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This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations' costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Reliability prediction for complex systems utilizing prognostic methods has attracted increasing attention. Furthermore, achieving accurate reliability predictions for complex systems necessitates considering the interaction between components and the multivariate functional relationship that exists among them. This paper proposes a bi‐level method to evaluate the variability of degradation processes and predictive reliability based on the profile monitoring approach for multicomponent systems. Firstly, a multivariate profile structure is introduced to model the framework of degradation analysis in scenarios where there exists stochastic dependency and a multivariate functional relationship between the degradation processes of components. At the component level, the objective is to evaluate the variability of the degradation process for each component considering the presence of stochastic dependence. For the system level analysis, the proposed approach enables the prediction of degradation variability and system reliability by considering the functional relationships among components, without the need for direct calculation of individual component reliabilities. The performance of the proposed model is evaluated through a numerical study and sensitivity analysis conducted on a multicomponent system with a k‐out‐of‐n structure. The results demonstrate the model's notable flexibility and efficiency. [ABSTRACT FROM AUTHOR]

Industries increasingly rely on intricate multi-component systems, necessitating efficient maintenance strategies to ensure system reliability and minimize downtime. Predictive maintenance, an emerging approach that utilizes data-driven techniques to forecast and prevent failures, holds significant potential in this regard. This paper presents a predictive maintenance strategy tailored specifically for multi-component systems. In order to accurately anticipate the remaining useful life (RUL) of components, we develop a method that combines data and model fusion based on a particle filtering approach and a degradation distribution model. By integrating degradation data with models, our method outperforms traditional model-based approaches in terms of prediction accuracy. Subsequently, we apply an optimized maintenance model to individual components based on the trigger threshold for RUL. This model determines the most optimal maintenance actions for each component, with the aim of minimizing maintenance costs. Furthermore, we introduce an optimized maintenance strategy that incorporates opportunistic maintenance to further reduce the overall maintenance cost of the system. This strategy leverages predicted RUL information to schedule proactive maintenance actions at the opportune moment, resulting in a significant cost reduction compared to traditional periodic maintenance approaches. To validate the feasibility and effectiveness of our proposed strategy, we utilize experimental data from open-source lithium-ion batteries at the NASA PCoE Center. Through this empirical validation, we provide real-world evidence showcasing the applicability and performance of our strategy in a multi-component system. [ABSTRACT FROM AUTHOR]

In recent years, the trend toward greater integration and complexity of mechanical systems has brought challenges to the formulation of preventive maintenance plans. It is very difficult to realize the traditional condition-based maintenance method that relies on calculating the optimal maintenance threshold to achieve optimal maintenance. However, in solving highly complex and challenging control and decision-making problems, the deep reinforcement learning (DRL) method shows its powerful ability and provides a new idea for the maintenance planning of complex systems. Numerical results show that DRL-based maintenance model can obtain optimization strategies through continuous exploration and realize the trade-off between component maintenance cost and the loss caused by system failure, whether in simple or complex multi-component systems. The policy minimizes the overall cost of the system by choosing actions that minimize the total long-term cost. The comparison with other maintenance strategies shows that the proposed model is superior to various baseline policies and reduces the system lifecycle cost. [ABSTRACT FROM AUTHOR]

In this article, we will address the complexity of non‐identical components in multi‐component systems. Most technical systems can be described as such since either component types or component functions within the system vary amongst components. While most reliability related work resorts to the assumption of homogeneous components, we aim to address the often more realistic assumption of heterogeneous components extending the model of Extended Sequential Order Statistics by two inferential methods. Firstly, the derivation of Maximum Likelihood Estimates including a simulation study demonstrating their good performance for large enough sample size. Secondly, we introduce a likelihood ratio test to test whether components can be assumed identical accompanied by a power study. Both methods are powerful tools in reliability contexts. The former increases our understanding of component behaviour, especially upon failure of other components. This knowledge empowers system operators to make better decisions regarding maintenance schedules and failure time prediction. The latter supports operators in their quest of identifying component equivalence. Therefore, both methods can be used to achieve meaningful results in real life applications. [ABSTRACT FROM AUTHOR]

There are situations in which the experimenter has some information about the components of the operating system and he/she wants to use this information for better assessment or operating of the underlying system. In such cases the notion of conditional probability may help the operator to use that information and improve his/her task. In the present study this notion has been examined, and some conditional stress-strength parameters have been introduced for s of k systems. The multi-component conditional stress-strength parameter (MCCSSP) and its maximum likelihood estimator have been calculated when the strength and stress random variables are exponentially distributed. In the case of having extra information about the parameters, a closed form has been derived for the Bayes estimator of MCCSSP and has been calculated by using an algorithm together with Monte Carlo method. For the case of non-exponential stress or strengths, the nonparametric estimator of the defined parameter has also been derived. Finally, some simulation study on the MLE and Bayes estimator, as well as real data analysis for nonparametric estimators have been done to verify the analytic results. [ABSTRACT FROM AUTHOR]

