New Method Using Piezoelectric Jerk Sensor to Detect Roller Bearing Failure

2013 ◽  
Vol 7 (5) ◽  
pp. 550-557 ◽  
Author(s):  
Nobuhiko Henmi ◽  
◽  
Shingo Takeuchi
Keyword(s):  
Rotation Speed ◽  
Roller Bearing ◽  
New Method ◽  
Bearing Failure ◽  
Acceleration Sensor ◽  
Damage Diagnosis ◽  
Vibration Detection ◽  
Diagnosis Method ◽  
Bearing Damage ◽  
Conical Roller

An acceleration sensor is usually used to examine for roller bearing damage. It is difficult, however, to detect abnormal vibration and examine for roller bearing damage when rotation speed is low. The final target of this study is to establish a bearing damage diagnosis system based on the piezoelectric jerk sensor we developed, which can be used for both low- and highspeed rotations. For this purpose, this paper aims to identify the features of an abnormal vibration detection signal at a low rotation speed, propose a new roller bearing damage diagnosis method that uses the features, and clarify the validity of the method. Experiments are conducted to analyze a scratch purposely made on the outer ring of a conical roller bearing that rotates at the low speeds of 10 or 40 rpm. The results verify the advantages of using the jerk sensor for the bearing damage diagnosis and the validity of the method proposed in this paper.

On-site monitoring of bearing failure in composite bolted joints using built-in eddy current sensing film

2020 ◽  
pp. 002199832097973
Author(s):  
Qijian Liu ◽  
Hu Sun ◽  
Yuan Chai ◽  
Jianjian Zhu ◽  
Tao Wang ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Eddy Current ◽  
Failure Modes ◽  
Bolted Joints ◽  
Bearing Failure ◽  
Monitoring Method ◽  
Current Sensing ◽  
Site Monitoring ◽  
Array Sensing ◽  
Bearing Damage

Bearing damage is one of the common failure modes in composite bolted joints. This paper describes the development of an on-site monitoring method based on eddy current (EC) sensing film to monitor the bearing damage in carbon fiber reinforced plastic (CFRP) single-lap bolted joints under tensile testing. Configuration design and operating principles of EC array sensing film are demonstrated. A series of numerical simulations are conducted to analyze the variation of EC when the bearing failure occurs around the bolt hole. The results of damage detection in the horizontal direction and through the thickness direction in the bolt hole with different exciting current directions are presented by the finite element method (FEM). Experiments are performed to prove the feasibility of the proposed EC array sensing film when the bearing failure occurs in CFRP single-lap bolted joints. The results of numerical simulations and experiments indicate that bearing failure can be detected according to the variation of EC in the test specimen.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

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.

On the Detectability of Roller Bearing Damage by Frequency Analysis

1993 ◽  
Vol 207 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Y-T Su ◽  
Y-T Sheen
Keyword(s):  
Frequency Analysis ◽  
Roller Bearing ◽  
Vibration Spectra ◽  
Frequency Information ◽  
Roller Bearings ◽  
Bearing Damage

This study investigates the detectability of roller bearing damage by the frequency analysis of bearing vibrations. The magnitude characteristics of peaks in the vibration spectra are analysed. It is shown that the frequency information of vibration spectra for undamaged roller bearings can be the same as that for damaged ones. However, the magnitude information of vibration spectra for undamaged roller bearings is different from that for damaged ones. Thus, to detect the initial fault of roller bearing reliably, both the frequency information and magnitude information of vibration spectra have to be used.

The Research of Fault Diagnosis Method of Roller Bearing Based on EMD and VPMCD

2014 ◽  
Vol 1014 ◽  
pp. 505-509 ◽  
Author(s):  
Ran Tao ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Shu Guo ◽  
Kun Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Classification Performance ◽  
Fault Models ◽  
Time Frequency ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Nonlinear Signal ◽  
Stable Classification

Empirical mode decomposition (EMD) can extract real time-frequency characteristics from the non-stationary and nonlinear signal. Variable prediction model based class discriminate (VPMCD) is introduced into roller bearing fault diagnosis in this paper. Therefore, a fault diagnosis method based on EMD and VPMCD is put forward in the paper. Firstly, the different feature vectors in the signal are extracted by EMD. Then, different fault models of roller bearing are distinguished by using VPMCD. Finally, an simulation example based on EMD and VPMCD is shown in this paper. The results show that this method can gain very stable classification performance and good computational efficiency.

Fault Diagnosis of Wind Turbines Based on Noise Detection

2013 ◽  
Vol 718-720 ◽  
pp. 405-408
Author(s):  
Jing Cheng ◽  
Wei Qing Wang ◽  
Shan He
Keyword(s):  
Fault Diagnosis ◽  
Engineering Application ◽  
Noise Measurement ◽  
Measurement Technology ◽  
Noise Detection ◽  
Vibration Detection ◽  
Technology Standard ◽  
Testing Technology ◽  
Diagnosis Method ◽  
Detecting Method

Aiming at backward current situation of testing technology and fault diagnosis technology of wind power generation in China, a fault diagnosis method based on based on noise detection is put forward. Studied IEC 61400-11 noise measurement technology standard, this paper elaborates the noise detecting method, analyzes the feasibility and diagnostic steps of fault diagnosis, proposes fault signal extracting method based on wavelet analysis. According to analysis and simulation, it is shown that noise measurement is earlier than vibration detection, and the fault signal can be extracted effectively, so it has important value for engineering application.

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