scholarly journals Roller Bearing Performance Degradation Assessment Based on Fusion of Multiple Features of Electrostatic Sensors

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 824 ◽  
Author(s):  
Ying Zhang ◽  
Anchen Wang ◽  
Hongfu Zuo
Keyword(s):  
Monitoring System ◽  
Mixed Model ◽  
Roller Bearing ◽  
Degradation Process ◽  
Performance Degradation ◽  
Vibration Monitoring ◽  
Multiple Features ◽  
Roller Bearings ◽  
The Time Domain ◽  
Spectral Regression

This paper presents a new method to assess the performance degradation of roller bearings based on the fusion of multiple features, with the aim of improving the early degradation detection ability of the electrostatic monitoring system. At first, a set of feature parameters of the electrostatic monitoring system indicating the normal state of the bearings are extracted from the perspective of the time domain, frequency domain and complexity. Then, the parameter set is processed to reduce the dimensions and eliminate the redundancy using spectral regression. With the processed features, a Gaussian mixed model is established to gauge the health of the bearing, providing the distance value obtained using Bayesian inference as a quantitative indicator for assessing the performance degradation. The method is applied to access the life of a bearing in which the mechanic fatigue is artificially accelerated. The test results show that the proposed method can better reflect the degradation process of the bearing compared to other evaluation methods. This enables the electrostatic monitoring technique to detect the degradation of the bearing earlier than the vibration monitoring, providing a powerful tool for the condition monitoring of roller bearings.

Bearing Defect Detection Using On-Board Accelerometer Measurements

Joint Rail ◽  
2002 ◽  
Author(s):  
John Donelson ◽  
Ronald L. Dicus
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Roller Bearing ◽  
Spectral Lines ◽  
Roller Bearings ◽  
Bearing Defects ◽  
Bearing Defect ◽  
Freight Car ◽  
Condition Monitoring System ◽  
Vibration Signatures

Vibration signatures of defective roller bearings on railroad freight cars were analyzed in an effort to develop an algorithm for detecting bearing defects. The effort is part of a project to develop an on-board condition monitoring system for freight trains. The Office of Research and Development of the Federal Railroad Administration (FRA) is sponsoring the project. The measurements were made at the Transportation Technology Center (TTC) in Pueblo, CO on July 26 – 29, 1999 during the Phase III Field Test of the Improved Wayside Freight Car Roller Bearing Inspection Research Program sponsored by FRA and the Association of American Railroads (AAR). Wheel sets with specific roller bearing defects were installed on a test train consisting of 8 freight cars designed to simulate revenue service. The consist also contained non-defective roller bearings. Accelerometers were installed on the inboard side of the bearing adapters to measure the vibration signatures during the test. Signatures of both defective and non-defective bearings were recorded. The data were recorded on Sony Digital Audio Tape (DAT) Recorders sampling at a rate of 48 K samples per second. We used both ordinary and envelope spectral analysis to analyze the data in an effort to detect features that could be related to known defects. The spectra of non-defective bearings show no remarkable features at bearing defect frequencies. In general, the ordinary spectra of defective bearings do not exhibit remarkable features at the bearing defect frequencies. In contrast, the envelope spectra of defective bearings contain a number of highly resolved spectral lines at these frequencies. In several cases the spectral lines could be related to specific bearing defects. Based on the analysis performed to date, the envelope spectrum technique provides a promising method for detecting defects in freight car roller bearings using an on-board condition monitoring system.

scholarly journals Assessing the Performance Degradation of Lithium-Ion Batteries Using an Approach Based on Fusion of Multiple Feature Parameters

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Anchen Wang ◽  
Ying Zhang ◽  
Hongfu Zuo
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Degradation Process ◽  
Gaussian Mixture ◽  
Assessment Method ◽  
Performance Degradation ◽  
Discharge Voltage ◽  
Health State ◽  
Multiple Features ◽  
Feature Parameters

