Feature Extraction for Bearing Fault based on Singular Value Product Sequence of Sliding Hankel Matrix

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

Download Full-text

The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value

Shock and Vibration ◽

10.1155/2016/5957179 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 15

Author(s):

Te Han ◽

Dongxiang Jiang ◽

Nanfei Wang

Keyword(s):

Feature Extraction ◽

Vibration Signal ◽

Rolling Bearing ◽

Difference Spectrum ◽

Singular Value ◽

Feature Extraction Method ◽

Bearing Fault ◽

Mode Decomposition ◽

The Difference ◽

Fault Feature Extraction

Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears). Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD) and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.

Download Full-text

Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS

Mathematical Problems in Engineering ◽

10.1155/2021/6655081 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Zhiqiang Liao ◽

Xuewei Song ◽

Baozhu Jia ◽

Peng Chen

Keyword(s):

Feature Extraction ◽

Singular Value Decomposition ◽

Extraction Method ◽

Singular Values ◽

Singular Value ◽

Feature Extraction Method ◽

Bearing Fault ◽

Fault Features ◽

Value Decomposition ◽

Fault Feature Extraction

Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.

Download Full-text

Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2018.08.056 ◽

2019 ◽

Vol 118 ◽

pp. 477-502 ◽

Cited By ~ 35

Author(s):

Hua Li ◽

Tao Liu ◽

Xing Wu ◽

Qing Chen

Keyword(s):

Feature Extraction ◽

Singular Value Decomposition ◽

Frequency Band ◽

Singular Value ◽

Bearing Fault ◽

Value Decomposition ◽

Fault Feature Extraction

Download Full-text

Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion

Sensors ◽

10.3390/s21072524 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2524

Author(s):

Huibin Zhu ◽

Zhangming He ◽

Juhui Wei ◽

Jiongqi Wang ◽

Haiyin Zhou

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Feature Fusion ◽

Singular Value ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Fault Feature Extraction ◽

Frequency Feature

Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribute to guaranteeing running stability and maintenance, thus promoting production efficiency and economic benefits. Usually, the bearing fault features are difficult to extract effectively, which results in low diagnosis performance. To solve the problem, this paper proposes a bearing fault feature extraction method and it establishes a bearing fault diagnosis method that is based on feature fusion. The basic idea of the method is as follows: firstly, the time-frequency feature of the bearing signal is extracted through Wavelet Packet Transform (WPT) to form the time-frequency characteristic matrix of the signal; secondly, the Multi-Weight Singular Value Decomposition (MWSVD) is constructed by singular value contribution rate and entropy weight. The features of the time-frequency feature matrix obtained by WPT are further extracted, and the features that are sensitive to fault in the time-frequency feature matrix are retained while the insensitive features are removed; finally, the extracted feature matrix is used as the input of the Support Vector Machine (SVM) classifier for bearing fault diagnosis. The proposed method is validated by data sets from the time-varying bearing data from the University of Ottawa and Case Western Reserve University Bearing Data Center. The results show that the algorithm can effectively diagnose the bearing under the steady-state and unsteady state. This paper proposes that the algorithm has better fault diagnosis capabilities and feature extraction capabilities when compared with methods that aree based on traditional feature technology.

Download Full-text

Early Bearing Fault Diagnosis based on the Improved Singular Value Decomposition Method

10.21203/rs.3.rs-911468/v1 ◽

2021 ◽

Author(s):

Lingli Cui ◽

Mengxin Sun ◽

Jinfeng Huang

Keyword(s):

Singular Value Decomposition ◽

Decomposition Method ◽

Selection Index ◽

Hankel Matrix ◽

Singular Value ◽

Bearing Fault ◽

Singular Value Decomposition Method ◽

Weak Fault ◽

Value Decomposition ◽

Fault Information

Abstract The traditional singular value decomposition (SVD) method is unable to diagnose the weak fault feature of bearings effectively, which means, it is difficult to retain the effective singular components (SCs). Therefore, a new singular value decomposition method, SVD based on the FIC (fault information content), is proposed, which takes the amplitude characteristics of fault feature frequency as the selection index FIC of singular components. Firstly, the Hankel matrix of the original signal is constructed and SVD is applied in the matrix. Secondly, the proposed index FIC is used to evaluate the information of the decomposed SCs. Finally, the SCs with fault information are selected and added to obtain the denoised signal. The results of bearing fault simulation signals and experimental signals show that compared with the traditional differential singular value decomposition (DS-SVD), the proposed method can select the singular components with larger amount of fault information, and is able to diagnose the fault under the heavy noise interference. The new method can be used for signal denoising and weak fault feature extraction.

Download Full-text