On-Line Monitoring and Warning of Important In-Service Pressure Equipment Based on Characteristic Safety Parameters

2017 ◽  
Author(s):  
Xuedong Chen ◽  
Tiecheng Yang ◽  
Zhichao Fan ◽  
Yunrong Lv
Keyword(s):  
Big Data ◽  
Real Time ◽  
Failure Mechanism ◽  
Failure Modes ◽  
Structural Integrity ◽  
Safety Margin ◽  
Measurement Monitoring ◽  
Safety Parameters ◽  
Safety Parameter ◽  
Definition Of

Characteristic safety parameter refers to the parameter that reflects the inherent safety margin of pressure equipments subjected to certain failure mechanism. It has three main characteristics. Firstly, it is sensitive to the change in failure mechanism. Secondly, the safety of pressure equipments can be guaranteed by controlling this parameter. Thirdly, it is easy to measure. By real-time monitoring of this characteristic safety parameter, the quantitative assessment of the structural integrity and furhter the diagnosis and warning on the safety of in-service pressure equipments can be realized. In this paper, the definition of characteristic safety parameter is given first for the pressure equipments subjected to several typical failure modes. After that, the selection principle, measurement technique and determination of its critical value, etc., are then introduced by analyzing typical examples. In combination with the technical concepts of the Internet of Things and Big-Data, some research suggestions are proposed with respect to the remote monitoring and diagnosis techniques based on the characteristic safety parameter, including the sensing measurement, monitoring and analysis of big data, real-time diagnosis and early warning of safety condition, etc.

scholarly journals Health and Structural Integrity of Monitoring Systems: The Case Study of Pressurized Pipelines

2020 ◽  
Vol 10 (17) ◽  
pp. 6023
Author(s):  
Vladimír Chmelko ◽  
Martin Garan ◽  
Miroslav Šulko ◽  
Marek Gašparík
Keyword(s):  
Real Time ◽  
Structural Integrity ◽  
Human Life ◽  
Cost Effective ◽  
Monitoring Systems ◽  
Long Run ◽  
Operational Safety ◽  
Pipeline Systems ◽  
Safety Parameters ◽  
High Pressure Pipeline

In the operation of some structures, particularly in energy or chemical industry where pressurized pipeline systems are employed, certain unexpected critical situations may occur, which must be definitely avoided. Otherwise, such situations would result in undesirable damage to the environment or even the endangerment of human life. For example, the occurrence of such nonstandard states can significantly affect the safety of high-pressure pipeline systems. The following paper discusses basic physical prerequisites for assembling the systems that can sense loading states and monitor the operational safety conditions of pressure piping systems in the long-run. The appropriate monitoring system hardware with cost-effective data management was designed in order to enable the real-time monitoring of operational safety parameters. Furthermore, the paper presents the results obtained from the measurements of existing real-time safety monitoring systems for selected pipeline systems.

scholarly journals MODELING OF LARGE VOLUMES OF DATA WITH THE USE OF NoSQL

2020 ◽  
Vol 69 (1) ◽  
pp. 323-326
Author(s):  
N.B. Zhapsarbek ◽  
Keyword(s):  
Big Data ◽  
Real Time ◽  
High Speed ◽  
Relational Databases ◽  
Accumulation Rate ◽  
Structured Data ◽  
Modern World ◽  
High Accumulation ◽  
Real Time Processing ◽  
Definition Of

In the modern world, specialists and the information systems they create are increasingly faced with the need to store, process and move huge amounts of data. The definition of large amounts of data, Big Data, is used to denote technologies such as storing and analyzing large amounts of data that require high speed and real-time decision making during processing. In this case, large volumes, high accumulation rate, and the lack of a strict internal structure of "big data" are considered. All of this also means that classic relational databases are not well suited for storing them. In this article, we showed solutions for processing large amounts of data for pharmacy chains using NoSQL. This paper presents technologies for modeling large amounts of data using NoSQL, including MongoDB, and also analyzes possible solutions, limitations that do not allow this to be done effectively. This article provides an overview of three modern approaches to working with big data: NoSQL, DataMining and real-time processing of event flows. In this article, as an implementation of the studied methods and technology, we consider a database of pharmacies for processing, searching, analyzing, forecasting big data. Also, when using NoSQL, we showed work with structured and poorly structured data in parallel in different aspects and showed a comparative analysis of the newly developed application for pharmacy workers.

