scholarly journals Performance Degradation Assessment of Rotary Machinery Based on a Multiscale Tsallis Permutation Entropy Method

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yong Chen ◽  
Mian Jiang ◽  
Kuanfang He
Keyword(s):  
Working Conditions ◽  
Initial Point ◽  
Health Indicators ◽  
Rolling Bearing ◽  
Health Condition ◽  
Entropy Method ◽  
Permutation Entropy ◽  
Rotary Machinery ◽  
The Status ◽  
Alarm Threshold

Methods based on vibration analysis are currently regarded as the most conclusive means for fault diagnosis and health prognostics in rotary machinery. However, changing working conditions mean that the vibration signals originating from rotary machinery exhibit different levels of complexity. This complexity leads to increased difficulty in constructing health indicators (HIs). In this paper, we propose a multiscale Tsallis permutation entropy (MTPE) to construct the HIs of rotary machinery under different working conditions. MTPE values are a function of an entropy index and scale, which have the universality for handling the complexity of a permutated time series. The health condition of the rotary machinery was effectively represented by the MTPEs in conditional monitoring; the initial point of the unhealthy stage was found using the 3 σ interval. This was set as the alarm threshold according to the varying HI trend. Once this was established, dividing the stages into two-stage health stages (HS) was straightforward. Using a rolling bearing, a run-to-failure experiment was conducted and results suggested that the proposed method effectively assessed the status of the rotary machinery. Taken together, this study provided a novel complexity measure based on a methodology for constructing the HIs of rotary machinery and enriches conditional monitoring theory.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder

2019 ◽  
Vol 9 (13) ◽  
pp. 2743 ◽  
Author(s):  
Dai ◽  
Tang ◽  
Shao ◽  
Huang ◽  
Wang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Health Condition ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Diagnostic Model ◽  
Denoising Autoencoder ◽  
Essential Information ◽  
Mode Function

Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

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.

Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

2014 ◽  
Vol 229 (10) ◽  
pp. 1887-1903 ◽  
Author(s):  
Ying Zhang ◽  
Hongfu Zuo ◽  
Fang Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Quantitative Indicator ◽  
Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

scholarly journals Research on Rolling Bearing Fault Feature Extraction and Diagnosis Method Based on Image Processing

2021 ◽  
Vol 5 (2) ◽  
pp. 1-7
Author(s):  
Sun Y
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Working Environment ◽  
Ring Rolling ◽  
Rolling Bearings ◽  
Binary Matrix ◽  
The Time Domain

In economic construction, there are many large and important machinery and equipment. Some equipment will continue to work in a harsh working environment, so many and various failures will occur. Rolling bearings are one of the widely used parts in rotating machinery. They are generally composed of inner ring, outer ring, rolling element and holding. The frame is composed of four parts, the failure of the bearing is particularly important, and its safe operation has a vital impact on the entire equipment, Feature extraction is the key link in the subsequent identification of fault types, Although feature extraction in the time domain and frequency domain is effective, it is also necessary to find new feature extraction methods in new areas. On the basis of the snowflake image obtained by using the principle of SDP(Symmetrized Dot Pattern), a method for extracting fault features of rolling bearings based on image processing is proposed, and the snowflake standard map for different working conditions is constructed. The number of snowflake images under different working conditions is different. The binary matrix of the test image is compared with it, and then classified and identified. Finally, the algorithm is validated, and the ideal result is obtained to verify its rationality and effectiveness.

scholarly journals Severity assessment of single-dose Oxford-AstraZeneca vaccinated individuals infected with severe acute respiratory syndrome coronavirus 2 in Southeast Bangladesh

2021 ◽  
pp. 220-226
Author(s):  
Eaftekhar Ahmed Rana ◽  
Pronesh Dutta ◽  
Md. Sirazul Islam ◽  
Tanvir Ahmad Nizami ◽  
Tridip Das ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Quantitative Polymerase Chain Reaction ◽  
Health Indicators ◽  
Good Health ◽  
Health Condition ◽  
Chain Reaction ◽  
Infection Period ◽  
Respiratory Problems ◽  
Polymerase Chain ◽  
Taste And Smell

Background and Aim: A vaccine program for coronavirus illness (coronavirus disease [COVID-19]) is currently underway in numerous regions of the world, including Bangladesh, but no health data on those who have been vaccinated are available at this time. The study aimed to investigate the health condition of people who had received their first dose of the Oxford- AstraZeneca vaccine and were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Materials and Methods: To detect SARS-CoV-2, a standard virological approach, real-time reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR), was used. Several health indicators from vaccinated patients were collected using pre-structured questionnaires during the infection phase. Results: A total of 6146 suspicious samples were analyzed, and 1752 were found to be positive for SARS-CoV-2, with 200 people receiving the first dose of the COVID-19 vaccine. One hundred and sixty-five (82.5%) were not hospitalized among the vaccinated people, and 177 (88.5%) did not have any respiratory problems. Only 8% of patients required further oxygen support, and 199 (99.5%) did not require intensive care unit intervention. Overall, oxygen saturation was recorded at around 96.8% and respiratory difficulties did not extend more than 5 days during the infection period. Among the vaccinated COVID-19-positive people, 113 (56.5%) and 111 (55.5%) had typical physiological taste and smell. Surprisingly, 129 (64.5%) people had diverse comorbidities, with high blood pressure (27.9%) and diabetes (32 [24.8%]) being the most common. The major conclusion of the current study was that 199 (99.5%) of vaccinated patients survived in good health and tested negative for RT-qPCR. Conclusion: According to the findings of this study, administering the first dose of the Oxford-AstraZeneca vaccine considerably reduces health risks during the COVID-19 infection period.

scholarly journals A Fault Diagnosis Method of Rolling Bearing Integrated with Cooperative Energy Feature Extraction and Improved Least-Squares Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Xu ◽  
Darong Huang ◽  
Tang Min ◽  
Yunhui Ou
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Support Vector ◽  
Swarm Optimization ◽  
Energy Characteristics ◽  
Wavelet Energy ◽  
Energy Feature

To solve the problem that the bearing fault of variable working conditions is challenging to identify and classify in the industrial field, this paper proposes a new method based on optimization of multidimension fault energy characteristics and integrates with an improved least-squares support vector machine (LSSVM). First, because the traditional wavelet energy feature is difficult to effectively reflect the characteristics of rolling bearing under different working conditions, based on analyzing the wavelet energy feature extraction in detail, a collaborative method of multidimension fault energy feature extraction combined with the method of Transfer Component Analysis (TCA) is constructed, which improves the discrimination between the different features and the compactness between the same features of rolling bearing faults. Then, for solving the problem of the local optimal of particle swarm optimization (PSO) in fault diagnosis and recognition of rolling bearing, an improved LSSVM based on particle swarm optimization and wavelet mutation optimization is established to realize the collaborative optimization and adjustment of LSSVM dynamic parameters. Based on the improved LSSVM and optimization of multidimensional energy characteristics, a new method for fault diagnosis of rolling bearing is designed. Finally, the simulation and analysis of the proposed algorithm are verified by the experimental data of different working conditions. The experimental results show that this method can effectively extract the multidimensional fault characteristics under variable working conditions and has a high fault recognition rate.

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