Critical systems, such as telecommunication networks, power grids, transportation networks, and supply chains, have been dramatically expanded over the past decades. To avoid significant interruptions of their services, failure-prevention technologies and strategies have been explored extensively. However, in addition to inherent faults and expected failures, such systems are subject to natural and man-made hazards. The frequent occurrences of these hazards result in an increase in the systems' operational uncertainty as well as significant disruptions of their services. Unfortunately, the traditional reliability metrics do not adequately describe a system's performance under such hazards. There is a need for assessing the resilience of a system, which characterizes the system's performance deterioration and restoration under hazards. To date, substantial effort has been devoted to describing and quantifying system resilience from different perspectives. However, conceptual understanding and visionary transition from traditional reliability to resilience are more than just taking one step further. In this article, we briefly review approaches that qualitatively and quantitatively assess system resilience and discuss their applicable scenarios and limitations. Challenges and opportunities in system resilience modeling and enhancement, such as multihazard resilience modeling and restoration sequence optimization, are also presented so that more reliability researchers and practitioners may dive into and contribute to this important area.

In a general high-dimensional process, a large number of process parameters or quality characteristics is found to be featured through their dependencies and relevance. The features that have similar characteristics or behaviors in the process operation can be categorized into multiple groups. Thus, when a few quality characteristics in the process change, it is highly probable that the process shift would have occurred in a few relevant groups. Recently, several advanced statistical process control techniques are developed to monitor the changes in high-dimensional processes under sparsity. However, monitoring schemes that utilize the grouped pattern of the quality characteristics are sparse. This paper proposes a new method to monitor the high-dimensional process when the grouped structure of the process data is observed. The proposed method identifies the potentially changed groups and individual variables within the groups based on a modified sparse group LASSO (MSGL) model. Then, a monitoring statistic is obtained using MSGL-based likelihood function to test abnormality of the process. Extensive numerical studies are conducted to demonstrate the effectiveness and efficiency of the proposed method. In addition, a real-life application of a liquefied natural gas process is presented to illustrate the proposed method.

This chapter presents modeling and analysis of the reliability, maintainability, safety, and resilience of manufacturing and automation systems and its elements. It serves as a guide during the design and operation of highly “reliable” automation systems. It discusses reliability metrics for both non-repairable and repairable systems. The maintainability of the system and methods for its improvements are presented. The reliability and safety of the systems are intertwined and approaches for the minimization of the effect of system failures on its production output and safety are also discussed. The reliability metrics are then extended to include the impact of the system’s failure due to external hazards on its performance by introducing resilience measures. The ability of the system to “absorb” the impact of the hazards and the ability to quickly recover to its normal performance level are captured in the quantification of its resilience.

There has been great concern about building resilient supply chains to expedite the supply chain’s recovery after a crisis or disruption. Few attempts, however, were made to study the resiliency of a supply chain after disruptions caused by counterfeit parts, especially in critical domains like information and communication that are embedded in almost every aspect of our daily lives and critical life-supporting systems. Counterfeits will penetrate a supply chain at one of the suppliers’ or manufacturers’ points. Hence, rigorous countermeasures should be taken at these stages. The aim of this paper is to analyze the performance of an Information and Communication Technology (ICT) manufacturing supply chain that is vulnerable to the counterfeit threat. To achieve this, a hybrid simulation model is developed to measure the effectiveness of specific, representative countermeasures that have been introduced. The performance measures considered include the system’s service levels, delivery time, and the proportion of good products (products free of counterfeit parts). The findings of the study indicate that the countermeasures have a positive impact on the supply chain’s resiliency. The simulation model can be extended to other structures of supply chain networks, thereby enabling manufacturers to adopt the most effective countermeasures to ensure the resilience of their supply chains.

