scholarly journals A two-step vibration-sound signal fusion method for weak fault feature detection in rolling bearing systems

2021 ◽  
Vol 13 (12) ◽  
pp. 168781402110671
Author(s):  
Guanchen Wu ◽  
Nengyu Yan ◽  
Kwang-nam Choi ◽  
Hoekyung Jung ◽  
Kerang Cao
Keyword(s):  
Feature Detection ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Sound Signal ◽  
Detection Accuracy ◽  
Fusion Method ◽  
Signal Fusion ◽  
Signal Features ◽  
Weak Fault

The vibration and sound signals get widely applications in fault diagnosis of rolling bearing systems, but the detection accuracy is unstable at different measuring positions. This paper puts forward a two-step vibration-sound signal fusion method, in which sound signal fusion and vibration-sound signal fusion are executed respectively. The sound signals are fused through weighting to the vibration signal to reduce the influence by measuring positions, and the phase difference is eliminated by a sliding window on the time axis. Then a second fusion between the vibration signal and sound signal is conducted after normalization and superposition, and the performance of two-step fusion is compared with the existing direct fusion. Results show that the two-step fusion provides a larger signal-to-noise ratio, and the amplitudes of characteristic frequencies are also higher. A cascaded bistable stochastic resonance system is applied in the post-processing of the fusion signal to make the signal features more clear, and it is proved that the fault detection effect has an obvious improvement after the whole process. This method provides a new approach for weak fault feature detection in vibration and sound signals, and is of great significance for the maintenance of rolling bearing systems.

An adaptive multiple time domain synchronous averaging method and its application in vibration signal feature enhancement

2021 ◽  
Author(s):  
Na Yin ◽  
Zong Meng ◽  
Yang Guan ◽  
Fengjie Fan
Keyword(s):  
Time Domain ◽  
Feature Detection ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Difficult Problem ◽  
Multiple Time ◽  
Denoising Method ◽  
Time Domain Signal ◽  
Signal Features ◽  
The Time Domain

Abstract The time domain synchronous averaging (TSA) method is a typical time domain signal denoising method, which is widely used in the state detection of rotating machinery. In order to solve the difficult problem of extracting vibration signal features from strong interference, an adaptive multiple time domain synchronous averaging(aMTSA) method based on signal period is proposed in this paper. In view of the blindness and randomness of period selection in TSA method, a new evaluation index of periodic impulse characteristics is proposed. In this method, the signal is resampled then the iteration stop threshold is set, and then the calculation period of interest is determined by two cycle screening. Finally, reconstructed signals with enhanced features are obtained by copying and stitching. Experimental results show that the proposed method is robust and superior in the feature detection of rolling bearing vibration signals.

scholarly journals Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 184 ◽  
Author(s):  
Qing Li ◽  
Steven Liang
Keyword(s):  
Vibration Signal ◽  
Singular Values ◽  
Rolling Bearing ◽  
L1 Norm ◽  
Detection Approach ◽  
The Matrix ◽  
Noisy Observation ◽  
Weak Fault ◽  
Convex Regularization ◽  
Fault Information

Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.

scholarly journals Heterogeneous Signal Fusion Method in Driving Fatigue Detection Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qingjun Wang ◽  
Zhendong Mu
Keyword(s):  
Traffic Accidents ◽  
Information Source ◽  
Steering Wheel ◽  
Detection Accuracy ◽  
Fusion Method ◽  
Eyelid Closure ◽  
Signal Fusion ◽  
Fusion Methods ◽  
Driving Fatigue ◽  
Fatigue Detection

Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver’s breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%.

scholarly journals A Fault Feature Extraction Method for Rolling Bearing Based on Intrinsic Time-Scale Decomposition and AR Minimum Entropy Deconvolution

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Jiakai Ding ◽  
Liangpei Huang ◽  
Dongming Xiao ◽  
Lingli Jiang
Keyword(s):  
Time Scale ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Sample Entropy ◽  
Minimum Entropy ◽  
Feature Extraction Method ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Weak Fault ◽  
Feature Frequency

It is very difficult to extract the feature frequency of the vibration signal of the rolling bearing early weak fault and in order to extract its feature frequency quickly and accurately. A method of extracting early weak fault vibration signal feature frequency of the rolling bearing by intrinsic time-scale decomposition (ITD) and autoregression (AR) minimum entropy deconvolution (MED) is proposed in this paper. Firstly, the original early weak fault vibration signal of the rolling bearing is decomposed by the ITD algorithm to proper rotations (PRs) with fault feature frequency. Then, the sample entropy value of each PR is calculated to find the largest PRs of the sample entropy. Finally, the AR-MED filtering algorithm is utilized to filter and reduce the noise of the largest PRs of the sample entropy value, and the early weak fault vibration signal feature frequency of the rolling bearing is accurately extracted. The results show that the ITD-AR-MED method can extract the early weak fault vibration signal feature frequency of the rolling bearing accurately.

scholarly journals Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 762
Author(s):  
Maoyou Ye ◽  
Xiaoan Yan ◽  
Minping Jia
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Model ◽  
Permutation Entropy ◽  
Support Vector ◽  
Detection Accuracy ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Bearing Vibration

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).

