scholarly journals A Two-Stage Compression Method for the Fault Detection of Roller Bearings

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Huaqing Wang ◽  
Yanliang Ke ◽  
Ganggang Luo ◽  
Lingyang Li ◽  
Gang Tang
Keyword(s):  
Fault Detection ◽  
Roller Bearing ◽  
Sampling Theorem ◽  
Two Stage ◽  
Detection Scheme ◽  
Bearing Fault Diagnosis ◽  
Roller Bearings ◽  
Fault Features ◽  
Shannon Sampling Theorem ◽  
Parameter Wave

Data measurement of roller bearings condition monitoring is carried out based on the Shannon sampling theorem, resulting in massive amounts of redundant information, which will lead to a big-data problem increasing the difficulty of roller bearing fault diagnosis. To overcome the aforementioned shortcoming, a two-stage compressed fault detection strategy is proposed in this study. First, a sliding window is utilized to divide the original signals into several segments and a selected symptom parameter is employed to represent each segment, through which a symptom parameter wave can be obtained and the raw vibration signals are compressed to a certain level with the faulty information remaining. Second, a fault detection scheme based on the compressed sensing is applied to extract the fault features, which can compress the symptom parameter wave thoroughly with a random matrix called the measurement matrix. The experimental results validate the effectiveness of the proposed method and the comparison of the three selected symptom parameters is also presented in this paper.

scholarly journals Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yanfeng Peng ◽  
Junhang Chen ◽  
Yanfei Liu ◽  
Junsheng Cheng ◽  
Yu Yang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Narrow Band ◽  
Matching Pursuit ◽  
Roller Bearing ◽  
Bearing Fault Diagnosis ◽  
Roller Bearings ◽  
Performance Accuracy ◽  
Mode Decomposition ◽  
Fault Features ◽  
Margin Classification

Adaptive sparsest narrow-band decomposition (ASNBD) method is proposed based on matching pursuit (MP) and empirical mode decomposition (EMD). ASNBD obtains the local narrow-band (LNB) components during the optimization process. Firstly, an optimal filter is designed. The parameter vector in the filter is obtained during optimization. The optimized objective function is a regulated singular local linear operator so that each obtained component is limited to be a LNB signal. Afterward, a component is generated by filtering the original signal with the optimized filter. Compared with MP, ASNBD is superior in both the physical meaning and the adaptivity. Drawbacks in EMD such as end effect and mode mixing are reduced in the proposed method because the application of interpolation function is not required. To achieve the fault diagnosis of roller bearings, raw signals are decomposed by ASNBD at first. Then, appropriate features of the decomposed results are chosen by applying distance evaluation technique (DET). Afterward, different faults are recognized by utilizing maximum margin classification based on flexible convex hulls (MMC-FCH). Comparisons between EMD and ASNBD show that the proposed method performs better in the antinoise performance, accuracy, orthogonality, and extracting the fault features of roller bearings.

scholarly journals Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Wei-Li Qin ◽  
Wen-Jin Zhang ◽  
Zhen-Ya Wang
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Unlabeled Data ◽  
Edge Weight ◽  
Ring Fault ◽  
Bearing Fault ◽  
Cut Edge ◽  
Bearing Fault Diagnosis ◽  
Roller Bearings ◽  
Diagnosis Method

Roller bearings are one of the most commonly used components in rotational machines. The fault diagnosis of roller bearings thus plays an important role in ensuring the safe functioning of the mechanical systems. However, in most cases of bearing fault diagnosis, there are limited number of labeled data to achieve a proper fault diagnosis. Therefore, exploiting unlabeled data plus few labeled data, this paper proposed a roller bearing fault diagnosis method based on tritraining to improve roller bearing diagnosis performance. To overcome the noise brought by wrong labeling into the classifiers training process, the cut edge weight confidence is introduced into the diagnosis framework. Besides a small trick called suspect principle is adopted to avoid overfitting problem. The proposed method is validated in two independent roller bearing fault experiment vibrational signals that both include three types of faults: inner-ring fault, outer-ring fault, and rolling element fault. The results demonstrate the desirable diagnostic performance improvement by the proposed method in the extreme situation where there is only limited number of labeled data.