Anomaly detection of three-dimensional (3D) topographic data is a challenging problem in spatial data analysis. In this paper, we investigate spatial patterns of 3D surface data that exhibit multiple in-control modes. In complex manufacturing processes, surfaces of final products could contain different topographic features from one in-control surface to another, thus making it difficult to monitor the surface with existing approaches, which rely on the assumption of the presence of single mode surface topography. We propose a novel anomaly detection approach for monitoring local topographic variations in the presence of multimode surface topography. We present a binarization model to capture the generic behavior of the multimode surfaces and enhance the representation of the surface. To systematically monitor the surface, we introduce a new probabilistic distance measure (PDM) that quantifies the similarity of spatial patterns between two binarized surfaces. The proposed PDM takes advantage of identifying local variations by utilizing the order neighbor statistics, which captures the local property on the surface. Experimental results with numerical simulation data and real-life paper surface data are provided to demonstrate the effectiveness of the proposed approach.

This chapter presents the traditional reliability metrics and their applicability, focusing on resilience, one of the extended reliability metrics. A detailed overview of potential multiple hazards and methods of predicting and quantifying them is given. Existing definitions of resilience are reviewed, as well as methods of assessment and quantifications. In the context of robustness and restoration ability, quantifications of resilience are proposed for both nonrepairable and repairable systems under multiple hazards. A detailed discussion of system recovery is included. Specifically, importance measures are recommended to identify components' failures and repairs that have the greatest impact on system performance.

The objective of this chapter is to study the resilience of health care systems and to provide a snapshot of the resilience of several countries' health care systems. A brief summary is provided of literature covering resilience assessment in general, and health care systems resilience in particular. Unlike traditional resilience models in which recovery is almost monotonic, the pandemic hazard COVID-19 has no effective recovery treatment and has shown recurrence after some recovery had occurred. Proposed generic methods for quantifying resilience are presented and discussed. A case study of resilience assessments of health care systems under the COVID-19 hazard scenario is included.

One-shot units are produced in batches and stored in either a dormant or standby mode until retrieved or activated to perform their function when needed. In this article, we propose hybrid reliability testing approaches to utilize the advantages of non-destructive testing and destructive testing for assessment of reliability metrics. Specifically, we design a sequence of optimal hybrid testing plans under flexible scenarios. Extensive simulation models are developed to validate the accuracy and efficiency of the proposed approaches.

The past several decades have witnessed an increasing number of natural and manmade hazards with a dramatic impact on the normal operations of the society. The occurrences of these hazards manifest a growing trend of uncertainty. Assessing the performance of systems under such hazards is a salient concern of researchers and practitioners. The notion of ‘resilience’ has been proposed and popularised to characterise system performance deterioration and restoration due to different hazards and threats. Substantial effort has been devoted to quantify and describe resilience from different perspectives. However, there is no generic metric for assessing the resilience of different systems under different hazards. This paper provides a review of existing approaches that quantitatively assess resilience, along with their applicable scenarios and limitations. New general and generic resilience metrics for systems with multimodal performance are proposed. Opportunities for multi-hazard resilience modelling and enhancements are presented.

Herein, we used nicotinonitrile derivatives 4a,b as scaffolds to build novel and active antineoplastic agents. The reaction of nicotinonitrile derivatives 4a,b with POCl3/PCl5 and/or hydrazine hydrate afforded 2-chloropyridones 6a,b and 2-hydrazinyl nicotinonitrile derivatives 11a,b, respectively, as building blocks for various heterocyclic compounds. The structures of all of the synthesized heterocycles were elucidated from their spectral and elemental analyses. The cytotoxic activities of the prepared derivatives were evaluated against different cancer cell lines. Results revealed potential cytotoxic effects of the synthesized compounds against evaluated cell lines, where NCIH 460 and RKOP 27 cell lines were the most affected by the prepared compounds. Derivative 14a was the most effective against all tested cell lines in terms of the obtained IC50 values (25 ± 2.6, 16 ± 2, 127 ± 25, 422 ± 26, and 255 ± 2 nM against NCIH 460, RKOP 27, HeLa, U937, and SKMEL 28 cells, respectively).

