Real-time Reliability Research Articles

A Design of FPGA-Based Neural Network PID Controller for Motion Control System.

In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority.

Research on a Real-time Reliability Evaluation Method Integrated with Online Fault Diagnosis: Subsea All-electric Christmas Tree System as a Case Study

The subsea all-electric Christmas tree is key equipment in subsea production systems. If a failure occurs, the marine environment will be seriously polluted. Therefore, strict reliability analysis and measures to improve reliability must be performed before such equipment is launched, which is crucial to safe subsea production. A real-time reliability evaluation method for the all-electric Christmas tree mechanical system integrated with the static Bayesian network fault diagnosis stage is proposed in this paper, which realizes the identification of the fault type of the components and the real-time reliability evaluation of the mechanical system under different failure rates of the components. As a supplement to the method, by using mutual information to conduct sensitivity analysis on the reliability of the mechanical system, the importance of the basic events of each component on the reliability of the system is finally given. The proposed method provides significant theoretical support for the maintenance of the subsea all-electric Christmas tree and can be extended to the reliability evaluation of general subsea production systems.

Research on anomaly detection and real-time reliability evaluation with the log of cloud platform

With the development of software, massive information systems generate large-scale data resources involving a large amount of log information, which is of lots of meaningful contents. Accurate analysis and anomaly prediction based on logs are particularly important for building safe and reliable systems. It is evident that current anomaly prediction is not accurate enough, and there are few applications to the study of real-time reliability evaluation. Therefore, this paper uses ensemble learning model to analyze and predict anomaly of the massive system logs based on the complete procedures of log processing, including log analysis, feature extraction, anomaly detection, prediction evaluation, and real-time reliability evaluation. Compared with the traditional machine learning methods, the proposed model is able to improve the accuracy, recall rate and F1 value of anomaly prediction. The evaluation results are used to correct the real-time reliability in view of the low predicted recall rate, which greatly improves the accuracy of real-time reliability and thus provides accurate data basis and anomaly location basis for Artificial Intelligence for IT Operations. In this paper we have made the following innovations and contributions in anomaly detection and reliability measurement. The whole process was established from original log to real-time reliability measurement. New methods were adopted to improve the accuracy, recall and F1 value of anomaly detection. A real-time reliability measurement method is proposed.

Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems

Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems

Algorithm of preparation of the long-range radar station work in special modes on the basis of real-time reliability model

Algorithm of preparation of the long-range radar station work in special modes on the basis of real-time reliability model

Comparison the Effect of Etomidate vs. Thiopentone on Left Ventricular Strain and Strain Rate at the Time of Anesthesia Induction in Patients Undergoing Elective Coronary Artery Bypass Surgery: A Randomized Double Blind Controlled Trial

AbstractMyocardial strain imaging with speckle tracking echocardiography (STE) is getting popularity because it does not depends on insonation angle and has shown good correlation with intraoperative and postoperative ventricular function in patients with coronary artery disease. The impact of thiopentone and etomidate induction on myocardial function was studied and compared on sixty patients undergoing elective coronary artery bypass grafting. Three loops for each views (apical 4 chamber, apical 2 chamber, and apical long axis ) were acquired at base line (T0) and 1 minute (T1) after induction for offline analysis. In Group T, significant increase in HR from the base line values (67.8 ± 13.8 vs 79.2 ± 15.6, p = 0.001) occurred post induction, where as in Group E it remain near to the base line (71.7 ± 8.3 vs 70.1± 8.9, p = 0.345). A reduction in mean arterial pressure (MAP) was noted in both the groups after the injection of the allocated drug. There was no significant difference in the index of contractility (ICON) (T0 vs T1: 48.7 ± 10.6 vs 47.0 ± 11.7, p = 0.120) in Group E where as in Group T there was a reduction in the ICON value (T0 vs T1: 45.0 ± 10.7 vs 41.0 ± 8.4, p = 0.005). A similar picture was also noted in systemic vascular resistance index. A significant decrease in cardiac index (CI) was seen in Group E (T0 vs T1: 2.7 ± 0.4 vs 2.5 ± 0.4, p = 0.027), however it remain near to the base line in Group T. There occurred no changes in stroke index (SI) in Group E (T0 vs T1: 38.7 ± 6 vs 37.0± 5.3, p = 0.134), where as a significant decrease was noted after injection of thiopental (T0 vs T1: 38.0 ± 6.2 vs 36.1± 4.9, p = 0.049). A significant decline in cardiac performance index (CPI) was also recorded in Group E (T0 vs T1: 0.57 ± 0.15 vs 0.52 ± 0.12, p = 0.032), and not in Group T. There was decrease in left ventricular ejection fraction (LVEF) after the injection of both the drugs (Group E, T0 vs T1: 57 ± 3.7 vs 54± 3.7, p= 0.001; and Group T, T0 vs T1: 57 ± 3.7 vs 54± 3.7, p = 0.001). In Group E, global longitudinal peak systolic strain (GLPSS) showed no change after the injection of the drug (T0 vs T1: −13.2 ± 2.2 vs −13.1± 2.3, p = 0.631). However, a significant decrease in GLPSS (T0 vs T1: −13.5 ± 1.5 vs – 10 ± 1.8, p = 0.001) after injection of thiopental. Longitudinal peak systolic strain rate (LPSSR) was significantly decreased in all echocardiographic views after the injection of respective drugs. However, the decrease in LPSSR was significantly less in Group E in comparison to Group T.To conclude, STE provides accurate and reliable real time quantitative regional and global LV assessment. Use of thiopentone for anesthesia induction is associated with more profound impairment of LV function in comparison to etomidate as assessed by a decreased longitudinal peak systolic strain rate and global longitudinal peak systolic strain. Further studies are warranted to understand the exact clinical impact, which may influence the choice of intravenous induction agent based upon preoperative patient characteristics.

