Valves are crucial components of a hydraulic system that enable reliable fluid management. Hydraulic valves, actuated by a solenoid are prone to degradation in their switching behavior, which may induce undesirable fluctuations in the fluid pressure and flow rate, thereby impairing the system performance and limiting its predictability and reliability. Therefore, it is imperative to monitor the switching behavior of solenoid-actuated hydraulic valves. Firstly, Recurrence Quantification Analysis (RQA) has been applied to the experimental flow sensor signals from a hydraulic circuit to understand the complex switching behaviour of the valve. Using RQA the monotonicity of six recurrence-based parameters has been assessed. In addition, two more nonlinear features, namely, Higuchi and Katz fractal dimensions have been extracted from the flow signals. Based on these eight features (six RQA derived features and two nonlinear features) a feature matrix is formulated. Secondly, in a parallel approach, eight different statistical features are extracted from the flow signal to construct another feature matrix. Subsequently, different machine learning methods namely Ensemble learning, KNN and SVM have been trained to these two feature sets to predict the valve switching characteristics. A comparison between two feature sets shows that ensemble learning gives better prediction accuracy (99.95 % versus 92.2 % using statistical features) when fed with RQA features combined with fractal dimensions. Therefore, this study demonstrates by utilizing recurrence plots and machine learning techniques on the flow rate signals, the degradation in the switching behavior of hydraulic valves can be monitored effectively with high prediction accuracy.

Modern design problems often require multi-modal, reconfigurable solutions. Function modeling is a common tool used to explore solutions in early mechanical design. Currently, function modeling formalisms minimally support the modeling of multi-modal systems in a formal manner. There is a need in function modeling to capture multi-modal system and analyze the effects of control signals and status signals on their operating modes. This paper presents the concept of functional conjugacy, where two function verbs or functional subgraphs are topological opposites of each other. The paper presents a formal representation of these conjugate verbs that formally captures the transition from one mode of operation to its topological opposite based on the existence of, or the value of, signal flows. Additionally, this paper extends functional conjugacy to functional features, which supports conjugacy-based reasoning at a higher level of abstraction. Through the example of a system-level function model of a geothermal heat pump operating in its heating and cooling modes, this paper demonstrates the ability to support modal reasoning on function models using functional conjugacy and illustrates the modeling efficacy of the extended representation.

Process uncertainty can have negative effects on part quality and is, therefore, critical to the safety and performance of products. Those effects are manifested in the dimensional measurement uncertainty associated with those parts and products. To minimize the effects of process uncertainties, the sources of dimensional uncertainty must be identified and clearly communicated to collaborators and suppliers. A principal source of dimensional uncertainty is the measurement equipment itself. This paper presents an activity model, rule types, and sample rules for selecting dimensional-metrology equipment. The activity model represents key operations and information flows associated with dimensional measurement. Analysis of the included activity model facilitates the development of rule types for measurement-equipment selection as described in the Quality Information Framework (QIF) standard. Rule types are based on design information and measurement requirements. Standard rule types enable industrial metrologists to capture, exchange, and share equipment-selection rules with their collaborators. Example QIF rules are defined for successful and cost-saving use in planning a measurement process with functionally complex and appropriate dimensional-measurement equipment.

Converting a hex mesh into a fundamental mesh by inserting fundamental sheets is an effective means to improve the hex mesh’s quality near the boundary. However, the high-quality and automatic fundamental sheets insertion is still a problem. In this paper, a method is proposed to automatically generate fundamental sheets with the support of stream surfaces. By establishing a constrained integer linear system, the types of fundamental sheets to be inserted are determined effectively and optimally. By constructing discrete stream surfaces associated with the relevant geometric entities, the optimized positions of fundamental sheets are automatically determined. The experimental results show that the proposed method can automatically insert high-quality fundamental sheets and effectively improve the elements’ geometric quality of the hex mesh.

Given the characteristics of decommissioning product diversity, wear difference, and structural complexity, we analyzed the disassembly line balance problems of minimum disassembly workstation, station load balancing, priority disassembly of high-demand parts and the lowest cost of invalid operations, and further considered the influence of random factors on actual disassembly operations. The purpose of this paper is to establish a model of sequence-dependent stochastic mixed-flowed partial disassembly line balancing problem and propose an adaptive hybrid particle swarm genetic algorithm to solve the model. The algorithm replaces the fixed value genetic operator with an adaptive crossover and mutation operator to improve the global optimization ability. The introduction of adaptive weighted particles leads to improved local optimization ability of the algorithm so that the algorithm has strong global and regional optimization ability to improve algorithm accuracy. The effectiveness of the proposed algorithm is verified by solving the classic disassembly line balancing problem with different scales and comparing it with the solution results of underlying genetic and particle swarm optimization algorithm. Meanwhile, the proposed model and algorithm are applied to the mixed-flow disassembly engineering project of a 25-task reducer. The results indicate that the proposed model is superior to the control group in terms of minimal disassembly workstation, station load balancing, invalid operation cost, and overall performance of the disassembly line, which verifies the validity of the model.