Precision guided projectiles (PGPs) experience severe shock loads during launch emanating from the propellant gasses and the surrounding air. Our most recent multiphysics effort showed that the complex flow environment during launch would result in the development of severe shock loads in the confined barrel space and at muzzle exit. The focus of the current effort is the survivability of the embedded integrated circuit chips and printed circuit boards (PCB). Four aspects of the work were accordingly examined. The first is concerned with the development of a new polymeric encapsulation technique to protect the embedded microelectronic systems (EMES). The second with testing the effectiveness and endurance of the newly proposed encapsulation techniques using instrumented pneumatic drop weight impact test facility. The third with constructing bottom-up multi-level micromechanics based homogenization schemes, accounting for complex constitutive material models of a multilayer circuit board, to determine the effective elastic properties of the PCB for the numerical FE simulations. The fourth with conducting three-dimensional high-resolution dynamic FE simulations to evaluate the structural integrity of the potted PCB assembly in response to the short duration impulse and to elucidate the experimental findings. The results of our experimental and numerical efforts reveal that the newly devised encapsulation technique is highly effective in protecting EMES and can be used to audit the survivability of the microelectronics that are embedded in PGPs.

A newly revised and updated edition that details both the theoretical foundations and practical applications of reliability engineeringReliability is one of the most important quality characteristics of components, products, and large and complex systemsbut it takes a significant amount of time and resources to bring reliability to fruition. Thoroughly classroom- and industry-tested, this book helps ensure that engineers see reliability success with every product they design, test, and manufacture.Divided into three parts, Reliability Engineering, Second Edition handily describes the theories and their practical uses while presenting readers with real-world examples and problems to solve. Part I focuses on system reliability estimation for time independent and failure dependent models, helping engineers create a reliable design. Part II aids the reader in assembling necessary components and configuring them to achieve desired reliability objectives, conducting reliability tests on components, and using field data from similar components. Part III follows what happens once a product is produced and sold, how the manufacturer must ensure its reliability objectives by providing preventive and scheduled maintenance and warranty policies.This Second Edition includes in-depth and enhanced chapter coverage of:Reliability and Hazard FunctionsSystem Reliability EvaluationTime- and Failure-Dependent ReliabilityEstimation Methods of the Parameters of Failure-Time DistributionsParametric Reliability ModelsModels for Accelerated Life TestingRenewal Processes and Expected Number of FailuresPreventive Maintenance and InspectionWarranty ModelsCase StudiesA comprehensive reference for practitioners and professionals in quality and reliability engineering, Reliability Engineering can also be used for senior undergraduate or graduate courses in industrial and systems, mechanical, and electrical engineering programs.

Degradation branching is a common phenomenon in many real-life applications. The degradation of a location not only increases with time, but also propagates to other locations in the same system. While the degradation of an individual location has been studied extensively, research on degradation branching is sparse. In this paper, we develop a general stochastic degradation branching model that characterizes both the degradation growth and degradation propagation. The probabilistic properties of the general degradation branching processes are analyzed. Reliability metrics such as the mean time to failure, mean residual life, failure probability and others are also investigated. In particular, closed-form expressions for the expectation and variance of the degradation and selected reliability metrics are obtained when the time to branch follows an exponential distribution. The model is validated using actual crack growth data.

This chapter develops time-dependent reliability expressions for both nonrepairable and repairable systems. It presents different approaches for estimating the reliability of failure-dependent systems; for example, when the failure of a component affects the failure rate of other components in the system. The chapter describes the alternating renewal process and the Markov process for estimating the availability of repairable systems. Reliability analysis of systems whose components experience dependent failures can be performed using the Markov model. The model performs well when the number of state-transition equations is small and when the failure-time and repair-time distributions are exponential. The chapter provides an example that illustrates the development of such a Markov model. Finally, it estimates different performance measures of the system such as mean time to failure, mean time between failures, and system's availability.

One of the quality characteristics that consumers require from the product manufacturers or service providers is reliability. This chapter provides definition of reliability and hazard function along with examples. Reliability may be used as a measure of the system's success in providing its function properly during its design life. The hazard-rate expression is of the greatest importance for system designers, engineers, and repair and maintenance groups. The expression is useful in estimating the time to failure (or time between failures), repair crew size for a given repair policy, the availability of the system, and in estimating the warranty cost. When a system is composed of two or more components, the joint life lengths are described by a multivariate distribution whose nature depends on the individual component life length. Failure data can be modeled using competing risk models or mixture of failure-rate models.