Reliability-monitoring data coupled model for concrete slab safety evaluation of CFRD and its engineering application

The concrete-faced rockfill dam (CFRD) is one of the widely constructed dam types, the concrete slab is the key anti-seepage structure of CFRD, and its safety directly affects the safety of the dam. In view of the traditional reliability analysis methods are complicated to calculate and cannot assess concrete slab cracking risk in real-time. In this paper, through the analysis of the response mechanism of slab stress and dam deformation, the coupling relationship between the reliability of concrete slab and monitoring data is revealed. Considering the randomness of the material parameters of CFRD, the limit state functions of concrete slab cracking are established by the response surface method (RSM). The accurate numerical simulation of the construction and operation process of CFRD is realized through the back analysis, then the time-variant reliability calculation of the concrete slab is realized. By analyzing the correlation between concrete slab reliability and some key factors such as dam displacement, water level, and time, a coupled evaluation model of reliability and monitoring data is proposed. Houziyan CFRD is selected as an example, the engineering application shows that the simulation relative error by using this coupled evaluation model is less than 5.0%, this model has the advantages of high efficiency, good precision, and strong applicability. The real-time reliability analysis of the concrete slab is realized and a new method for prediction and early warning of concrete slab cracking of CFRDs is provided.

The policy operations room: Analyzing path-dependent decision-making in wicked socio-ecological disruptions

The magnitude and speed of change in complex human-environmental systems pose a systemic dilemma for societies. Human-induced environmental changes have pushed Earth’s socio-ecological systems into an era of chronic, complex, and rapid disruptions, which call for quick intuitive decisions and effective implementation. Yet the complexity, interconnectedness and long lead times of the problems would require thoughtful and time-consuming weighing of evidence by a broad range of experts. To address the dilemma, we develop a framework, the Policy Operations Room (POR), for simultaneous practice and analysis of decision-making that prevents decisions made under time pressure from leading to unwanted socio-ecological disruptions decades ahead. The POR framework is based on earlier research on control rooms of critical infrastructures and simulation exercises of emergency response, and preliminary data from our first experiments with PORs. It immerses the policymakers in a simulated “time machine” that combines the real-time reliability management of control rooms with the long-term planning for crisis avoidance and preparedness. The POR framework can contribute significantly to novel styles of decision-making by policymakers, engineers, and corporate strategists responsible for developing urgent, forward-looking, and evidence-based policies to cope with the coming challenges of human-environmental interaction.

Using multithreaded programming for reliability simulation of the restorable technical systems with identical elements and arbitrary reliability structure

This scientific paper deals with the real-time reliability simulation of the restorable technical systems with identical elements and arbitrary reliability structure using multithreaded programming for the Microsoft® Windows™ operating system. The offered by the author algorithms for simulation of each of the system elements and estimation of the stationary availability factor using the simulation results are discussed. The developed by the author software implementation of the offered algorithms for reliability simulation of technical systems is also presented. Finally, an example of reliability simulation of the technical system with identical elements and bridge reliability structure using the developed reliability simulation program is also given.