Taking the multi-component perspective in Predictive Maintenance (PdM) is one promising approach to improve prediction quality. Therefore, detection and modeling of interdependencies within systems are important, especially as systems become more complex and personalized. However, existing solutions in PdM mostly focus on a single-component perspective, neglecting the dependencies between components, even if interdependencies can be found between most components in the real world. The major reason for this lost opportunity is the challenge of identifying and modeling interdependencies between components. This paper introduces a framework to identify interdependencies and explain their impact on PdM within a Multi-Component System (MCS). The contribution of this approach is two-fold. First, it shows the impact of modeling interdependencies in predictive analytics. Second, it helps to understand which components interact with each other and to which degree they affect the deterioration state of corresponding components. As a result, our approach can identify and explain the existence of interdependencies within components. In particular, we demonstrate that time from last change of component is a valuable feature to quantify interdependencies. Moreover, we show that taking into account the interdependencies provides a statistically significant improvement of f1-score by 7% on average compared to the model where interdependencies are neglected. We expect that our findings will improve maintenance scheduling in the industry while improving prediction models in general. [ABSTRACT FROM AUTHOR]

Industries increasingly rely on intricate multi-component systems, necessitating efficient maintenance strategies to ensure system reliability and minimize downtime. Predictive maintenance, an emerging approach that utilizes data-driven techniques to forecast and prevent failures, holds significant potential in this regard. This paper presents a predictive maintenance strategy tailored specifically for multi-component systems. In order to accurately anticipate the remaining useful life (RUL) of components, we develop a method that combines data and model fusion based on a particle filtering approach and a degradation distribution model. By integrating degradation data with models, our method outperforms traditional model-based approaches in terms of prediction accuracy. Subsequently, we apply an optimized maintenance model to individual components based on the trigger threshold for RUL. This model determines the most optimal maintenance actions for each component, with the aim of minimizing maintenance costs. Furthermore, we introduce an optimized maintenance strategy that incorporates opportunistic maintenance to further reduce the overall maintenance cost of the system. This strategy leverages predicted RUL information to schedule proactive maintenance actions at the opportune moment, resulting in a significant cost reduction compared to traditional periodic maintenance approaches. To validate the feasibility and effectiveness of our proposed strategy, we utilize experimental data from open-source lithium-ion batteries at the NASA PCoE Center. Through this empirical validation, we provide real-world evidence showcasing the applicability and performance of our strategy in a multi-component system.

Human society is entering the era of Industry 4.0; engineering systems are becoming more complex, which increases the difficulties of maintenance support work. Failure dependence exists widely in multi-component systems. In this work, a model of two-dimensional (2D) warranty decision making was constructed by using a failure-dependence analysis for multi-component systems and by considering the extended warranty cost and the system availability. The decision was to cut the warranty cost as much as possible for manufacturers, while the constraint condition was the minimum acceptable availability for the customer. The model combined preventive maintenance as well as corrective maintenance strategies. Under the condition that the multi-component system is replaced upon the expiration of the extended warranty (EW), the optimal 2D EW duration and preventive maintenance interval could be obtained through the model. In a case analysis, the optimal EW scheme for the gearbox of an electric multiple unit (EMU) system was obtained by using a grid search algorithm, a PSO algorithm, and a PSO–BAS algorithm. Through comparison, the PSO–BAS algorithm obtained a better scheme with lower warranty costs and higher system availability. A comparative analysis and a sensitivity analysis showed that the model provides a theoretical basis for manufacturers to optimize their 2D extended gearbox warranties. [ABSTRACT FROM AUTHOR]

Breakdowns and unplanned shutdowns in industrial processes and equipment can lead to significant loss of availability and revenue. It is imperative to perform optimal maintenance of such systems when signs of abnormal behavior are detected and before they propagate and lead to catastrophic failure. This is particularly challenging in systems with interconnected multiple components as it is difficult to isolate the effect of one component on the operation of other components in the system. In this work, an ensemble approach based on Cascaded Convolutional neural network and Long Short-term Memory (CC-LSTM) network models is proposed for detecting and predicting the time of onset of faults in interconnected multicomponent systems. The performance of the ensemble CC-LSTM model was demonstrated on an industrial 4-component system and was found to improve the accuracy of onset time predictions by ~15% compared to individual CC-LSTM models and ~25-40% compared to commonly used deep learning techniques such as dense neural networks, convolutional neural networks and LSTMs. The CC-LSTM and the ensemble models also had the lowest missed detection rates and zero false positive rates making them ideal for real-time monitoring and fault detection in multicomponent systems.