A method based on fusion of multiple features is proposed to assess and accurately describe the performance degradation of lithium-ion batteries in this paper. First, the discharge voltage signal of lithium-ion batteries under real-time monitoring is analyzed from the perspective of time domain and complexity to obtain the values of multiple features. Then, the multi-feature parameters undergo a spectral regression process to reduce the number of dimensions and to eliminate redundancy, and on the basis of this regression, a Gaussian mixture model is established to model the health state of batteries. Thus, the degree of lithium-ion battery performance degradation can be quantitatively assessed using the Bayesian inference-based distance metric. A case calculation experiment is carried out to verify the effectiveness of the method proposed in this paper. The experimental results demonstrate that, compared with other assessment methods, the performance degradation assessment method proposed in this paper can be used to monitor the degradation process of lithium-ion batteries more effectively and to improve the accuracy of condition monitoring of batteries, thereby providing powerful support for making maintenance decisions.

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

Research on Wireless Vibration Monitoring System for Devices in the Engine Room

2012 ◽  
Vol 503-504 ◽  
pp. 1651-1654
Author(s):  
Guo Yong Zhang ◽  
Shuo Wu
Keyword(s):  
Monitoring System ◽  
Vibration Sensor ◽  
Vibration Monitoring ◽  
Vibration State ◽  
Engine Room ◽  
On Line ◽  
The Common

The vibration can influence the running of devices in the engine room. It is necessary to monitor the vibration state of all running machineries. Through integrating the Bluetooth technology into the common vibration sensor, a wireless on-line vibration monitoring system is designed to monitor all devices. It will be helpful to avoid severe failure and improve the cruising ability.

Performance degradation analysis of solid lubricated bearings based on Laplace wavelet filter and self-organizing map

2013 ◽  
Vol 228 (10) ◽  
pp. 1743-1753 ◽  
Author(s):  
Chao Zhang ◽  
Shaoping Wang
Keyword(s):  
Feature Vector ◽  
Vibration Signal ◽  
Degradation Process ◽  
Performance Degradation ◽  
Friction Torque ◽  
Self Organizing Map ◽  
Wavelet Filter ◽  
Degradation Analysis ◽  
Space Mechanisms ◽  
Self Organizing

Solid lubricated bearings are commonly used in space mechanisms and other appliances, and their reliability analysis has drawn more and more attention. This paper focuses on the performance degradation analysis of solid lubricated bearings. Based on the vibration and friction torque signal of solid lubricated bearings, Laplace wavelet filter is adopted to process vibration signal and feature vector is constructed by calculating time-domain parameters of filtered vibration signal and original friction torque signal. Self-organizing map is then adopted to analyze the performance degradation based on extracted feature vectors. Experimental results show that this method can describe performance degradation process effectively.

A Kind of Vibration Sensor and Testing System in Electromechanical Equipment Based on Fiber Bragg Grating

2016 ◽  
Vol 693 ◽  
pp. 1300-1307
Author(s):  
Qi Jiang ◽  
Teng Yun Guo
Keyword(s):  
Fiber Bragg Grating ◽  
Monitoring System ◽  
Experimental Analysis ◽  
Mechanical Vibration ◽  
Vibration Sensor ◽  
Vibration Monitoring ◽  
Bragg Grating ◽  
Element Analysis ◽  
Finite Element Analysis Simulation ◽  
Electromechanical Equipment

Mechanical vibration analysis is an important index to measure the running state of the electromechanical equipment. The vibration signals contain the information about the equipment running state. This paper studies and designs the vibration monitoring system based on fiber Bragg grating (FBG). Through the finite element analysis simulation, optimizes the sensor's structure, and uses the labview software to compile the corresponding vibration monitoring analysis software. Finally verifies the detection effect of the monitoring system, through the pulse signal and continuous signal dynamic experimental analysis. The result of the experimental analysis shows: this vibration monitoring system can monitor the vibration information and analyze vibration state effectively. It has the advantages of reducing the temperature interference and lateral disturbance, and detects the vibration of three direction at the same time. So it is feasible to monitor the electromechanical equipment.

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.

Sign in / Sign up

Export Citation Format

Share Document