A hybrid deep-learning model for fault diagnosis of rolling bearings under strong noise environment

2022 ◽  
Author(s):  
Ke Zhang ◽  
Caizi Fan ◽  
Xiaochen Zhang ◽  
Huaitao Shi ◽  
Songhua Li
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Recognition Rate ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Learning Model ◽  
Detection Accuracy ◽  
Practical Applications ◽  
Reconstructed Signal ◽  
Deep Learning Model

Abstract Aiming at the problem that the signal of rolling bearing is interfered by strong noise in practical engineering environment, which leads to the decline of the diagnosis accuracy of intelligent diagnosis model. This paper proposes a novel hybrid model (CDAE-BLCNN). First, the rolling bearing vibration signal containing noise was input into the Convolutional Denoising Auto-Encoder (CDAE), which denoises the signal through unsupervised learning, and then outputs the reconstructed data. Secondly, a hybrid neural network (BLCNN) composed of multi-scale wide convolution kernel block (MWCNN) and bidirectional long-short-term memory network (BiLSTM) was used to extract intrinsic fault features from the reconstructed signal and diagnose fault types. The analysis results demonstrate that the proposed hybrid deep learning model achieves higher detection accuracy even under different noise and various rotating speed. Compared with other models, there is a high fault recognition rate, robustness, and generalization ability, which may be favorable to practical applications.

scholarly journals Rolling Bearing Fault Vibration Signal Denoising Based on Adaptive Morphological Wavelet Perona–Malik Filter Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hao Li ◽  
Yifan Tan ◽  
Yun Pu
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Haar Wavelet ◽  
Signal Denoising ◽  
Second Derivative ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Noise Point

This paper proposes an adaptive Perona–Malik filtering algorithm based on the morphological Haar wavelet, which is used for vibration signal denoising in rolling bearing fault diagnosis with strong noise. First, the morphological Haar wavelet operator is utilized to presmooth the noisy signal, and the gradient of the presmooth signal is estimated. Second, considering the uncertainty of gradient at the strong noise point, a strong noise point recognition operator is constructed to adaptively identify the strong noise point. Third, the two-step gradient average value of the strong noise point in the same direction is used to substitute, and the second derivative is introduced into the diffusion coefficient. Finally, diffusion filtering is performed based on the improved Perona–Malik model. The simulation experiment result indicated that not only the algorithm can denoise effectively, but also the average gradient and second derivative in the same direction can effectively suppress the back diffusion of strong noise points to improve the denoising signal-to-noise ratio. The experimental results of rolling bearing show that the algorithm can adaptively filter out strong noise points and keep the information of peak in the signal well, which can improve the accuracy of rolling bearing fault diagnosis.

A dual sensor signal fusion approach for detection of faults in rotating machines

2017 ◽  
Vol 24 (12) ◽  
pp. 2621-2630 ◽  
Author(s):  
M Saimurugan ◽  
R Ramprasad
Keyword(s):  
Fault Diagnosis ◽  
Decision Tree ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Sound Signal ◽  
Maintenance Strategy ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Signal Fusion

The growing industrial sector utilizes machinery that needs to be monitored continuously by proficient experts and robust software to ensure a proper maintenance strategy. Condition monitoring using vibration signal analysis is one of the chief methods used in predictive maintenance strategy for rotating machinery. Generally, sound signal analysis is considered as secondary as it involves noise. In this paper, the signals for various rotating machinery faults are studied by simulating them in a machine fault simulator at various speeds and the best features are fused to obtain more efficiency in the fault diagnosis of rotating machinery. The vibration signal data obtained from accelerometers and sound signal data from a microphone is extracted for features using wavelet transform. The best features from vibration and sound signals are selected using the decision tree algorithm and are fused. Further, the features are classified using an artificial neural network and the corresponding efficiency at various motor speeds is found. The results of this paper imply that the signal fusion of vibration and sound by the decision tree algorithm is effective in machine fault diagnosis methodologies.

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

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