Manufacturing systems consist of a set of interdependent components. However, addressing the dependence between these components remains a challenge in both maintenance modeling and the optimization process. In this paper, we propose a multi-agent deep reinforcement learning-based maintenance approach for a manufacturing system, taking into consideration both stochastic and economic dependencies between components. In this manner, we introduce a novel state interactions model, suggesting that the degradation state of one component may influence the degradation process of others. Subsequently, a maintenance planning approach based on multi-agent deep reinforcement learning is developed to optimize maintenance decisions in both fully and partially observed states. The deployed multi-agent deep reinforcement algorithm, specifically Weighted QMIX, ensures scalability and efficient consideration of state interactions and economic dependencies between components. The feasibility and performance of the proposed maintenance approach are investigated through various numerical studies. When compared to traditional maintenance approaches, such as value iteration method, Dueling Double Deep Q Network, and Multi-Agent Deep Q Network, our proposed approach consistently demonstrates superior results. • A novel state interactions model is proposed for stochastic dependence between components. • A multi-agent deep reinforcement learning based maintenance approach is developed. • The impact of component dependencies in maintenance optimization is investigated. • The applicability of the proposed maintenance approach in different contexts is studied. • A comparison study with traditional maintenance approaches is provided. [ABSTRACT FROM AUTHOR]

In this study, system maintenance and production scheduling are jointly decided to solve the problems of resource idleness and time cost increase due to system maintenance in the processing of production scheduling. Based on the previous research on the integration of scheduling and maintenance of single machine and multi machine, considering the deterioration and economic dependence of each component in the system, a joint strategy of multi component condition-based maintenance combined with production scheduling is formulated. All the maintenance combinations and probability calculation formulas in the production scheduling process are developed using the deterioration state space partition method, and the probability density function and its numerical solution method are derived. A joint decision model is established to minimize the total weighted expected completion time. Finally, taking the KS5 adjustable multi axis tapping machine as an example, numerical experiments were conducted to verify the correctness of the proposed strategy and the established model. [ABSTRACT FROM AUTHOR]

Quasi-elastic neutron scattering (QENS) and small angle neutron scattering (SANS), in combination with isotopic contrast variation, have been used to determine the structure and dynamics of three-component lipid membranes, in the form of vesicles, comprising an unsaturated [palmitoyl-oleoyl-phosphatidylcholine (POPC) or dioleoyl-phosphatidylcholine (DOPC)], a saturated phospholipid (dipalmitoyl-phosphatidylcholine (DPPC)), and cholesterol, as a function temperature and composition. SANS studies showed vesicle membranes composed of a 1:1:1 molar ratio of DPPC:DOPC:cholesterol and a 2:2:1 molar ratio of DPPC:POPC:cholesterol phase separated, forming lipid rafts of ∼18 and ∼7 nm diameter respectively, when decreasing temperature from 308 to 297 K. Phase separation was reversible upon increasing temperature. The larger rafts observed in systems containing DOPC are attributed to the greater mis-match in lipid alkyl chains between DOPC and DPPC, than for POPC and DPPC. QENS studies, over the temperature range 283–323K, showed that the resulting data were best modelled by two Lorentzian functions: a narrow component, describing the “in-plane” lipid diffusion, and a broader component, describing the lipid alkyl chain segmental relaxation. The overall “in-plane” diffusion was found to show a significant reduction upon increasing temperature due to the vesicle membranes transitioning from one containing rafts to one where the component lipids are homogeneously mixed. The use of different isotopic combinations allowed the measured overall reduction of in-plane diffusion to be understood in terms of an increase in diffusion of the saturated DPPC lipid and a corresponding decrease in diffusion of the unsaturated DOPC/POPC lipid. As the rafts are considered to be composed principally of saturated lipid and cholesterol, the breakdown of rafts decreases the exposure of the DPPC to cholesterol whilst increasing the exposure of cholesterol to unsaturated lipid. These results show the sensitivity of lipid diffusion to local cholesterol concentration, and the importance of considering the local, rather that the global composition of a membrane when understanding the diffusion processes of lipids within the membrane. The novel combination of SANS and QENS allows a non-intrusive approach to characterize the structure and dynamics occurring in phase-separated model membranes which are designed to mimic the lateral heterogeneity of lipids seen in cellular membranes–a heterogeneity that can have pathological consequences.