Learning for Predictions: Real-Time Reliability Assessment of Aerospace Systems

Prognostics and health management aim to predict the remaining useful life (RUL) of a system and to allow a timely planning of replacement of components, limiting the need for corrective maintenance and the downtime of equipment. A major challenge in system prognostics is the availability of accurate physics-based representations of the faults dynamics. Additionally, the analysis of data acquired during flight operations is traditionally time consuming and expensive. This work proposes a computational method to overcome these limitations through the dynamic adaptation of the state-space model of fault propagation to onboard observations of the system’s health. Our approach aims at enabling real-time assessment of systems’ health and reliability through fast predictions of the remaining useful life that accounts for uncertainty. The strategy combines physics-based knowledge of the system damage propagation rate, machine learning. and real-time measurements of the health status to obtain an accurate estimate of the RUL of aerospace systems. The original method is demonstrated for the RUL prediction of an electromechanical actuator for aircraft flight controls. We observe that the strategy allows us to refine rapid predictions of the RUL in fractions of seconds by progressively learning from onboard acquisitions.

Reliability Evaluation of Piston Pump Based on Comprehensive Evaluation Index

Abstract After focusing on the randomness of the degradation process of the piston pump, the randomness lead to the proposal of a method based on the comprehensive evaluation index to select the characteristic parameters of different levels and characteristics. It was aimed to characterize the process of degradation. Initially the sensitive characteristics of the piston pump degradation were screened out, followed by the short-time test with a small sample for reliability assessment performed on the piston pump valve plate. The local characteristic-scale decomposition was employed to decompose the vibrational signal. Features characterizing the degradation of the valve plate were selected on the basis of the comprehensive evaluation index. Further application of the Principal Component Analysis algorithm was used to transform the degraded feature data to reduce dimension and extract the feature quantities. Then these feature quantities were substituted into the Weibull proportional reliability model. It aimed to calculate the reliability of the piston pump under different degradation states. The bizarre results showed that the combination of real-time effective features and reliability models not only fully reflect the operating state of the piston pump but also effectively evaluate the reliability of the piston pump.

Machine vision for drill string slip status detection

Slip status of drill string is system generated binary value computed by comparison of sensor generated real time hook load value with a minimum threshold value of hook load stored in measurement system. This research article describes a novel method of slip status detection by machine vision technology which helps overcome the constraints of slip status detection with legacy measurement method. It also helps improve the real time drilling data quality and optimize and automate drilling operations. A method to detect drill string slip status with high-resolution digital camera installed on mast near rig floor is described along with backend vision processing and communication modules, which generate binary values of slip status. The binary values are transferred in real time to drilling measurement system of rig to compute other drilling parameters like bit depth, hole depth and stand counters. This method includes deploying active optical sensors at the rig floor, obtaining 1-D, 2-D, or 3D image data, and processing it to obtain the status of drill string. Reliable measurement of slip status by machine vision helps reduce non-productive time (NPT) by reliable real time surveillance of drilling operations.

Emergency Contact and Location Sharing System for Women Safety

The crime rates against women are increasing day by day. This is a major issue in India as well as other countries. Women in India have been given equal rights as men but Safety has been not provided. As an engineer we can develop such a device which is useful for women safety. Our device helps women for her safety. This device can fit in purse with accurate and reliable real time tracking system. She can use mobile application for select any mobile number. One switch is given to send her location via message to emergency number. If switch pressed Arduino Mega read the switch and collect information from GPS system and send this information via Bluetooth to the selected Mobile number. This project focuses on the proposed model that can be used to deal with the problem of security issue of women harassment using GPS and Bluetooth based tracking application.

In Situ Thermal Inspection of Automated Fiber Placement for manufacturing induced defects

Automated Fiber Placement (AFP) manufacturing is prone to several varieties of manufacturing defects that can be detrimental to the structural integrity of the part. Detecting these defects after layup is time and labor intensive, and detection of some defect types post-cure can be difficult to infeasible due to the size of the flaws. The In Situ Thermal Inspection System (ISTIS) answers the need for fast and reliable real time inspection to enable high rate AFP manufacturing with minimal modifications or interference to the manufacturing process. This paper presents the details of the design of ISTIS and the thermal data analysis. This work presents a novel calibration and performance assessment regime which demonstrates ISTIS detecting all tested artificial manufacturing induced defects and sizing most to within 0.762 mm.

Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model

Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management.

