Diagnosis of Roller Bearings Compound Fault Using Underdetermined Blind Source Separation Algorithm Based on Null-Space Pursuit

Cui, Lingli; Wu, Chunguang; Ma, Chunqing; Wang, Huaqing

doi:10.1155/2015/131489

Cited by 9 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Blind source separation (BSS), which can recover underlying sources from observations without the knowledge of the mixing system, is widely used in machinery fault diagnosis [1][2][3][4][5], speech recognition [6], wireless communication [7], and so on. Nowadays, BSS techniques applied in the machinery fault diagnosis mainly focus on two aspects: (1) removal of interferences and disturbances and (2) parameter modeling and feature detection for mechanical faults.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction

Huang

Jin

2016

Shock and Vibration

View full text Add to dashboard Cite

Nowadays, the existing blind source separation (BSS) algorithms in rotating machinery fault diagnosis can hardly meet the demand of fast response, high stability, and low complexity simultaneously. Therefore, this paper proposes a spectrum correction based BSS algorithm. Through the incorporation of FFT, spectrum correction, a screen procedure (consisting of frequency merging, candidate pattern selection, and single-source-component recognition), modifiedk-means based source number estimation, and mixing matrix estimation, the proposed BSS algorithm can accurately achieve harmonics sensing on field rotating machinery faults in case of short-sampled observations. Both numerical simulation and practical experiment verify the proposed BSS algorithm’s superiority in the recovery quality, stability to insufficient samples, and efficiency over the existing ICA-based methods. Besides rotating machinery fault diagnosis, the proposed BSS algorithm also possesses a vast potential in other harmonics-related application fields.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In [11], an improved morphological component analysis (MCA) is proposed to diagnose compound faults of gearboxes. Cui et al [4] put forward a null-space pursuit (NSP) BSS algorithm to diagnose compound faults of roller bearings.…”

Section: Introductionmentioning

confidence: 99%

Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction

Huang

Jin

2016

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Because the equipment that maintains healthy operation has different requirements for different faults, it is of great significance to detect the main fault factors that affect the equipment's healthy operation under complex fault conditions. Actually, studies on multi-fault diagnosis have emerged, such as turbo-expander [13], rotor system [14], gearbox [15,16], and rolling bearings [17,18]. However, most of the researches on multi-fault diagnosis were on fault separation and diagnosis from the perspective of signal processing, such as Wavelet Packet Transform [15,18,19], Blind Source Separation (BSS) [17], and Empirical Mode Decomposition (EMD) [13,14].…”

mentioning

confidence: 99%

“…Actually, studies on multi-fault diagnosis have emerged, such as turbo-expander [13], rotor system [14], gearbox [15,16], and rolling bearings [17,18]. However, most of the researches on multi-fault diagnosis were on fault separation and diagnosis from the perspective of signal processing, such as Wavelet Packet Transform [15,18,19], Blind Source Separation (BSS) [17], and Empirical Mode Decomposition (EMD) [13,14]. Zhao [12] exploited the generalized demodulation algorithm and Fast Fourier Transformation (FFT) algorithm to transform the multi-fault bearing signal under time-varying rotational speed, and the analysis of the experimental bearing signal validated the effectiveness and reliability of the proposed approach.…”

mentioning

confidence: 99%

A Novel Method for Intelligent Single Fault Detection of Bearings Using SAE and Improved D–S Evidence Theory

Zhang

Tang

2019

Entropy

View full text Add to dashboard Cite

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster-Shafer evidence theory (D-S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors' data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D-S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases; when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types' fault detection accuracy-reached to 99.12%, 99.33% and 98.46% by the improved Dempster-Shafer evidence theory (IDS) to fuse the sensors' results-is respectively 0.38%, 2.06% and 0.76% higher than the traditional D-S evidence theory. That indicated the effectiveness of improving the D-S evidence theory by evidence weight calculation of PCC.Entropy 2019, 21, 687 2 of 21 extracted features by Wavelet Packet Transformation, selected features through automatic encoder, and classified them by SVM. The proposed approach achieved very high accuracy and robustness to classify ball, inner race, and outer race faults even under very poor signal-to-noise ratio (SNR) conditions. Due to the unavoidable limitation in terms of accuracy and robustness in the feature extraction approaches of traditional shallow learning, Lu [6] investigated the deep learning method based on a convolutional neural network (CNN), the novel feature representation method for bearing data using supervised deep learning with the goal of identifying more robust and salient feature representations to reduce information loss, and two experiments had proved the efficiency of the proposed method. Wei [7] proposed a new adaptive features selection technique applied to the bearing fault diagnosis with affinity propagation clustering, and results demonstrated that the approach is able to reliably and accurately identify different fault categories and severities of bearings. For the studies shown above and others [8][9][10][11], they all aimed at the failure of one single point on the bearings; the current methods of feature automatic extraction and automatic classification didn't consider the failure conditions of multiple faults combined. Actually, current researche...

show abstract

“…NSP is used to estimate the differential operator from vibration model of bearing fault. In that study, the approach used to estimate the mixing matrix is AJD [8]. In previous research, [9] we have succeeded in separating the mixed signal so it can be used to determine the condition of the engine by using AJD approach [9].…”

mentioning

confidence: 99%

Multi-way Array Decomposition on Acoustic Source Separation for Fault Diagnosis of a Motor-Pump System

Vinaya¹,

Arifianto²

2017

IJECE

View full text Add to dashboard Cite

<span>In this study, we propose a multi-way array decomposition approach to solve the complexity of approximate joint diagonalization process for fault diagnosis of a motor-pump system. Sources used in this study came from drive end-motor, nondrive end-motor , drive end pump , and nondrive end pump. An approximate joint diagonalization is a common approach to resolving an underdetermined cases in blind source separation. However, it has quite heavy computation and requires more complexity. In this study, we use an acoustic emission to detect faults based on multi-way array decomposition approach. Based on the obtained results, the difference types of machinery fault such as misalignment and outer bearing fault can be detected by vibration spectrum and estimated acoustic spectrum. The performance of proposed method is evaluated using MSE and LSD. Based on the results of the separation, the estimated signal of the nondrive end pump is the closest to the baseline signal compared to other signals with LSD is 1.914 and MSE is 0.0707. The instantaneous frequency of the estimated source signal will also be compared with the vibration signal in frequency spectrum to test the effectiveness of the proposed method.</span>

show abstract

Diagnosis of Roller Bearings Compound Fault Using Underdetermined Blind Source Separation Algorithm Based on Null-Space Pursuit

Cited by 9 publications

References 21 publications

Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction

Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction

A Novel Method for Intelligent Single Fault Detection of Bearings Using SAE and Improved D–S Evidence Theory

Multi-way Array Decomposition on Acoustic Source Separation for Fault Diagnosis of a Motor-Pump System

Contact Info

Product

Resources

About