scholarly journals Stepwise Intelligent Diagnosis Method for Rotor System with Sliding Bearing Based on Statistical Filter and Stacked Auto-Encoder

2020 ◽  
Vol 10 (7) ◽  
pp. 2477
Author(s):  
Haihong Tang ◽  
Zhiqiang Liao ◽  
Yayoi Ozaki ◽  
Peng Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Binary Classification ◽  
Health Condition ◽  
Rotor System ◽  
Sliding Bearing ◽  
Intelligent Diagnosis ◽  
Fault Features ◽  
Oil Whirl ◽  
Diagnosis Method ◽  
The Relationship

Since the raw signal collected from the sliding bearing is contaminated with background noise, and it is difficult to obtain high-precision results for the traditional methods due to the low signal-to-noise ratio (SNR). Therefore, a stepwise intelligent diagnosis method based on statistical filter and stacked auto-encoder (SAE) that is established with several auto-encoders is proposed to identify several faults of sliding bearing in a rotor system. Firstly, the statistical filter is utilized to reduce the interference information for the different abnormal states and to increase the SNR. Secondly, the stepwise intelligent diagnosis based on SAE is performed to learn the useful fault features, and it can automatically complete the fault diagnosis which is contributed with the superiority binary classification to fully mine the relationship between the fault characteristics and the health condition of bearing. Finally, the diagnosis of the oil whirl and structural faults in a rotor system is cited as an example to demonstrate the effectiveness of proposed method. It can effectively illustrate the advantages of the stepwise diagnosis method to obtain the maximum diagnostic accuracy.

The Demodulating Principle of Autocorrelation-Envelope and its Application to Gearbox Diagnosis

2013 ◽  
Vol 380-384 ◽  
pp. 1029-1034 ◽  
Author(s):  
Zhan Dong Bi ◽  
Yong Chen ◽  
Zhi Zhao Peng ◽  
Yu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Modulation Frequency ◽  
Vibration Signal ◽  
Health Condition ◽  
Vibration Signals ◽  
Fault Features ◽  
Diagnosis Method ◽  
Simulation Results ◽  
Modulated Signals ◽  
Periodic Components

As the most important transmission system of vehicles, the gearbox has a high fault rate, so it is meaningful to evaluate and diagnose its health condition and faults accurately. Autocorrelation -envelope analysis is a fault diagnosis method that can suppress the noise and reserve the periodic components of vibration signals. A conclusion has been deduced: amplitude modulated, frequency modulated, or amplitude& frequency modulated signals can be transformed into amplitude modulated signals with the same modulation frequency through autocorrelation processing. Therefore, the aucorrelation-envelope technique is suitable for extracting the fault features of gearbox from its vibration signal with the coexistence of amplitude modulation and frequency modulation. The simulation results verify the validity of the conclusion and the experiment of vehicle gearbox diagnosis indicates the effectiveness of this method.

scholarly journals Exploring Employment Trend and Relationship With Individual and Family-Related Factors in 65 or Older Koreans

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 65-65
Author(s):  
Yeonji Ryou ◽  
Ryou Yeonji
Keyword(s):  
Employment Status ◽  
Secondary Data ◽  
Educational Background ◽  
Health Condition ◽  
Related Factors ◽  
The People ◽  
Close Relationship ◽  
Working People ◽  
The Status ◽  
The Relationship

Abstract The purpose of this study is to identify the trend of the employment status in 65 years or older adults who reside in South Korea and to explore the relationship between the status of employment and individual and family-related factors. This study utilized 10-year and 6-wave secondary data from the Korean Longitudinal Study of Ageing (KLoSA). The original panel sample is a random sample of 10,254 adults who are 45 or older, but for the aim of this study, the participants younger than 65 years were excluded. The number of samples in each wave is different, ranging from 4,013 to 4,335 due to the death of the participant, the rejection of additional interviews, and the refreshment participant collected in Wave 5. The findings indicate that the absolute employment of the people aged 65 or older and the proportion of working people among those have increased over the past decade. In this study, it is also found that there is a close relationship between employment status and individual factors such as gender, educational background, health condition, region, etc. Moreover, the results suggest that there are various facets of the relationship between employment status and family-related factors including whether living with children, the number of the member whom I help with daily activities, the total amount of financial support from/to children/parents/other family or whether participating social activities, etc. The implications of the need for employing the older population and the consideration family-related factors in the policy-making process in Korea are discussed.