Design principles of phenotypes in organisms are fundamental issues in physical biology. So far, understanding “systems” of living organisms have been chiefly promoted by understanding the underlying biomolecules such as genes and proteins, and their intra- and inter-relationships and regulations. After a long period of sophistication, biophysics and molecular biology have established a general framework for understanding ‘molecular systems’ in organisms without regard to species, so that the findings of fly studies can be applied to mouse studies. However, little attention has been paid to exploring “phenotypic systems” in organisms, and thus its general framework remains poorly understood. Here I review concepts, methods, and case studies using butterfly and moth wing patterns to explore phenotypes as systems. First, I present a unifying framework for phenotypic traits as systems, termed multi-component systems. Second, I describe how to define components of phenotypic systems, and also show how to quantify interactions among phenotypic parts. Subsequently, I introduce the concept of the macro-evolutionary process, which illustrates how to generate complex traits. In this point, I also introduce mathematical methods, “phylogenetic comparative methods”, which provide stochastic processes along molecular phylogeny as bifurcated paths to quantify trait evolution. Finally, I would like to propose two key concepts, macro-evolutionary pathways and genotype-phenotype loop (GP loop), which must be needed for the next directions. I hope these efforts on phenotypic biology will become one major target in biophysics and create the next generations of textbooks. This review article is an extended version of the Japanese article, Biological Physics in Phenotypic Systems of Living Organisms, published in SEIBUTSU-BUTSURI Vol. 61, p. 31–35 (2021).

The ability of date seed-derived biochar (DSB) to remove cadmium(II) and lead(II) from single and multi-solute solutions in packed column have been investigated. These column trials were con- ducted to understand the effects of column parameters such as bed depths, initial Cd(II)/Pb(II) concentrations and flow rates. The results indicated that the metal uptake of DSB decreased with increase in flow rate, and decrease in initial metal concentration and bed depth values. At a flow rate of 0.3 L/h, bed height of 25 cm, and initial metal concentration of 1 mmol/L, DSB recorded maximum Cd(II) and Pb(II) uptake values of 0.525 and 0.462 mmol/g, respectively. The continuous adsorption data models such as Yoon–Nelson, Thomas, and modified dose-response equations were utilized. Multi-component column studies were also performed and the results indicated decreased uptake of Cd(II) and Pb(II) by DSB due to competition. At optimized conditions, DSB recorded 0.447 and 0.449 mmol/g for Cd(II) and Pb(II), respectively, in multi-solute system. The column elution and subsequent reuse of DSB for next cycle was possible with 0.01 M HCl as elutant. The column regeneration was attempted for three cycles and the results indicated that DSB produced consistently high Cd(II) and Pb(II) removal efficiencies and uptakes. [ABSTRACT FROM AUTHOR]

In reliability modelling and analysis of systems, quantile functions play a significant role when the distribution function under consideration does not possess an analytically tractable form, though the corresponding quantile function has an explicit form. In the present paper, we propose a quantile-based approach for stress-strength reliability of a system. We also introduce the quantile versions of stress-strength reliability for residual lifetime random variables. The effect of monotone transformations and some characterizations of the proposed measures are discussed. The reliability of multi-component stress-strength systems based on quantiles is also considered. [ABSTRACT FROM AUTHOR]

Design principles of phenotypes in organisms are fundamental issues in physical biology. So far, understanding "systems" of living organisms have been chiefly promoted by understanding the underlying biomolecules such as genes and proteins, and their intra- and inter-relationships and regulations. After a long period of sophistication, biophysics and molecular biology have established a general framework for understanding 'molecular systems' in organisms without regard to species, so that the findings of fly studies can be applied to mouse studies. However, little attention has been paid to exploring "phenotypic systems" in organisms, and thus its general framework remains poorly understood. Here I review concepts, methods, and case studies using butterfly and moth wing patterns to explore phenotypes as systems. First, I present a unifying framework for phenotypic traits as systems, termed multi-component systems. Second, I describe how to define components of phenotypic systems, and also show how to quantify interactions among phenotypic parts. Subsequently, I introduce the concept of the macroevolutionary process, which illustrates how to generate complex traits. In this point, I also introduce mathematical methods, "phylogenetic comparative methods", which provide stochastic processes along molecular phylogeny as bifurcated paths to quantify trait evolution. Finally, I would like to propose two key concepts, macro-evolutionary pathways and genotype-phenotype loop (GP loop), which must be needed for the next directions. I hope these efforts on phenotypic biology will become one major target in biophysics and create the next generations of textbooks. This review article is an extended version of the Japanese article, Biological Physics in Phenotypic Systems of Living Organisms. [ABSTRACT FROM AUTHOR]

