A Processing Method for Combined Fatigue Accelerated Test Data

2017 ◽  
Author(s):  
Songwang Zheng ◽  
Cao Chen ◽  
Lei Han ◽  
Xiaoyong Zhang ◽  
Xiaojun Yan
Keyword(s):  
Data Processing ◽  
Test Data ◽  
Turbine Blades ◽  
Processing Method ◽  
Test Time ◽  
Accelerated Test ◽  
The Real ◽  
Data Processing Method ◽  
Stress Levels ◽  
Vibration Stress

To carry out combined low and high cycle fatigue (CCF) test on turbine blades in a bench environment, it is imperative to simulate the vibration loads of turbine blades in the field. Due to the low vibration stress of turbine blades in the working state, the test time will be very long if the test vibration stress is equal to the real vibration stress in working state. Therefore, an accelerated test will be used when the test life reach the target value (typically 107). During the accelerated test, each blade is tested at two or more times than the real vibration stress. That means some specimens are tested under two vibration stress levels. In this case, a reasonable data processing method becomes very important. For this reason, a data processing method for the CCF accelerated test is proposed in this paper. These test data are iterated on the basis of S-N curve. Finally, ten real turbine blades are tested in a bench environment, one of them is tested under two vibration stress levels. The test data is processed using the method proposed above to obtain the unaccelerated life data.

Rail Damage Detection Based on AE Technology and Wavelet Data Processing

2015 ◽  
Vol 744-746 ◽  
pp. 1339-1343
Author(s):  
Yang Song ◽  
Fan Wu
Keyword(s):  
Data Processing ◽  
Damage Detection ◽  
Processing Method ◽  
The Real ◽  
Damage Location ◽  
Data Processing Method ◽  
Mallat Algorithm ◽  
Defect Recognition ◽  
Railway Operation ◽  
Non Destructive

In order to improve the accuracy and reliability of the defect recognition in rails, the new principle and methods of NDT should be explored, and the safety of railway operation and guiding the repair can be guaranteed. The study is aiming at comparing and analyzing the characteristics of destructive data signals with non-destructive ones. The damage and defect can be judged by the differences of the processed data in the frequency domain and energy spectrum. The location of the defect and damage can be obtained by the singularities of destructive signals using wavelet data processing method: continuous wavelet transform, Mallat algorithm and à Trous algorithm. Based on the above consequence, à Trous algorithm is found that its result is more similar to the real damage location, which proves that the method can be used in the real damage detection and provide us more precise defect location information for early warning.

PENGARUH ETIKA PEGAWAI, PELAYANAN PUBLIK DAN REFORMASI BIROKRASI TERHADAP PENERAPAN ZONA INTEGRITAS PADA LAPAS MAKASSAR

2020 ◽  
Vol 2 (1) ◽  
pp. 13-15
Author(s):  
Adi Sucipto ◽  
Hasanuddin Remmang ◽  
Haeruddin Saleh
Keyword(s):  
Regression Analysis ◽  
Data Processing ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Random Sampling ◽  
Public Services ◽  
Simple Random Sampling ◽  
Processing Method ◽  
Data Processing Method

Penelitian ini bertujuan menguji pengaruh Etika Pegawai, Pelayanan Publik dan Reformasi Birokrasi terhadap Penerapan Zona Integritas. Pengaruh Etika Pegawai, Pelayanan Publik dan Reformasi Birokrasi terhadap Penerapan Zona Integritas pada Lembaga Pemasyarakatan Kelas I Makassar Responden dalam penelitian ini adalah Pengunjung dan keluarga nara-pidana Lembaga Pemasyarakatan Kelas I Makassar. Jumlah pengunjung yang menjadi sampel penelitian ini adalah 55 orang. Metode penentuan sampel yang digunakan dalam penelitian ini adalah Simple Random Sampling, sedangkan metode pengolahan data yang digunakan peneliti adalah analisis regresi berganda. Hasil penelitian ini menunjukkan bahwa Etika Pegawai dan Pelayanan Publik berpengaruh signifikan terhadap Penerapan Zona Integritas di Lembaga Pemasyarakatan Kelas I Makassar.     This study examines the effect of employee ethics and the improvement of public services on the implementation of the integrity zone. The effect of employee ethics, and improvement of public services on the implementation of integrity zone on Lembaga Pemasyarakatan Kelas 1 Makassar. Respondents in this study were Makassar class in penitentiary visitors. the number of visitors who sampled this study was 55 people. the method of determining the sample used in this study is simple random sampling, while the data processing method used by researchers is multiple regression analysis. the results of this study indicate that employee ethics and public services have a significant effect on the implementation of the integrity zone in Makassar class in penitentiary.

EAST-NBI experimental data processing method based on improved OPTICS algorithm

2021 ◽  
Vol 172 ◽  
pp. 112737
Author(s):  
Jinxin Wang ◽  
Zhimin Liu ◽  
Yuanzhe Zhao ◽  
Yahong Xie ◽  
Yuanlai Xie
Keyword(s):  
Experimental Data ◽  
Data Processing ◽  
Processing Method ◽  
Experimental Data Processing ◽  
Data Processing Method

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

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