Singular Vector-Inspired Dictionary Learning for Sparse Decomposition of Vibration Signal

2019 ◽  
Author(s):  
Baoqing Ding ◽  
Ruqiang Yan ◽  
Shibin Wang ◽  
Chaowei Tong ◽  
Xuefeng Chen
Keyword(s):  
Dictionary Learning ◽  
Singular Vector ◽  
Hankel Matrix ◽  
Design Procedure ◽  
Vibration Signal ◽  
Sparse Decomposition ◽  
Signal Features ◽  
Fault Features ◽  
Value Decomposition ◽  
Basis Vectors

Abstract Dictionary design is a primary challenge for sparsity-assisted fault diagnosis techniques. The dictionary can either be specified through signal priors or learned from data samples. In this paper, we propose a singular vector-inspired dictionary learning (SVDL) method, combining signal priors and data samples for sparse decomposition of machinery vibration signal. The SVDL is a data driven scheme for learning dictionary atoms and simultaneously exploring the prior information. It is achieved by alternating between atom design and dictionary update steps. First, we introduce the singular vector as vibration signal priors to atom design procedure. Through singular value decomposition on the Hankel matrix of the vibration signal, the inherent signal features corresponding to mechanical fault can be expressed with some singular vectors. These vectors, closely related to fault features, are extracted as basis vectors to constitute atoms. Second, the vector quantization method is used for atom design, by clustering the extracted basis vectors for the purpose of learning representative atoms. As to the dictionary update step, a strategy including sparse coding and dictionary optimization is adopted to simplify and update atoms. The results of the numerical simulation and experimental application show that the proposed SVDL method outperforms the classical K-SVD method for vibration signal denoising and fault feature extraction.

Feature extraction of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy

2020 ◽  
pp. 107754632092566 ◽  
Author(s):  
HongChao Wang ◽  
WenLiao Du
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Sparse Decomposition ◽  
Vibration Data ◽  
Fault Features ◽  
Band Pass

As the key rotating parts in machinery, it is crucial to extract the latent fault features of rolling bearing in machinery condition monitoring to avoid the occurrence of sudden accidents. Unfortunately, the latent fault features are hard to extract by using the traditional signal processing method such as envelope demodulation because the effect of envelope demodulation is influenced strongly by the degree of background noise. Sparse decomposition, as a new promising method being able of capturing the latent fault feature components buried in the vibration signal, has attracted a lot of attentions, especially the predefined dictionary-based sparse decomposition methods. However, the feature extraction effect of the predefined dictionary-based sparse decomposition depends on whether the prior knowledge of the analyzed signal is sufficient or not. To overcome the above problems, a feature extraction method of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy is proposed in the article. First, a self-learned sparse atomics method is applied on the early weak vibration signal of rolling bearing and several self-learned atomics are obtained. Then, the self-learned atomics owing bigger kurtosis values are selected and used to reconstruct the vibration signal to remove the other interference signals. Subsequently, the frequency band entropy method is used to analyze the reconstructed vibration signal, and the optimal parameter of band-pass filter could be calculated. At last, the reconstructed vibration signal is filtered using the optimal band-pass filter, envelope demodulation on the filtered signal is applied, and better fault feature is extracted. The feasibility and effectiveness of the proposed method are verified through the vibration data of the accelerated fatigue life test of rolling bearing. Besides, the analysis results of the same vibration data using Autogram and spectral kurtosis methods are also presented to highlight the superiority of the proposed method.

scholarly journals Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5541
Author(s):  
Kai Zheng ◽  
Yin Bai ◽  
Jingfeng Xiong ◽  
Feng Tan ◽  
Dewei Yang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
State Of The Art ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
The State ◽  
Low Rank ◽  
Sparse Decomposition ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Value Decomposition

Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods.

Engine Fault Diagnosis Based on EEMD Difference Energy Spectrum

2014 ◽  
Vol 598 ◽  
pp. 210-214 ◽  
Author(s):  
Hong Xia Pan ◽  
Gang Xiang Guo ◽  
Hai Feng Ren
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Energy Spectrum ◽  
Working Condition ◽  
Hankel Matrix ◽  
Vibration Signal ◽  
Difference Spectrum ◽  
Singular Value ◽  
Value Decomposition ◽  
Fault Information

To fault diagnosis of diesel engine, put forward a fault diagnosis of diesel engine based on EEMD difference energy spectrum of singular value and RBF. Nonstationary original acceleration vibration signal of kinds of diesel engine’s working condition is separated to several IMF and structure a Hankel matrix by the IMF for singular value decomposition, then de-noise and reconstruction one IMF on the basis of the theory of singular value difference spectrum, and use the reconstructed IMF’s energy which include fault information as the income of RBF. This method can judge the kinds of diesel engine’s working condition and fault types accurately in the experiment.

