scholarly journals Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 291
Author(s):  
Jakub Jakubowski ◽  
Przemysław Stanisz ◽  
Szymon Bobek ◽  
Grzegorz J. Nalepa
Keyword(s):  
Anomaly Detection ◽  
Degradation Process ◽  
Data Driven ◽  
Hot Strip Mill ◽  
Data Set ◽  
Turbofan Engine ◽  
Steel Making ◽  
Hot Strip ◽  
Variational Autoencoder ◽  
Key Aspects

Development of predictive maintenance (PdM) solutions is one of the key aspects of Industry 4.0. In recent years, more attention has been paid to data-driven techniques, which use machine learning to monitor the health of an industrial asset. The major issue in the implementation of PdM models is a lack of good quality labelled data. In the paper we present how unsupervised learning using a variational autoencoder may be used to monitor the wear of rolls in a hot strip mill, a part of a steel-making site. As an additional benchmark we use a simulated turbofan engine data set provided by NASA. We also use explainability methods in order to understand the model’s predictions. The results show that the variational autoencoder slightly outperforms the base autoencoder architecture in anomaly detection tasks. However, its performance on the real use-case does not make it a production-ready solution for industry and should be a matter of further research. Furthermore, the information obtained from the explainability model can increase the reliability of the proposed artificial intelligence-based solution.

Temperature Prediction for Finish Entry of Hot Strip Mill Based on Data-driven

2020 ◽  
Author(s):  
Hu Bo ◽  
Yongjun Zhang ◽  
Lu sihan ◽  
Zhang Fei ◽  
Qiang Guo ◽  
...  
Keyword(s):  
Data Driven ◽  
Hot Strip Mill ◽  
Temperature Prediction ◽  
Hot Strip

Modernization of the hot strip mill at Kobe Steel

1990 ◽  
Vol 87 (1) ◽  
pp. 79-88
Author(s):  
K. Hirata ◽  
Y. Yamamoto ◽  
Y. Ohiké ◽  
J. Sato ◽  
S. Honda ◽  
...  
Keyword(s):  
Hot Strip Mill ◽  
Hot Strip

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

Precipitation Upon Hot Strip Mill Conditions of a Ti-IF Steel

2001 ◽  
Vol 7 (S2) ◽  
pp. 508-509
Author(s):  
W. Regone ◽  
A. M. 𝚓orge Júnior ◽  
O. Balancin
Keyword(s):  
Hot Strip Mill ◽  
Interstitial Free ◽  
If Steel ◽  
Precipitation Sequence ◽  
Hexagonal Crystal Structure ◽  
Hexagonal Crystal ◽  
Hot Strip ◽  
Hot Torsion ◽  
And Diffusion ◽  
Interstitial Elements

Upon hot strip mill of titanium Interstitial Free (IF) steels, during cooling from austenite to ferrite region, the level of interstitial elements not removed by steelmaking process is dropped down by Ti that combines with N, C and S. Some authors [1-3] have reported that the traditional precipitation sequence TiN, TiS, Ti4C2S2 and TiC occurs with freestanding particles formed by nucleation and growth processes. Other authors [4] have indicated that the transformation from TiS to Ti4C2S2 may be considered as a hybrid of shear and diffusion, i.e., faulted Ti8S9 (9R) + 10[Ti] + 9[C] → 41/2Ti4C2S2 (or H for its hexagonal crystal structure). At low temperature (≤930°C), the stabilization process continues through epitaxial growth of carbides on H phase. to study the evolution of precipitation upon hot strip mill conditions, samples of a Ti - IF steel were subjected to double straining tests [5] by means of a computerized hot torsion machine, at 1000 °C and 920 °C, with strain rate of 1 s-1 and interpass times ranging from 0.5 to 100 s.

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

scholarly journals Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

2021 ◽  
Vol 11 (11) ◽  
pp. 4773
Author(s):  
Qiaoping Tian ◽  
Honglei Wang
Keyword(s):  
Quantile Regression ◽  
Probability Density ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Health State ◽  
Rolling Bearings ◽  
Data Set ◽  
Time Frequency ◽  
Useful Life ◽  
Degradation Indicator

High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

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