scholarly journals Faults Detection and Classification under Variable Condition Using Intrinsic Time - Scale Decomposition and Neural Network

2021 ◽  
Vol 54 (5) ◽  
pp. 777-782
Author(s):  
Saidani Djama Leddine ◽  
Rahmoune Chamceddine ◽  
Zenasni Ramdane
Keyword(s):  
Time Scale ◽  
Fault Identification ◽  
Multilayer Network ◽  
Vibration Signals ◽  
New Approach ◽  
Intrinsic Time ◽  
System A ◽  
Average Accuracy ◽  
Time Scale Decomposition ◽  
Variable Condition

Misalignment and unbalance are a common fault occurring in the rotor system. A new approach for detecting misalignment and unbalance problems combining the intrinsic time - scale decomposition (ITD), the root mean square (RMS) and perceptron multilayer network (MLP) is proposed in this paper. Vibration signals of normal condition, misalignment horizontal, misalignment vertical and unbalance with different level are collected under different speed. ITD, nonlinear analysis of signals, was applied to decompose the vibration signals into 8 proper rotation components. The RMS values of 8 components are calculated and using as features vector. Last, the perceptron multilayer network was used for fault identification and classification. The proposed approach accurately classified and detection of unbalance and misalignment; the average accuracy achieved is 97.99%.

scholarly journals An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 617
Author(s):  
Jianpeng Ma ◽  
Shi Zhuo ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guangzhu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Time Scale ◽  
Lévy Noise ◽  
Model Parameters ◽  
Rolling Bearings ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Weak Fault ◽  
Levy Noise ◽  
The Impact

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

A novel intrinsic time–scale decomposition–graph signal processing–based characteristic extraction method for rotor–stator rubbing of aeroengine

2021 ◽  
pp. 107754632098596
Author(s):  
Mingyue Yu
Keyword(s):  
Signal Processing ◽  
Autocorrelation Function ◽  
Time Scale ◽  
Energy Index ◽  
Signal Processing Algorithm ◽  
Intrinsic Time ◽  
Graph Signal Processing ◽  
Laplacian Energy ◽  
Rotational Components ◽  
Time Scale Decomposition

Intrinsic time-scale decomposition and graph signal processing are combined to effectively identify a rotor–stator rubbing fault. The vibration signal is decomposed into mutually independent rotational components, and then, the Laplacian energy index is obtained by the graph signal of the autocorrelation function of rotational components, and the signal is reconstructed by an autocorrelation function of each proper rotation (PR) component relative to smaller Laplacian energy index (less complexity). Finally, characteristics are extracted from rotor–stator rubbing faults in an aeroengine according to square demodulation spectrum of a reconstructed signal. To validate the effectiveness of the algorithm, a comparative analysis is made among traditional intrinsic time-scale decomposition algorithm, combination of intrinsic time-scale decomposition and autocorrelation function, and the proposed intrinsic time-scale decomposition–graph signal processing algorithm. Comparative result shows that the proposed intrinsic time-scale decomposition–graph signal processing algorithm is more precise and effective than the traditional intrinsic time-scale decomposition and intrinsic time-scale decomposition and autocorrelation function algorithms in extracting characteristic frequency and frequency multiplication of rotor–stator rubbing faults and can greatly reduce the number of noise components irrelevant to faults.

scholarly journals Defining a trend for time series using the intrinsic time-scale decomposition

2014 ◽  
Vol 16 (8) ◽  
pp. 085004 ◽  
Author(s):  
Juan M Restrepo ◽  
Shankar Venkataramani ◽  
Darin Comeau ◽  
Hermann Flaschka
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Intrinsic Time ◽  
Time Scale Decomposition

Adaptive carrier fringe pattern enhancement for wavelet transform profilometry through modifying intrinsic time-scale decomposition

2020 ◽  
Vol 59 (20) ◽  
pp. 6191
Author(s):  
Hanxiao Wang ◽  
Yinghao Miao ◽  
Hailu Yang ◽  
Zhoujing Ye ◽  
Linbing Wang
Keyword(s):  
Wavelet Transform ◽  
Time Scale ◽  
Fringe Pattern ◽  
Intrinsic Time ◽  
Time Scale Decomposition

Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine

2017 ◽  
Vol 232 (5) ◽  
pp. 881-894 ◽  
Author(s):  
Yu Liu ◽  
Junhong Zhang ◽  
Kongjian Qin ◽  
Yueyun Xu
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Time Scale ◽  
Relevance Vector Machine ◽  
Economic Losses ◽  
Intrinsic Time ◽  
Fault Features ◽  
Time Scale Decomposition ◽  
Diagnosis Approach

Diesel engine is the most widely used power source of machines. However, faults occur frequently and often cause terrible accidents and serious economic losses. Therefore, diesel engine fault diagnosis is very important. Commonly, a single unitary pattern recognition method is used to diagnose the faults of diesel engine, but its performance decreases sharply when there are many fault types. Targeting this problem, a novel diesel engine fault diagnosis approach is proposed in this study. The approach is composed of four stages. Firstly, the nonstationary and nonlinear vibration signal of diesel engine is decomposed into a series of proper rotation components (PRCs) and a residual signal by the intrinsic time-scale decomposition (ITD) method. Secondly, six typical time-domain and four typical frequency-domain characteristics of the first several PRCs are extracted as fault features. Then, the modular and ensemble concepts are introduced to construct the multistage Adaboost relevance vector machine (RVM) model, in which the kernel fuzzy c-means clustering (KFCM) algorithm is used to decompose a complex classification task into several simple modules, and the Adaboost algorithm is used to improve the performance of each RVM based module. Finally, the fault diagnosis results can be obtained by inputting the fault features into the multistage Adaboost RVM model. The analysis results show that the fault diagnosis approach based on ITD and multistage Adaboost RVM performs effectively for the fault diagnosis of diesel engine, and it is better than the traditional pattern recognition methods.

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