A Fast, Memory-Efficient Discrete-Time Realization Algorithm for Reduced-Order Li-Ion Battery Models

2017 ◽  
Vol 14 (1) ◽  
Author(s):  
Krishnakumar Gopalakrishnan ◽  
Teng Zhang ◽  
Gregory J. Offer
Keyword(s):  
Discrete Time ◽  
Porous Electrode ◽  
Hankel Matrix ◽  
Lithium Ion ◽  
Computation Time ◽  
Power Sources ◽  
Reduced Order ◽  
The Time Domain ◽  
Computational Bottleneck ◽  
Value Decomposition

Research into reduced-order models (ROM) for Lithium-ion batteries is motivated by the need for a real-time embedded model possessing the accuracy of physics-based models, while retaining computational simplicity comparable to equivalent-circuit models. The discrete-time realization algorithm (DRA) proposed by Lee et al. (2012, “One-Dimensional Physics-Based Reduced-Order Model of Lithium-Ion Dynamics,” J. Power Sources, 220, pp. 430–448) can be used to obtain a physics-based ROM in standard state-space form, the time-domain simulation of which yields the evolution of all the electrochemical variables of the standard pseudo-2D porous-electrode battery model. An unresolved issue with this approach is the high computation requirement associated with the DRA, which needs to be repeated across multiple SoC and temperatures. In this paper, we analyze the computational bottleneck in the existing DRA and propose an improved scheme. Our analysis of the existing DRA reveals that singular value decomposition (SVD) of the large Block–Hankel matrix formed by the system's Markov parameters is a key inefficient step. A streamlined DRA approach that bypasses the redundant Block–Hankel matrix formation is presented as a drop-in replacement. Comparisons with existing DRA scheme highlight the significant reduction in computation time and memory usage brought about by the new method. Improved modeling accuracy afforded by our proposed scheme when deployed in a resource-constrained computing environment is also demonstrated.

Research of the Vertical Vibrations of Tandem Mill Based on Wavelet Analysis

2015 ◽  
Vol 724 ◽  
pp. 279-282
Author(s):  
Chun Hua Ren ◽  
Xu Ma ◽  
Ze Ming Li ◽  
Yan Hong Ding
Keyword(s):  
Wavelet Analysis ◽  
Wavelet Decomposition ◽  
Vibration Signal ◽  
Vertical Vibration ◽  
Rolling Speed ◽  
Vibration Data ◽  
Signal Features ◽  
Operating Side ◽  
Vertical Vibrations ◽  
Vibration Parameters

In this paper, the defect sheet was captured coincidentally. According to the defective product’s characteristics, we suspected to be caused by the vertical vibration of the roll. When the rolling speed reached a certain value, the vibration of the fourth stand can be feel. The experiment of the vibration data collection was taken to compare the vibration parameters of rolling operating side with those of drive side by wavelet analysis. The result states that the abnormal vibration signal features can be extracted in a special frequency segment of wavelet decomposition, and the vibration frequency to the roll is confirmed which appeared product defects.

scholarly journals Vibration-based tool life monitoring for ceramics micro-cutting under various toolpath strategies

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 125
Author(s):  
Zsolt János Viharos ◽  
László Móricz ◽  
Máté István Büki
Keyword(s):  
Tool Path ◽  
High Efficiency ◽  
Vibration Signal ◽  
Ceramic Materials ◽  
Micro Milling ◽  
The Novel ◽  
Feature Selection Technique ◽  
Tool Condition ◽  
Signal Features ◽  
Milling Tool

The 21st century manufacturing technology is unimagined without the various CAM (Computer Aided Manufacturing) toolpath generation programs. The aims of developing the toolpath strategies which are offered by the cutting control software is to ensure the longest possible tool lifetime and high efficiency of the cutting method. In this paper, the goal is to compare the efficiency of the 3 types of tool path strategies in the very special field of micro-milling of ceramic materials. The dimensional distortion of the manufactured geometries served to draw the Taylor curve for describing the wearing progress of the cutting tool helping to determine the worn-in, normal and wear out stages. These isolations allow to separate the connected high-frequency vibration measurements as well. Applying the novel feature selection technique of the authors, the basis for the vibration based micro-milling tool condition monitoring for ceramics cutting is presented for different toolpath strategies. It resulted in the identification of the most relevant vibration signal features and the presentation of the identified and automatically separated tool wearing stages as well.

scholarly journals Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zong Yuan ◽  
Taotao Zhou ◽  
Jie Liu ◽  
Changhe Zhang ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Ranking And Selection ◽  
Fault Features ◽  
Feature Importance ◽  
Importance Ranking ◽  
Common Signal ◽  
Diagnosis Approach

The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.