Quantitative Decisions Relative to Structural Integrity

1980 ◽  
Vol 102 (1) ◽  
pp. 56-63 ◽  
Author(s):  
C. A. Rau ◽  
P. M. Besuner
Keyword(s):  
Failure Modes ◽  
Structural Integrity ◽  
Product Liability ◽  
Turbine Rotor ◽  
Recent Developments ◽  
Quantitative Basis ◽  
Strong Motivation ◽  
Definition Of ◽  
And Control ◽  
Liability Claims

The injury potential and increased cost of licensing, insurance premiums, product liability claims, and field repairs or recalls provide strong motivation to quantitatively evaluate and control the risk of various products. Risk analysis involves the definition of the probable failure modes and the assessment of failure probability, failure severity and the corresponding risks and costs. Basic concepts are reviewed and recent developments in methods to quantify the risk of structural failure when limited failure experience is available are presented. An example involving a turbine rotor is described which illustrates how the conventional and, new methods provide a quantitative basis for assessing structural integrity and risk and for making decisions regarding future operation, repair, or replacement.

A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

1995 ◽  
Vol 34 (05) ◽  
pp. 475-488
Author(s):  
B. Seroussi ◽  
J. F. Boisvieux ◽  
V. Morice
Keyword(s):  
Real Time ◽  
Linear Time ◽  
Treatment Plan ◽  
Control Architecture ◽  
Real Time Control ◽  
Time Representation ◽  
Time Control ◽  
Continuous Support ◽  
Definition Of ◽  
Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

Social Failure Modes in Technology and the Ethics of AI

2020 ◽  
pp. 441-461
Author(s):  
Jason Millar
Keyword(s):  
Engineering Design ◽  
Failure Modes ◽  
Design Practice ◽  
Detailed Understanding ◽  
Ethical Implications ◽  
The Social ◽  
Underlying Causes ◽  
Physical Defects ◽  
Definition Of

This chapter argues that, just as technological artefacts can break as a result of mechanical, electrical, or other physical defects not fully accounted for in their design, they can also break as a result of social defects not fully accounted for in their design. These failures resulting from social defects can be called social failures. The chapter then proposes a definition of social failure as well as a taxonomy of social failure modes—the underlying causes that lead to social failures. An explicit and detailed understanding of social failure modes, if properly applied in engineering design practice, could result in a fuller evaluation of the social and ethical implications of technology, either during the upstream design and engineering phases of a product, or after its release. Ideally, studying social failure modes will improve people’s ability to anticipate and reduce the rate or severity of undesirable social failures prior to releasing technology into the wild.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

Design and development of a backstepping controller autopilot for fixed-wing UAVs

2021 ◽  
pp. 1-27
Author(s):  
D. Sartori ◽  
F. Quagliotti ◽  
M.J. Rutherford ◽  
K.P. Valavanis
Keyword(s):  
Real Time ◽  
Control Law ◽  
Parametric Uncertainties ◽  
Hardware In The Loop ◽  
Aircraft Control ◽  
Backstepping Approach ◽  
Small Uav ◽  
Backstepping Controller ◽  
Definition Of ◽  
Performance Results

Abstract Backstepping represents a promising control law for fixed-wing Unmanned Aerial Vehicles (UAVs). Its non-linearity and its adaptation capabilities guarantee adequate control performance over the whole flight envelope, even when the aircraft model is affected by parametric uncertainties. In the literature, several works apply backstepping controllers to various aspects of fixed-wing UAV flight. Unfortunately, many of them have not been implemented in a real-time controller, and only few attempt simultaneous longitudinal and lateral–directional aircraft control. In this paper, an existing backstepping approach able to control longitudinal and lateral–directional motions is adapted for the definition of a control strategy suitable for small UAV autopilots. Rapidly changing inner-loop variables are controlled with non-adaptive backstepping, while slower outer loop navigation variables are Proportional–Integral–Derivative (PID) controlled. The controller is evaluated through numerical simulations for two very diverse fixed-wing aircraft performing complex manoeuvres. The controller behaviour with model parametric uncertainties or in presence of noise is also tested. The performance results of a real-time implementation on a microcontroller are evaluated through hardware-in-the-loop simulation.

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