scholarly journals Development of an estimation-experimental method for estimating the preservation indicators of single-use articles

2021 ◽  
Vol 6 (3 (114)) ◽  
pp. 18-35
Author(s):  
Boris Lanetskii ◽  
Vadym Lukianchuk ◽  
Igor Koval ◽  
Hennadii Khudov ◽  
Andrii Hordiienko ◽  
...  
Keyword(s):  
Distribution Function ◽  
Experimental Method ◽  
Continuous Monitoring ◽  
Parametric Estimation ◽  
Generation Model ◽  
Data Generation ◽  
Censored Samples ◽  
Operational Tests ◽  
Single Use ◽  
Non Parametric Estimation

To manage the operation of modern single-use products, it is necessary to evaluate their preservation indicators as uncontrolled, non-repairable, and maintenance-free objects. Data for assessing its parameters are considered as one-time censored samples with continuous monitoring, which does not correspond to the mode of storage of products during operation. Under the conditions of limited volumes of censored samples, it is problematic to identify the parametric model of persistence. To solve this problem, a non-parametric estimation-experimental method has been devised, which is a set of models for data generation, estimation of the function of the distribution of the preservation period and preservation indicators. The data generation model is represented by a scheme of operational tests and analytical relationships between the quantities of tested and failed articles. The model of estimating the distribution function describes the process of its construction on the generated data. Models for estimating preservation indicators are represented by ratios for their point and interval estimates, as functionals from the restored distribution function. Unlike the well-known ones, the developed method implements the assessment of indicators under the conditions of combined censorship. The method can be used to assess the preservation indicators of single-use articles with an error of at least 7 %. At the same time, their lower confidence limits are estimated at 0.9 with an error not worse than 14 % with a censorship degree of not more than 0.23. The restored distribution function agrees well (reliability 0.9, error 0.1) with the actual persistence of articles with censorship degrees of not more than 0.73, which is acceptable for solving the problems of managing their operation.

Intrusion detection of railway clearance from infrared images using generative adversarial networks

2020 ◽  
pp. 1-13
Author(s):  
Yundong Li ◽  
Yi Liu ◽  
Han Dong ◽  
Wei Hu ◽  
Chen Lin
Keyword(s):  
Intrusion Detection ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Single Shot ◽  
Data Generation ◽  
Infrared Images ◽  
Adversarial Networks ◽  
Training Samples ◽  
Rgb Images

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.

scholarly journals A Time-Series Data Generation Method to Predict Remaining Useful Life

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1115
Author(s):  
Gilseung Ahn ◽  
Hyungseok Yun ◽  
Sun Hur ◽  
Si-Yeong Lim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Series Data ◽  
Generation Model ◽  
Data Generation ◽  
Training Time ◽  
Symbolic Aggregate Approximation ◽  
Useful Life ◽  
Occurrence Patterns

Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model.

Non-parametric estimation for aggregated functional data for electric load monitoring

2009 ◽  
Vol 20 (2) ◽  
pp. 111-130 ◽  
Author(s):  
Ronaldo Dias ◽  
Nancy L. Garcia ◽  
Angelo Martarelli
Keyword(s):  
Functional Data ◽  
Parametric Estimation ◽  
Electric Load ◽  
Load Monitoring ◽  
Non Parametric Estimation ◽  
Non Parametric
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