Quality-Reliability Chain Modeling for System-Reliability Analysis of Complex Manufacturing Processes

Maintenance, production and quality are strongly linked to each other. Predictive maintenance is an effective way to eliminate the potential failures and ensure the stable operation of the manufacturing system, and further improve the reliability of the manufacturing system and the quality of manufactured products. Regarding the intelligent manufacturing philosophy “prediction and manufacturing#x201D;, a novel predictive maintenance strategy is proposed, in which the product quality control is integrated into predictive maintenance decision-making. First, the key process variables that characterize equipment wear are identified and integrated into the modeling of the equipment failure rate. Second, quality deviation that characterizes product quality level is defined based on co-effect between manufacturing system component reliability and product quality (i.e., Q-R chain). Third, the optimal maintenance strategy is obtained by optimizing the quality cost, maintenance cost, and interruption cost simultaneously. Finally, a case study on the predictive maintenance strategy decision-making for a cylinder head manufacturing system is presented to illustrate the effectiveness of the proposed method. The final result shows that the proposed method can achieve a superior economic performance relative to the conventional preventive maintenance mode.

Chapter 4 - Review on mechanical system reliability models

5 - Reliability of Large Multi-State Systems

This book chapter explores the integration of Internet of Things (IoT) and biometric authentication technologies in the development of smart locker systems for secure access within campus environments. With the increasing demand for efficient, secure, and automated solutions, smart locker systems are emerging as a transformative solution in educational institutions, offering enhanced security, real-time monitoring, and improved user experience. The chapter delves into the architecture and design of these systems, focusing on key components such as IoT sensors, biometric devices, and data encryption protocols. It addresses the challenges faced in securing communication, data transmission, and user privacy, providing best practices for ensuring the integrity and confidentiality of sensitive data. The performance and reliability of system components are critically evaluated, emphasizing the importance of scalability, responsiveness, and integration with existing campus infrastructure. The chapter further examines future trends, including the adoption of 5G connectivity, artificial intelligence, and blockchain technologies, which promise to elevate the capabilities of smart locker systems. With a focus on practical applications, this work serves as a comprehensive guide for the deployment of secure, efficient, and adaptable smart locker solutions in modern campus facilities.

A variety of definitions are available through the literature, but the universal one is in defining the well completion as the optimal path for the reservoir fluids to be produced. That means to achieve a desired production with minimal costs. Wells represent the greatest part of expenditure when developing the reservoir. For a long period of time it was defined to be simple, reliable, and safe with enough flexibility to allow future operations. Nowadays, so called “intelligent completions” appear to give more financial benefits, flexibility, and control. The reliability of system components is essential for long-lasting production. In addition, the differences according to natural flowing well risk and artificial lift are given.

In this paper, the authors studied the effects of process and component/part commonality and uncertain factors (viz. lead time, machine breakdown etc.) on deliverable parameters of a multi product, multi-period and multistage dependent demand manufacturing environment. The throughput of finished products and average production time of the system are examined for various experimental scenarios on the basis of existing manufacturing setup of a company consisting of multiple end products. Simulation package, namely WITNESS is used to simulate/analyze the situations of the production lines. Simulation models are developed, verified and validated with the historical data from the company. It is found that inclusion of common components in manufacturing system is always beneficial over the non-commonality environment, especially i) in uncertain situations, ii) for long procurement lead time of components and iii) when the number of parts increase in the system. Impacts of machine breakdown on system outcomes are higher than that of the lead time variation. The combination of uncertain factors has more impact on outcomes (throughput and average production time) compared to the individual factor. Commonality has a better control over the machine breakdown than lead time uncertainty. Component commonality is more effective for a production environment under uncertain situations than the process commonality.