Taking the multi-component perspective in Predictive Maintenance (PdM) is one promising approach to improve prediction quality. Therefore, detection and modeling of interdependencies within systems are important, especially as systems become more complex and personalized. However, existing solutions in PdM mostly focus on a single-component perspective, neglecting the dependencies between components, even if interdependencies can be found between most components in the real world. The major reason for this lost opportunity is the challenge of identifying and modeling interdependencies between components. This paper introduces a framework to identify interdependencies and explain their impact on PdM within a Multi-Component System (MCS). The contribution of this approach is two-fold. First, it shows the impact of modeling interdependencies in predictive analytics. Second, it helps to understand which components interact with each other and to which degree they affect the deterioration state of corresponding components. As a result, our approach can identify and explain the existence of interdependencies within components. In particular, we demonstrate that time from last change of component is a valuable feature to quantify interdependencies. Moreover, we show that taking into account the interdependencies provides a statistically significant improvement of f1-score by 7% on average compared to the model where interdependencies are neglected. We expect that our findings will improve maintenance scheduling in the industry while improving prediction models in general.

It is a challenge to identify each phase in a multi-component polymer system and uniquely determine the interfacial properties between the different phases. Using atomic force microscopy nanomechanical mapping (AFM-NM) and AFM-based infrared spectroscopy (AFM-IR), we identify each phase, visualize structural developments, and determine the interfacial properties in a blend of three polymers: high-density polyethylene (HDPE), polyamide (PA6) and poly(styrene-b-ethylene-co-butylene-b-styrene) (SEBS). Each phase can be identified from the Young's modulus, along with the structural development within the phases before and after compatibilization. The interfacial widths between HDPE/PA6, HDPE/SEBS and SEBS/PA6 were determined independently in one measurement from a Young's modulus map. The structural, mechanical property development and identity of the phases were determined by AFM-NM, while AFM-IR, providing complementary chemical information, identified interfacial reactions, showed the chemical affinity of a compatibilizer with the component phases, and mapped the distribution of the compatibilizer in the ternary polymer blends. The chemical, structural and interfacial information obtained by these measurements provide information that is essential for producing mechanically robust materials from incompatible mixtures of polymers. [ABSTRACT FROM AUTHOR]

Enzymes are encoded with a gamut of information to catalyze a highly selective transformation by selecting the proper reactants from an intricate mixture of constituents. Mimicking biological machinery, two switchable catalysts with differently sized cavities and allosteric control are conceived that allow complementary size‐selective acyl transfer in an on/off manner by modulating the effective local concentration of the substrates. Selective activation of one of two catalysts in a mixture of reactants of similar reactivity enabled upregulation of the desired product. [ABSTRACT FROM AUTHOR]

Condition-Based Maintenance (CBM) is an effective maintenance strategy to improve system performance while lowering operating and maintenance costs. Real-world systems typically consist of a large number of components with various interactions among components. However, existing studies on CBM mainly focus on single-component systems. Multi-component CBM, which joins the components' stochastic degradation processes and the combinatorial maintenance grouping problem, remains an open issue in the literature. In this article, we study the CBM optimization problem for multi-component systems. We first develop a multi-stage stochastic integer model with the objective of minimizing the total maintenance cost over a finite planning horizon. We then investigate the structural properties of a two-stage model. Based on the structural properties, two efficient algorithms are designed to solve the two-stage model. Algorithm 1 solves the problem to its optimality and Algorithm 2 heuristically searches for high-quality solutions based on Algorithm 1. Our computational studies show that Algorithm 1 obtains optimal solutions in a reasonable amount of time and Algorithm 2 can find high-quality solutions quickly. The multi-stage problem is solved using a rolling horizon approach based on the algorithms for the two-stage problem. are available for this article. Go to the publisher's online edition of IISE Transaction, datasets, additional tables, detailed proofs, etc. [ABSTRACT FROM AUTHOR]

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• A dynamic mission abort policy for multi-component transportation systems. • The transportation system with stochastic dependence and mission payload. • The dynamic mission abort problem is constructed as a Markov decision process. • A deep reinforcement learning approach with parameter sharing is customized. • The Markov decision process is solved by the deep reinforcement learning approach. The mission abort policy is considered an effective approach for managing operational risks in transportation systems, which are typically equipped with mission payloads to perform designated tasks. However, existing research mainly focuses on the impact of component failures on system survivability while neglecting situations where the system only experiences functional failures. Additionally, the stochastic dependence between components is always neglected. To address the above problems, a dynamic mission abort method is developed. First, a mission abort policy is proposed to consider the stochastic dependence and mission payload, and the specific mission abort action is determined based on the health state of components and the time in the mission. Next, to maximize the expected cumulative reward during the mission, a dynamic mission abort decision-making model is established based on the Markov decision process. Then, to address the dimensionality curse caused by a continuous state space, a customized deep reinforcement learning method is developed, where the parameter sharing technique is used to reduce the model parameters of the network. Finally, the effectiveness of the proposed method is verified through a numerical example of a reconnaissance UAV, and the superiority of the proposed method is demonstrated by comparing it with heuristic policies. [ABSTRACT FROM AUTHOR]