The 3D surface topography of finished products is a key characteristic for monitoring the quality of products and manufacturing processes. The topography has unique properties in which the topographic values are spatially autocorrelated with their neighbours and the locations of topographic values randomly change from one surface to another under the in-control process behaviour, making the online detection of local topographic changes challenging. Due to the complex structure of topographic data, the existing monitoring approaches lack the detection of local changes. Therefore, we develop a novel online monitoring approach for detecting local changes in 3D topographic surfaces. We introduce a multilevel surface thresholding algorithm for enhancing the representation of topographic values by slicing the 3D surface topography into cumulative levels in reference to the characteristics of the in-control surfaces. The spatial and random properties of topographic values are quantified at each surface level through the proposed spatial randomness profile. After obtaining the spatial randomness profile, an effective monitoring statistic based on the functional principal component analysis is developed for detecting anomaly surfaces. The proposed approach shows superior performance in identifying a wide range of fault patterns and outperforms the existing approaches in both simulated and real-life topographic data.

A new validated method based on potentiometric transduction for bispyribac herbicide assessment in commercial formulations, rice and wastewater samples is fabricated and characterized. Sensors are based in terms of their fabrication on tridodecyl methyl ammonium chloride (TDMAC) as recognition material. TDMAC was plasticized in a poly (vinyl chloride) (PVC) matrix to prepare the membrane. Under static modes of operation, the sensors revealed a Nernstian anionic slope of −63.6 ± 0.7 mV/decade within a linear range of 9.1 × 10−6–1.0 × 10−2 in 50 mM phosphate buffer solution (PBS), pH7. The detection limit was 6.0 × 10−6 M. The sensor was successfully introduced in a flow-stream system revealing a Nernstian response of −53.8 ± 1.3 mV/decade over a linear range of 2 × 10−4–1.0 × 10−2 M and lower detection limit of 5.6 × 10⁻⁵ M. The sampling rate was calculated to be (~42 sample/h). Validation of the assay method is presented in detail including accuracy, trueness, bias, between-day variability and within-day variability, and good performance characteristics of the method are obtained. The presented method was successfully introduced to bispyribac determination in different complex matrices such as commercial bispyribac sodium known as (Nominee-kz, 3% soluble liquid (SL)), rice samples and agricultural wastewater samples. The samples were analyzed successfully under both static and hydrodynamic modes of operation. The results obtained were in a good agreement with those obtained by the liquid chromatographic method.

Surface topography is a critical quality characteristic of many products and manufacturing processes. Various defects commonly appear on the topography of finished products in spatial patterns after or during manufacturing. These defects are difficult to be identified using traditional monitoring approaches because of the complex structure of topographic data. This article develops a novel and effective approach for monitoring spatial defects in topographic surfaces. The approach improves the representation of surface characteristics through the developed multilabel separation-deviation surface (MSS) model, which labels the important surface characteristics and smooths out the noisy characteristics. We develop two features for monitoring changes in the characteristics of the assigned labels. The MSS feature is introduced for capturing deviations within the assigned labels, and the generalized spatial randomness feature is derived for quantifying deviations between the assigned labels. These two features are integrated into a single monitoring statistic, which is successfully applied for detecting various defects in topographic surfaces, outperforming the traditional monitoring approaches.

Reliability estimation requires the knowledge of the underlying failure time distribution of the component or the failure time distribution of the system being modeled. The accuracy of the estimate of the parameters depends on the sample size and the method used for estimating the parameters. The statistics, calculated from the samples that are used to estimate population parameters, are called estimators. Three of the most widely used methods for estimating the parameters of the population are the method of moments, the maximum likelihood method, and the least-squares method. The main idea of the method of moments is based on the moment-generating function. This chapter presents the concept of the likelihood function, followed by a description of the maximum likelihood method. The method of least squares provides an efficient and unbiased estimator of the distribution parameters. The chapter describes Bayes’ theorem that is used for estimating the parameters of distributions.