the filtered signal and each IMF, and then the AR model of the selected IMF is established. Subsequently, the AR model parameters are considered as the input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of a rolling bearing. Experimental study performed on a bearing test system has shown that the presented approach can accurately identify faults in rolling bearings.

Wind Speed Forecasting Using a Combined Method Based on Auto Regression and Wavelet Transform

2012 ◽  
Vol 512-515 ◽  
pp. 803-808
Author(s):  
Ji Long Tong ◽  
Zeng Bao Zhao ◽  
Wen Yu Zhang
Keyword(s):  
Wind Speed ◽  
Original Data ◽  
Ar Model ◽  
Wind Speed Forecasting ◽  
Wind Speed Prediction ◽  
Prediction Time ◽  
New Strategy ◽  
Speed Prediction ◽  
Auto Regression

This paper presents a new strategy in wind speed prediction based on AR model and wavelet transform.The model uses the adjacent data for short-term wind speed forecasting and the data of the same moment in earlier days for long-term wind speed prediction at that moment,taking the similarity of wind speed at the same moment every day into account.Using the new model to analyze the wind speed of An-xi,China in April,2010,this paper concludes that the model is effective for that the correlation coefficient between the predicted value and the original data is larger than 0.8 when the prediction is less than 48 hours;while the prediction time is long ahead (48-120h),the error is acceptable (within 40%),which demonstrates that the new method is a novel and good idea for prediction on wind speed.

Online Monitoring Recognition Theory Based on the Time Series of Chatter

2013 ◽  
Vol 819 ◽  
pp. 160-164
Author(s):  
Yong Xiang Jiang ◽  
Bing Du ◽  
Pan Zhang ◽  
San Peng Deng ◽  
Yu Ming Qi
Keyword(s):  
Time Series ◽  
Chaotic Dynamics ◽  
Online Monitoring ◽  
Vibration Signal ◽  
Threshold Value ◽  
Coarse Grained ◽  
Attractor Dimension ◽  
Machining Chatter ◽  
Recognition Theory ◽  
On Line

On-line monitoring recognition for machining chatter is one of the key technologies in manufacturing. Based on the nonlinear chaotic control theory, the vibration signal discrete time series for on-line monitoring indicator is studed. As in chatter the chaotic dynamics process attractor dimension is reduced, the KolmogorovSinai entropy (K-S) index is extracted to reflected the regularity of workpiece chatter, then the k-S entropy is simplified by coarse - grained entropy rate (CER), which can easily evaluated as chatter online monitoring threshold value. The milling test shows that the CER have a sharp decline when chatter occurre, and can quickly and accurately forecast chatter.

Application of Immune Genetic Algorithm in Survey Data Processing

2012 ◽  
Vol 241-244 ◽  
pp. 1737-1740
Author(s):  
Wei Chen
Keyword(s):  
Genetic Algorithm ◽  
Immune System ◽  
Data Processing ◽  
Survey Data ◽  
Heuristic Algorithm ◽  
Experimental Results ◽  
Immune Genetic Algorithm ◽  
Basic Principles ◽  
Immune Operator ◽  
Biological Immune System

The immune genetic algorithm is a kind of heuristic algorithm which simulates the biological immune system and introduces the genetic operator to its immune operator. Conquering the inherent defects of genetic algorithm that the convergence direction can not be easily controlled so as to result in the prematureness；it is characterized by a better global search and memory ability. The basic principles and solving steps of the immune genetic algorithm are briefly introduced in this paper. The immune genetic algorithm is applied to the survey data processing and experimental results show that this method can be practicably and effectively applied to the survey data processing.

scholarly journals Learning to See the Vibration: A Neural Network for Vibration Frequency Prediction

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2530 ◽  
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang
Keyword(s):  
Neural Network ◽  
Vibration Frequency ◽  
Frequency Estimation ◽  
Vibration Signal ◽  
Frequency Measurement ◽  
Optical Methods ◽  
Vibration Measurement ◽  
Vibration Signals ◽  
Engineering Practices ◽  
To Receive

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrate that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

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