The pulsatile-release Chronotopic™ system was conceived of as a drug-containing core surrounded by a coat made of swellable/soluble hydrophilic polymers, the latter being able to provide a programmable lag phase prior to drug liberation. This system was also proposed in a colon-targeting configuration, entailing a gastroresistant film to prevent early interaction of the inner coat with gastric fluids and enabling the attainment of a lag phase matching the small intestinal transit time. Over the years, various multiple-step manufacturing processes have been tested for the fabrication of the Chronotopic™ system in both its configurations. This work focused on the evaluation of 3D printing by fused deposition modeling in view of its potential towards product personalization, on demand one-step manufacturing and efficient scale down of batches. The feasibility of each part of the Chronotopic™ system was independently investigated starting from in-house made filaments, characterizing the resulting specimens for physico-technological and performance characteristics. The printing parameters identified as suitable during the set-up phase were then used to fabricate prototypes either in a single step for the pulsatile configuration or following two different fabrication approaches for the colon-targeting one.

Highlights • Model discrepancy prediction for dynamic system using Bayesian state estimation. • Using neural network as a surrogate sensor. • Predicting discrepancy for multi-component systems under untested loading. Abstract We present a methodology to predict the discrepancy in modeling dynamic system response for untested system inputs. In particular, the methodology focuses on multi-component systems, where discrepancy in the system response is caused by model form errors in some of the system components. We combine a deterministic time-series artificial neural network (ANN) with Bayesian state estimation to formulate a relationship between inputs to the system and the corresponding discrepancies in the model estimates of system responses. We also identify the system components affected by model form errors, and predict discrepancies even in unmeasured locations of the system. The proposed approach is illustrated on a linear multi-degree of freedom dynamic system, and on an air cycle machine – a multi-component dynamic system. [ABSTRACT FROM AUTHOR]

Intrinsically conducting polymers (ICP) and conductive fillers incorporated conductive polymer-based composites (CPC) greatly facilitate the research in electromagnetic interference (EMI) shielding because they not only provide excellent EMI shielding but also have advantages of electromagnetic wave absorption rather than reflection. In this review, the latest developments in ICP and CPC based EMI shielding materials are highlighted. In particular, existing methods for adjusting the morphological structure, electric and magnetic properties of EMI shielding materials are discussed along with the future opportunities and challenges in developing ICP and CPC for EMI shielding applications. [ABSTRACT FROM AUTHOR]

Highlights • Accurate estimation of component loading conditions in complex systems for life prediction model performance improvement. • Insights of components states for reliability assessment. • Informational criterion for practicability of recovering component observability in combined data-driven and domain knowledge condition. • Modeling of systems under measurement deficient condition. Abstract Techniques addressing the loading condition of components in complex systems are of great significance for the real-time reliability analyses of systems. To recover component observabilities with combined condition monitoring data and empirical rules, an information criterion identifying the necessary data/rule set for the modeling of systems with the same hierarchical topologies to real-in-world realizations, referred to as trans-layer model learning (TLML), is proposed and proved. Then, with regard to general multi-component dynamic systems, a specific TLML algorithm is proposed. In this algorithm, the loss function and alternative training scheme of component models are specified for harnessing the information from sensor readings and empirical rules to serve the modeling. TLML is applied first on a simulation system to testify its ability to reveal component loading conditions, and then on an aircraft engine to test its effectiveness in improving the Residual Useful Life (RUL) prediction performance of engine turbine blades. Results show that TLML can provide real-time estimations of component loading conditions with sufficient accuracy, and thus improve the precision and reliability of the RUL estimation of system parts. [ABSTRACT FROM AUTHOR]

Abstract Layout design of multi-component systems has received ever-increasing attention over recent years. However, most of the current literature on the layout design of multi-component systems conducted are all carried out in an implicit way. In this paper, an explicit optimization model based on the moving morphable bars method is proposed. To this aim, topology description functions are used to describe the geometrical shapes of embedded components, while moving bars act as supporting structure that connects these embedded components. Different from traditional optimization methods, the geometric parameters used to describe the size, shape, location and orientation of the moving bars and embedded components are considered as design variables in this work. To avoid remeshing the grids and improve the efficiency of computation, the moving bars and embedding components are mapped into two density fields on a fixed grid using a smoothed Heaviside function. A discrete material interpolation scheme developed for orientation optimization problem is extended for the first time to the material parameterization of multi-component systems. Moreover, a single explicit constraint based on the volume and perimeter of embedded components is proposed for avoiding overlaps between the embedded components, and between each component and the design domain boundary. Several numerical examples are performed to show the effectiveness and flexibility of the presented optimization model in handling layout optimization of structures with multi-phase embedded components. [ABSTRACT FROM AUTHOR]