scholarly journals Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3510 ◽  
Author(s):  
Zhijian Wang ◽  
Junyuan Wang ◽  
Wenhua Du
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Optimal Number ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Multiple Fault ◽  
Mode Decomposition ◽  
Fault Features ◽  
Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

scholarly journals Inferring health conditions from fMRI-graph data

2018 ◽  
Author(s):  
PierGianLuca Porta Mana ◽  
Claudia Bachmann ◽  
Abigail Morrison
Keyword(s):  
Binary Classification ◽  
Disease Diagnosis ◽  
Health Condition ◽  
Automated Classification ◽  
Imaging Data ◽  
Data Set ◽  
Sufficient Statistics ◽  
Conjugate Priors ◽  
Healthy Control ◽  
Partial Exchangeability

Automated classification methods for disease diagnosis are currently in the limelight, especially for imaging data. Classification does not fully meet a clinician's needs, however: in order to combine the results of multiple tests and decide on a course of treatment, a clinician needs the likelihood of a given health condition rather than binary classification yielded by such methods. We illustrate how likelihoods can be derived step by step from first principles and approximations, and how they can be assessed and selected, using fMRI data from a publicly available data set containing schizophrenic and healthy control subjects, as a working example. We start from the basic assumption of partial exchangeability, and then the notion of sufficient statistics and the "method of translation" (Edgeworth, 1898) combined with conjugate priors. This method can be used to construct a likelihood that can be used to compare different data-reduction algorithms. Despite the simplifications and possibly unrealistic assumptions used to illustrate the method, we obtain classification results comparable to previous, more realistic studies about schizophrenia, whilst yielding likelihoods that can naturally be combined with the results of other diagnostic tests.

Fault Diagnosis Method Research of Avionics Systems Based on Testing

2014 ◽  
Vol 519-520 ◽  
pp. 1149-1154
Author(s):  
Wen Jun Zhao
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Diagnostic Testing ◽  
Automatic Test Equipment ◽  
Testing Strategy ◽  
Automatic Test ◽  
Location Accuracy ◽  
Diagnosis Method ◽  
Diagnosis Model ◽  
The Relationship

As for this problem that the equipment/devices maintenance and troubleshooting of new avionics systems is very difficult, the fault Diagnosis Method based on testing is proposed. This method is used to build fault diagnosis model and generate diagnostic testing strategy by establishing the relationship between the fault and test, and then the automatic test equipment is used to test for fault under the reasoning of the diagnosis inference, finally, fault conclusions are drawn. Application shows that this method is feasible, fault location accuracy is high and application prospect is broad.

The Application of Improved SVM in Mixed Circuit

2012 ◽  
Vol 224 ◽  
pp. 493-496 ◽  
Author(s):  
Huai Long Wang ◽  
Qiang Pan ◽  
Hong Liu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Small Samples ◽  
Lifting Wavelet Transform ◽  
Fault Features ◽  
Diagnosis Method ◽  
Lifting Wavelet ◽  
Current Characteristics ◽  
Mixed Circuit ◽  
Better Than

In order to improve the speed and the rate of fault diagnosis in mixed circuit, this paper introduces a new fault diagnosis method. Through extracting fault features of current characteristics effectively and applying to Improved SVM, the ability of pattern recognition will be better than the traditional BP Neural Network and Single SVM, especially in small samples or non-linear cases. Meanwhile, this paper presents the lifting wavelet transform in order to obtain the feature information accurately. The accuracy of fault diagnosis can greatly enhance by discussing the Improved SVM combined with lifting wavelet transform in a specific monostable trigger. That points out a new direction for the fault diagnosis of mixed circuit.

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