Cavitation plays a crucial role in the reliability of components in refrigeration systems. The properties of refrigerants change significantly with temperature, thereby amplifying the impact of thermodynamic effects. This study, based on the Large Eddy Simulation (LES) method and the Schnerr-Sauer (S-S) cavitation model, investigates the transient cavitating flow characteristics of the R134a refrigerant in a Venturi tube (VT). The bubble number density in the S-S model was improved based on the experimental data of pressure and temperature. Simulation results indicate that there are two shedding modes of cavitation clouds in R134a refrigerant. One is induced by the combined action of reentrant flow and the vortices centrifugal force, while the other is generated by the central jet of the mainstream and the reverse jet produced by the collapsing cavitation bubbles. Furthermore, the thermodynamic effects of the refrigerant exert a certain inhibitory effect on cavitation, revealing the causes of instability in the refrigerant cavitation interface and the shedding characteristics of cavitation clouds. The relationship between local sound speed, flow velocity, and heat conduction rate in the cavitation region was studied, unveiling a time-lag in temperature changes relative to pressure changes in the intensive cavitation region. This study provides insights into the complex cavitation dynamics, especially in R134a refrigerant systems, and provides an approach for accurately predicting and managing cavitation in various industrial applications.

This paper describes a methodology based on safety factor concept and peak load considerations for a reliability evaluation of a generation system. This paper also refers to major research papers presented for the evaluation of reliability of power systems, reliability indices and modelling of power system components. Load models are considered as a straight line and a stepped load duration curve is used for reliability evaluation of system using Simpson's 1/3rd rule. The probability of load exceeding the generating capacity has also been considered in Loss of Load Probability (LOLP) evaluation.

There is a need to provide information system managers with better tools to estimate the trustworthiness of an information system. We believe it is necessary to consider trustworthiness of an enterprise information system as being more than an estimate of the reliability of the individual components. Some approach for analyzing component-level attacks to evaluate the impact on enterprise-level goals is needed as well as some approach for analyzing a series of information system attacks as part of a possible attack plan against the enterprise. Lower-level sensing of malicious activities and reaction to these activities is necessary to maintain reliability of individual information system components. However, toomuch of the current research effort is directed at component-level activity detection and reaction and too little of the current effort is directed at enterprise-level detection and reaction. We provide some thoughts on what is needed to be able to accumulate estimates of reliabilities of information system components into estimates of trustworthiness of information systems.

This paper deals with an application of partial least squares (PLS) methods to an industrial terephthalic acid (TPA) manufacturing process to identify and remove the major causes of variability in the product quality. Multivariate statistical analyses were performed to find the major causes of variability in the product quality, using the PLS models built from historical data measured on the process and quality variables. It was found from the PLS analyses that the variations in the catalyst concentrations and the process throughput significantly affect the product quality, and that the quality variations are propagated from the oxidation unit to the digestion units of the TPA process. A simulation-based approach was addressed to roughly estimate the effects of eliminating the major causes on the product quality using the PLS models. Based on the results that considerable amounts of the variations in the product quality could be reduced, we have proposed practical approaches for removing the major causes of product quality variations in the TPA manufacturing process.

<div class="section abstract"><div class="htmlview paragraph">A study of the cockpit information system architecture of current single-engine single-pilot aircraft was performed to establish a baseline for the evaluation of the reliability of new cockpit systems being developed through the Advanced General Aviation Transport Experiments (AGATE) program. That study defines a “typical” General Aviation (GA) cockpit information system architecture consisting of 38 components making up 32 subsystems. It also developed a reliability (fault tree) model for the system and utilized a proprietary analysis tool to compute system reliability.</div><div class="htmlview paragraph">Fault tree reliability models have gained wide acceptance since their introduction in the 1960’s to analyze the probability of success of military defense systems. Fault trees use logic gates to express the relationships between failures of the components and resulting failures of subsystems and of the system.</div><div class="htmlview paragraph">In a subsequent study an analytical model corresponding to the specific cockpit system reliability model was developed and implemented in a spreadsheet format. In addition to the system reliability equation, all of the subsystem reliability equations were also derived. From the analytic reliability model, the sensitivities of all of the subsystem and system reliabilities to changes in the component reliabilities were derived. These equations were also implemented in the spreadsheet tool. The resulting tool allows a user to modify the component reliability parameters and compute and display the system and subsystem reliabilities and the sensitivities of these reliabilities to changes in the component reliabilities.</div><div class="htmlview paragraph">This paper describes the single-engine single-pilot aircraft cockpit information system reliability model, explains the derivation of the analytical model for reliability and sensitivity computation, discusses the insight obtained from the analytical model, and presents the application of the spreadsheet tool.</div></div>