Comprehensive Analysis of Tariff Barriers Worldwide: A Composite Assessment Approach

International trade has fundamental importance for all the countries and the analysis concerning international trade particularly concerning tariff barriers is high on the agenda of researchers in the field of economics, business and politics. Aim of the study is to assess the world tariff barriers of 158 countries. Overall design of the study comprises of a crisp literature review, data extraction and analysis. It is a study of one hundred fifty-eight countries that uses secondary data taken from World Development Indicators (WDI) 2020. It uses Grey Relational Analysis (GRA) as research methodology. Results of GRA show that Macao SAR, China, Hong Kong, Singapore, Montenegro, Mauritius, Brunei Darussalam, Myanmar, Chile, Peru and Australia have relatively highest grey relational grades meaning thereby, low tariff trade barriers whereas Grenada, Antigua and Barbuda, Belize, Central African Republic, Nepal, Guinea-Bissau, Fiji, Gabon, Barbados, Djibouti, St. Kitts and Nevis have lowest grey relational grade meaning thereby, these countries have high level of tariff based barriers of international trade. Interestingly, all the member countries of European Union occupy the rank of 27 (i.e. all countries have the same rank) which can be explained in the perspective of their union of tariff. Since, they have uniformed tariff policy as against rest of the world, therefore, have same rank. It is a study based on reliable real time data set. The study has value for all stakeholders i.e. international community, local governments, society at large, policy makers, researchers and international institutions.

Modeling and analysis of landing collision dynamics for a shipborne helicopter

A Lagrange dynamic model is established based on small-angle approximation to improve the simulation model for shipborne helicopter landing collision. To describe fuselage motion effectively, the proposed model considers ship motion, the interaction of the tires with the deck, and tire slippage. A mechanism of sliding motion is built, and a real-time reliability analysis of the algorithm is implemented to validate the proposed model. Numerical simulations are also conducted under different operation conditions. Results show that the proposed dynamic model can simulate the collision motion of helicopter landing in real time. Several suggestions for helicopter pilot landing are likewise provided.

Remote monitoring system for ITER PF converter system based on EtherCAT

Abstract This paper describes ITER PF converter system architecture in details. According to its real-time and reliability characteristics, ITER PF converter system uses an industrial Ethernet fieldbus based on EtherCAT protocol with high real-time communication performance to develop its remote monitoring system. This system follows ITER design specification, it realize the real-time monitoring of thousands of analog and digital signals, and can realize remote operation of the switches in the field layer, real-time data and historical data query, event alarm and authority management functions. The system has achieved continuous and stable operation on the test platform of ITER PF converter system, meeting the requirements of high real-time performance and reliability of the remote monitoring system of ITER PF converter system.

Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches

Unmanned aerial vehicles (UAVs) are becoming an integral part of numerous commercial and military applications. In many of these applications, the UAV is required to self-navigate in highly dynamic urban environments. This means that the UAV must have the ability to determine its location in an autonomous and real time manner. Existing localization techniques rely mainly on the Global Positioning System (GPS) and do not provide a reliable real time localization solution, particularly in dense urban environments. Our objective is to propose an effective alternative solution to enable the UAV to autonomously determine its location independent of the GPS and without message exchanges. We therefore propose utilizing the existing 5G cellular infrastructure to enable the UAV to determine its 3-D location without the need to interact with the cellular network. We formulate the UAV localization problem to minimize the error of the RSSI measurements from the surrounding cellular base stations. While exact optimization techniques can be applied to accurately solve such a problem, they cannot provide the real time calculation that is needed in such dynamic applications. Machine learning based techniques are strong candidates to provide an attractive alternative to provide a near-optimal localization solution with the needed practical real-time calculation. Accordingly, we propose two machine learning-based approaches, namely, deep neural network and reinforcement learning based approaches, to solve the formulated UAV localization problem in real time. We then provide a detailed comparative analysis for each of the proposed localization techniques along with a comparison with the optimization-based techniques as well as other techniques from the literature.

Data-Driven Scheduling of Energy Storage in Day-Ahead Energy and Reserve Markets With Probabilistic Guarantees on Real-Time Delivery

Energy storage systems (ESS) may provide the required flexibility to cost-effectively integrate weather-dependent renewable generation, in particular by offering operating reserves. However, since the real-time deployment of these services is uncertain, ensuring their availability requires merchant ESS to fully reserve the associated energy capacity in their day-ahead schedule. To improve such conservative policies, we propose a data-driven probabilistic characterization of the real-time balancing stage to inform the day-ahead scheduling problem of an ESS owner. This distributional information is used to enforce a tailored probabilistic guarantee on the availability of the scheduled reserve capacity via chance constrained programming, which allows a profit-maximizing participation in energy, reserve and balancing markets. The merit order-based competition with rival resources in reserve capacity and balancing markets is captured via a bi-level model, which is reformulated as a computationally efficient mixed-integer linear problem. Results show that a merchant ESS owner may leverage the competition effect to avoid violations of its energy capacity limits, and that the proposed risk-aware method allows sourcing more reserve capacity, and thus more value, from storage, without jeopardizing the real-time reliability of the power system.