A frequency-weighted energy operator and swarm decomposition for bearing fault diagnosis

2022 ◽  
pp. 1-13
Author(s):  
Xianyou Zhong ◽  
Tianyi Xia ◽  
Yankun Zhao ◽  
Xiao Zhao
Keyword(s):  
Pearson Correlation ◽  
Energy Operator ◽  
Single Mode ◽  
Rolling Bearings ◽  
Detection Scheme ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Weighted Energy ◽  
Fault Features ◽  
Impulse Characteristics

The weak fault characteristics of rolling bearings are difficult to identify due to strong background noise. To address this issue, a bearing fault detection scheme combining swarm decomposition (SWD) and frequency-weighted energy operator (FWEO) is presented. First, SWD is applied to decompose the bearing fault signal into single mode components. Then, a new evaluation index termed LEP is constructed by combining the advantages of envelope entropy, Pearson correlation coefficient and L-kurtosis, and it is utilized to choose the sensitive component containing the richest bearing fault characteristics. Finally, FWEO is employed for extracting the bearing fault features from the sensitive component. Simulation and experimental analyses indicate that the LEP index has better performance than the L-kurtosis index in determining the sensitive component. The method has the effect of suppressing noise and enhancing impulse characteristics, which is superior to the SWD-based envelope demodulation method.

scholarly journals Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Hosameldin O. A. Ahmed ◽  
Asoke K Nandi
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Fault Classification ◽  
Health Conditions ◽  
Connected Components ◽  
Rotating Machines ◽  
Two Stage ◽  
Vibration Signals ◽  
Roller Bearings

AbstractRoller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

Fault severity identification of roller bearings using flow graph and non-naive Bayesian inference

2019 ◽  
Vol 233 (14) ◽  
pp. 5161-5171 ◽  
Author(s):  
Jun Yu ◽  
Yonggang Xu ◽  
Guangbin Yu ◽  
Lifei Liu
Keyword(s):  
Bayesian Inference ◽  
Roller Bearing ◽  
Flow Graph ◽  
Naive Bayesian ◽  
Roller Bearings ◽  
Naïve Bayesian ◽  
Fault Severity ◽  
Training Samples ◽  
Fault Features ◽  
The Impact

In order to address the problem that redundant condition attribute nodes and poor reasoning ability of flow graph may lead to high computational burden and low diagnosis accuracy, a fault severity identification method of roller bearings using flow graph and non-naive Bayesian inference is put forward in this paper. First, a normalized flow graph constructed according to fault features of roller bearings extracted from training samples is used to represent and describe the causal relationship among attributes. Then, the significance degree of condition attribute node with respect to the decision attribute node set is defined to quantitatively measure the impact of the node on the decision-making abilities of the flow graph. A node reduction algorithm based on significance degree of condition attribute node is developed to delete redundant or irrelevant condition attribute nodes, which can improve clustering distribution and reduce computational complexity. Finally, non-naive Bayesian inference is utilized to extend the flow graph to make it applicable in the tasks of reasoning, and an non-naive Bayesian inference algorithm based on flow graph is presented to identify roller-bearing conditions in test samples. The effectiveness of the proposed method is validated through a fault severity identification experiment of roller bearings. Fault severity identification results show that the proposed method can intuitively and accurately recognize fault severities of roller bearings.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

scholarly journals Distributed global actuator fault-detection scheme for a class of linear multi-agent systems with disturbances

2020 ◽  
Vol 53 (2) ◽  
pp. 4202-4207
Author(s):  
Anass Taoufik ◽  
Michael Defoort ◽  
Mohamed Djemai ◽  
Krishna Busawon ◽  
Juan Diego Sánchez-Torres
Keyword(s):  
Fault Detection ◽  
Actuator Fault ◽  
Multi Agent Systems ◽  
Detection Scheme ◽  
Agent Systems ◽  
Multi Agent

Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

2021 ◽  
pp. 147592172110360
Author(s):  
Dongming Hou ◽  
Hongyuan Qi ◽  
Honglin Luo ◽  
Cuiping Wang ◽  
Jiangtian Yang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Signal Attenuation ◽  
Transmission Path ◽  
Bearing Fault Diagnosis ◽  
Operation Conditions ◽  
Fault Features ◽  
High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

Weak fault detection with a two-stage key frequency focusing model

2021 ◽  
Author(s):  
Dawei Gao ◽  
Yongsheng Zhu ◽  
Wei Kang ◽  
Hong Fu ◽  
Ke Yan ◽  
...  
Keyword(s):  
Fault Detection ◽  
Two Stage ◽  
Weak Fault
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