• Binary liquid mixtures with a miscibility gap ending in a critical point of solution can be used as solvents. • At the critical point, the solubility limit of a solid deviates from the van't Hoff law. • Deviations are in the positive sense when dissolution is exothermic, in the negative sense when dissolution if endothermic. • Deviations are observed in the case of metal oxides and metal salts dissolving in a mixture of isobutyric acid + water. • As predicted by the critical point isomorphism concept, the deviations vanish upon addition of a completely soluble solute. A mixture of isobutyric acid + water has a critical point of solution at a composition of 38.8 mass % isobutyric acid and a temperature near 300 K. Under these conditions, the pH = 2, which makes the mixture a ready solvent for metal salts and basic/amphoteric oxides. Measurements of the temperature dependence of the solubility of a solid in the critical region of such a mixture can be used as a test of the Principle of Critical Point Universality. This inductive generalization, which is thought to govern all critical phenomena, predicts that there will be a critical effect in the solubility of a solid when at constant temperature and pressure the position of equilibrium is specified by no more than three fixed variables. We report herein a determination of the temperature dependence of the solubility of gallium (III) oxide in a mixture of isobutyric acid + water, and in a separate experiment the determination of the temperature dependence of the solubility of titanium dioxide in this solvent. In a third experiment, we report the effects of the critical point of this mixture on the simultaneous dissolution of calcium sulfate and barium sulfate. In each of these experiments, there are three fixed variables. We carry out a fourth experiment involving the solubility of solid lead (II) iodide in isobutyric acid + water doped with completely dissolved sodium sulfate. In this case, four variables are fixed. Independent of the chemical details, and consistent with the universality principle, we find a critical solubility effect in each of the first three experiments but none in the fourth. [ABSTRACT FROM AUTHOR]

Several industrial systems are required to carry out a sequence of missions in which the maintenance activity is allowed only between two consecutive missions. In such case, the selective maintenance strategy is widely adopted, which aims to select the best components to be maintained, taking into account the constrained maintenance resources. The suggested maintenance selective resolution methods in the literature are based essentially on theoretical analyses and estimated parameters, which does not necessarily reflect the reality. In this study, we propose to combine techniques based on data mining approach to the selective maintenance of a multi-component system, using the maintenance data collection. For this purpose, the similar components are clustered, as a first step, and the age degradation coefficient of similar components is computed by using the K-means clustering algorithm. Next, we propose a mixed integer programming model that uses as input data the components degradation coefficients of the first step, in order to decide which components need to be replaced first. After, using the Apriori algorithm, the frequent sequences of maintained component are extracted, which provides us additional knowledge to predict the next maintenance activity. Finally, the proposed approach is assessed through a real-world case study of maintenance data of a flexible manufacturing system. [ABSTRACT FROM AUTHOR]

Assessing the reliability of complex multi-state systems comprising numerous sub-systems and components is one of the key challenges faced by industry. The effect of degradation of individual component on other associated components plays a vital role in entire system transition from a higher performance level to a lower performance level. The proposed work focuses on the effect of dependence or functional association among various components on system reliability. There are many components in any complex system that hold higher critical order and failure of these critical components may cause the decline in the performance level of the total system. This paper takes under consideration the critical order of each component along with their dependent nature and captures the effect of their degradation on overall system reliability. [ABSTRACT FROM PUBLISHER]

Knowledge about methods and materials and their correct usage is the basis for compression therapy. This study compares knowledge and practical skills of participants with further training with those who had no training. This comparison provides information on whether further qualifications have an impact on knowledge and practical skills. In seminars for compression therapy, data on specific and non-specific expertise were acquired. A practical test determined the participants' skills for creating a compression bandage in a pressure value range of 50-60 mmHg. In total, 1338 participants with specific expertise and 138 participants with non-specific expertise took part. Knowledge evaluation showed that 7·9% of the specific expertise group had knowledge regarding padding, 10% regarding multi-component systems and 13·6% regarding ulcer stocking systems. In the practical test, 12·3% of all participants achieved the target range. The majority of users in both groups is not familiar with the different compression materials or their appropriate usage. In the non-specific expertise group, knowledge concerning up-to-date materials and methods is one-third lower. The practical test showed major deficits on both sides. Appropriate exercises, for example, with pressure-measuring devices, should be mandatory for all professional groups who perform compression bandaging. [ABSTRACT FROM AUTHOR]