Mission reliability model is important for evaluation of the production capability of a manufacturing system. The manufacturing system mission reliability refers to the ability that the manufacturing system completes the production mission under specified conditions and within the specified time. The production mission includes two factors: quality of products and the productivity. Calculation of traditional evaluation parameters like Cpk and Ppk excludes the abnormal interruption of production and inspection errors. For some manufacturing systems with low degree of automation in the field of domestic weapons, both production disruptions and misjudgments in inspection processes have an impact on the mission reliability of the manufacturing system. A method of mission reliability modeling of discrete manufacturing system is proposed in this paper. The manufacturing system is composed of several processes. Abnormal interruption of production, inspection errors and substandard quality parameters of the products are involved in the modeling of the process mission reliability. The sequential and concurrent relationship between the processes is also taken into account in the modeling process.

Since a typical distribution system accounts for 40% of the cost to deliver power and 80% of customer reliability problems, distribution system design and operation is critical for financial success and customer satisfaction. To address this situation, Commonwealth Edison is integrating reliability assessment techniques into distribution planning and engineering. Just as equipment loading and voltage regulation are treated with analytical rigor with power flow models, interruptions and outages can be treated with analytical rigor with reliability models. This is made possible through the use of predictive reliability assessment tools that are able to predict customer reliability characteristics based on system topology and component reliability data. Reliability software predicts expected interruption frequencies and duration in a manner analogous to current and voltage evaluations in traditional power flow models. This article describes the distribution system's reliability assessment model jointly developed by Commonwealth Edison and ABB. This effort modeled, calibrated, and assessed the reliability of more than 3300 feeders. After completing the model, an intelligent system was used to automatically identify potential reliability problems and recommend reliability improvement projects based on expected benefits and costs.

Recent technological advancements in computing, sensing and communication have led to the development of cyber-physical manufacturing processes, where a computing subsystem monitors the manufacturing process performance in real-time by analyzing sensor data and implements the necessary control to improve the product quality. This paper develops a predictive control framework where control actions are implemented after predicting the state of the manufacturing process or product quality at a future time using process models. In a cyber-physical manufacturing process, the product quality predictions may be affected by uncertainty sources from the computing subsystem (resource and communication uncertainty), manufacturing process (input uncertainty, process variability and modeling errors), and sensors (measurement uncertainty). In addition, due to the continuous interactions between the computing subsystem and the manufacturing process, these uncertainty sources may aggregate and compound over time. In some cases, some process parameters needed for model predictions may not be precisely known and may need to be derived from real time sensor data. This paper develops a dynamic Bayesian network approach, which enables the aggregation of multiple uncertainty sources, parameter estimation and robust prediction for online control. As the number of process parameters increase, their estimation using sensor data in real-time can be computationally expensive. To facilitate real-time analysis, variance-based global sensitivity analysis is used for dimension reduction. The proposed methodology of online monitoring and control under uncertainty, and dimension reduction, are illustrated for a cyber-physical turning process.

Spinning reserve is idle capacity connected to the system with the purpose of balancing power demand and supply in real-time and ensuring reliable system operations in the case of equipment outages. The reserve has an economic value since it reduces the load shedding costs. This value is impacted by the reliability and dynamic characteristics of system components, the variability of the load from its forecast, the system operation policies, and economic aspects such as the risk preferences of the demand. In this paper, we take into account all these aspects to estimate the reserve value. The results require little computational effort, and are useful in the development of reserve demand functions and optimal reserve requirements.