A dictionary learning method with atom splitting for seismic footprint suppression

Geophysics ◽  
2021 ◽  
pp. 1-97
Author(s):  
Dawei Liu ◽  
Lei Gao ◽  
Xiaokai Wang ◽  
wenchao Chen
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Original Data ◽  
Morphological Features ◽  
Time Slice ◽  
Learning Method ◽  
Seismic Attribute ◽  
Morphological Component Analysis ◽  
Value Decomposition

Acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. In this work, we assume that the time slice of 3D post-stack migration seismic data mainly comprises two components, i.e., useful signals and acquisition footprint. Useful signals describe the spatial distributions of geological structures with local piecewise smooth morphological features. However, acquisition footprint often behaves as periodic artifacts in the time-slice domain. In particular, the local morphological features of the acquisition footprint in the marine seismic acquisition appear as stripes. As useful signals and acquisition footprint have different morphological features, we can train an adaptive dictionary and divide the atoms of the dictionary into two sub-dictionaries to reconstruct these two components. We propose an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D post-stack migration seismic data. To obtain an adaptive dictionary, we use the K-singular value decomposition algorithm to sparsely represent the patches in the time slice of 3D post-stack migration seismic data. Each atom of the trained dictionary represents certain local morphological features of the time slice. According to the difference in the variation level between the horizontal and vertical directions, the atoms of the trained dictionary are divided into two types. One type significantly represents the local morphological features of the acquisition footprint, whereas the other type represents the local morphological features of useful signals. Then, these two components are reconstructed using morphological component analysis based on different types of atoms, respectively. Synthetic and field data examples indicate that the proposed method can effectively suppress the acquisition footprint with fidelity to the original data.

Retrieving the leaked signals from noise using a fast dictionary learning method

Geophysics ◽  
2021 ◽  
pp. 1-86
Author(s):  
Wei Chen ◽  
Omar M. Saad ◽  
Yapo Abolé Serge Innocent Oboué ◽  
Liuqing Yang ◽  
Yangkang Chen
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
State Of The Art ◽  
Computational Cost ◽  
Learning Method ◽  
Global Parameter ◽  
Radius Parameter ◽  
Orthogonalization Method ◽  
Value Decomposition ◽  
Learned Features

Most traditional seismic denoising algorithms will cause damages to useful signals, which are visible from the removed noise profiles and are known as signal leakage. The local signal-and-noise orthogonalization method is an effective method for retrieving the leaked signals from the removed noise. Retrieving leaked signals while rejecting the noise is compromised by the smoothing radius parameter in the local orthogonalization method. It is not convenient to adjust the smoothing radius because it is a global parameter while the seismic data is highly variable locally. To retrieve the leaked signals adaptively, we propose a new dictionary learning method. Because of the patch-based nature of the dictionary learning method, it can adapt to the local feature of seismic data. We train a dictionary of atoms that represent the features of the useful signals from the initially denoised data. Based on the learned features, we retrieve the weak leaked signals from the noise via a sparse co ding step. Considering the large computational cost when training a dictionary from high-dimensional seismic data, we leverage a fast dictionary up dating algorithm, where the singular value decomposition (SVD) is replaced via the algebraic mean to update the dictionary atom. We test the performance of the proposed method on several synthetic and field data examples, and compare it with that from the state-of-the-art local orthogonalization method.

scholarly journals Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098056
Author(s):  
Walid Touzout ◽  
Djamel Benazzouz ◽  
Fawzi Gougam ◽  
Adel Afia ◽  
Chemseddine Rahmoune
Keyword(s):  
Singular Value Decomposition ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Vibration Signal ◽  
Relevant Information ◽  
Singular Value ◽  
Inference System ◽  
Operation Conditions ◽  
Neuro Fuzzy ◽  
Value Decomposition

Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.

Sign in / Sign up

Export Citation Format

Share Document