Industrial systems are subject to several maintenance tasks during their operational life. To ensure optimal performance of these systems, it is crucial to build maintenance models that describe the real behavior of the system. In this scope, the herein paper provides an original approach for maintenance efficiency modeling of multi-component systems. The proposed approach seeks to model the behavior of multi-component system after the replacement of one or more of its components and evaluate this effect. As a matter of fact, from maintenance efficiency point of view, the replacement of one component can be considered as perfect maintenance for the component itself but imperfect for the whole system. To this end, a hybrid model, which considers geometric adjustment of failure intensity, proportional adjustment of failure intensity and proportional adjustment of age is provided. Then, an efficient procedure based on Bayesian networks is provided for the assessment of the imperfect maintenance model parameters. Finally, the proposed model is compared to the most common imperfect maintenance models. • A hybrid imperfect maintenance model is proposed. • The proposed model can represent various imperfect maintenance situations. • Imperfect maintenance effect is assessed after system component replacement. • An efficient heuristic approach for the maintenance effect assessment is proposed. • The proposed approach effectiveness is illustrated through numerical applications. [ABSTRACT FROM AUTHOR]

This paper presents a two-component load-sharing system. The failure rates of the two components are time dependent and load dependent. Whenever one fails, it is imperfectly repaired with a time delay during which the failure rate of the survival component increases because of the resulting overload. Three maintenance policies are proposed considering imperfect preventive maintenance and system replacement. The optimal average costs in the long run under different maintenance policies are derived from the theoretical propositions. Sensitivity analyses through numerical examples are carried out. [ABSTRACT FROM AUTHOR]

We analyze the performance of an aggregation and disaggregation procedure in giving the optimal maintenance decisions for a multi-component system under partial observations in a finite horizon. The components deteriorate in time and their states are hidden to the decision maker. Nevertheless, it is possible to observe signals about the system status and to replace components in each period. The aim is to find a cost effective replacement plan for the components in a given time horizon. The problem is formulated as a partially observable Markov decision process (POMDP). We aggregate states and actions in order to reduce the problem space and obtain an optimal aggregate policy which we disaggregate by simulating it using dynamic Bayesian networks (DBN). The procedure is statistically compared to an approximate POMDP solver that uses the full state space information. Cases where aggregation performs relatively better are isolated and it is shown that k-out-of-n systems belong to this class. [ABSTRACT FROM AUTHOR]

Last-minute maintenance planning is often undesirable, as it may cause downtime during operational hours, may require rescheduling of other activities, and does not allow to optimize the management of spare parts, material, and personnel. In spite of the aforementioned drawbacks of last-minute planning, most existing methods plan maintenance activities at the last minute. In this paper, we propose a new strategy for timely maintenance planning in multi-component systems. As a first step, we determine for each system component independently the most appropriate maintenance planning strategy. This way, the maintenance decisions can be tailored to the specific situations. For example, conservative maintenance decisions can be taken when the risk tolerance is low, and maintenance decisions can be made timely when we can accurately predict future degradation behavior. In the second step, we optimize the maintenance plan at the system level. Here, we account for economic and structural dependence with the aim to profit from spreading or combining various maintenance activities. The applicability of the method is demonstrated on a railway case. It is shown how the different cost functions (e.g. costs of maintenance, downtime, and failure) influence the maintenance decisions. [ABSTRACT FROM AUTHOR]

This paper develops a condition-based maintenance (CBM) model for a multi-component system operating under a dynamic environment. The degradation process of each component depends on both its intrinsic characteristic and the common operating environment. We model the environment evolution by a continuous-time Markov process, given which, the degradation increment of each component is described by a Poisson distribution. System reliability is firstly obtained, followed by a CBM policy to sustain system operation and ensure safety. In modelling the environmental effect on component degradation processes, two scenarios are considered. The first scenario considers renewable environment evolution while the second scenario on non-renewable environment evolution. The problem is casted into the Markov decision process (MDP) framework where the total expected discounted cost in the long-run horizon is utilized as the optimization objective to assess the policy. Structural properties of the optimal maintenance policy are investigated under mild conditions, which are further embedded into the value iteration algorithm to reduce the computational burden in calculating the maintenance cost. Applicability of the proposed model is illustrated through numerical examples. • We investigate the reliability and a CBM policy for a multi-component components. • The system is generic and no specific system configuration is assumed. • We discuss two types of environment effects on the maintenance strategy. • Under mild conditions, the structural properties of the policy are derived. [ABSTRACT FROM AUTHOR]