Increasingly tight coupling and heavy connectedness in system of systems (SoS) present new problems for systems’ designers and engineers. While the failure of one system within a loosely coupled SoS may produce little collateral damage beyond a loss in SoS capability, a highly interconnected SoS can experience significant damage when one member system fails in an unanticipated way. It is therefore important to develop systems that are “good neighbors” with the other systems in an SoS by failing in ways that do not further degrade an SoS’s ability to complete its mission. This paper presents a method to (1) analyze a system of interest (SoI) for potentially harmful spurious system emissions (failure flows that exit the SoI’s system boundary and may cause failure initiating events in other systems within the SoS) and (2) choose mitigation strategies that provide the best return on investment for the SoS. The method is intended for use during the system architecture phase of the system design process when functional architectures are being developed, and analysis of alternatives and trade-off studies are being conducted 2 .

In graph-based function models, the function verbs and flow nouns are usually chosen from predefined vocabularies. The vocabulary class definitions, combined with function modeling grammars defined at various levels of formalism, enable function-based reasoning. However, the text written in plain English for the names of the functions and flows is presently not exploited for formal reasoning. This paper presents a formalism (representation and reasoning) to support semantic and physics-based reasoning on the information hidden in the plain-English flow terms, especially for automatically decomposing black box function models, and to generate multiple design alternatives. First, semantic reasoning infers the changes of flow types, flow attributes, and the direction of those changes between the input and output flows attached to the black box. Then, a representation of qualitative physics is used to determine the material and energy exchanges between the flows and the function features needed to achieve them. Finally, a topological reasoning is used to infer multiple options of composing those function features into topologies and to thus generate multiple alternative decompositions of the functional black box. The data representation formalizes flow phases, flow attributes, qualitative value scales for the attributes, and qualitative physics laws. An eight-step algorithm manipulates these data for reasoning. This paper shows four validation case studies to demonstrate the workings of this formalism.

A modular product architecture is a strategic means to deliver external variety and internal commonality. In this paper, we propose a new clustering-based method for product modularization that integrates product complexity and company business strategies. The proposed method is logically verified by a studied industrial case, where the architecture of a heavy truck driveline is analyzed in terms of how it has evolved over a couple of decades, due to changed business strategies and the evolution of new technology. The presented case indicates that the new methodology is capable of identifying and proposing reasonable module candidates that address product complexity as well as company-specific strategies. Furthermore, the case study clearly shows that the business strategic reasons for a specific architecture can be found by analyzing how sensitive the clusters are to changes in the module drivers (MD).

A challenge systems engineers and designers face when applying system failure risk assessment methods such as probabilistic risk assessment (PRA) during conceptual design is their reliance on historical data and behavioral models. This paper presents a framework for exploring a space of functional models using graph rewriting rules and a qualitative failure simulation framework that presents information in an intuitive manner for human-in-the-loop decision-making and human-guided design. An example is presented wherein a functional model of an electrical power system testbed is iteratively perturbed to generate alternatives. The alternative functional models suggest different approaches to mitigating an emergent system failure vulnerability in the electrical power system's heat extraction capability. A preferred functional model configuration that has a desirable failure flow distribution can then be identified. The method presented here helps systems designers to better understand where failures propagate through systems and guides modification of systems functional models to adjust the way in which systems fail to have more desirable characteristics.

In most city water distribution systems, a considerable amount of water is lost because of leaks occurring in pipes. Moreover, an unobservable fluid leakage fault that may occur in a hazardous industrial system, such as nuclear power plant cooling process or chemical waste disposal, can cause both environmental and economical disasters. This situation generates crucial interest for industry and academia due to the financial cost related with public health risks, environmental responsibility, and energy efficiency. In this paper, to find a reliable and economic solution for this problem, adaptive neuro fuzzy inference system (ANFIS) method which consists of backpropagation and least-squares learning algorithms is proposed for estimating leakage locations in a complex water distribution system. The hybrid algorithm is trained with acceleration, pressure, and flow rate data measured through the sensors located on some specific points of the complex water distribution system. The effectiveness of the proposed method is discussed comparing the results with the current methods popularly used in this area.

The design of complex engineering systems requires that the problem is decomposed into subproblems of manageable size. From the perspective of decision-based design (DBD), typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision-making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here, we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled decision support problem (DSP) construct, and two key classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.

Modern cyber-physical production systems (CPPS) connect different elements like machine tools and workpieces. The constituent elements are often equipped with high-performance sensors as well as information and communication technology, enabling them to interact with each other. This leads to an increasing amount and complexity of data that requires better analysis tools to support system refinement and revision performed by an expert. This paper presents a user-guided visual analysis approach that can answer relevant questions concerning the behavior of cyber-physical systems. The approach generates visualizations of aggregated views that capture an entire production system as well as specific characteristics of individual data features. To show the applicability of the presented methodologies, an exemplary production system is simulated and analyzed.

Simulation-based methods are emerging to address the challenges of complex systems risk assessment, and this paper identifies two problems related to the use of such methods. First, the methods cannot identify new hazards if the simulation model builders are expected to foresee the hazards and incorporate the abnormal behavior related to the hazard into the simulation model. Therefore, this paper uses the concept of deviation from design intent to systematically capture abnormal conditions that may lead to component failures, hazards, or both. Second, simulation-based risk assessment methods should explicitly consider what expertise is required from the experts that build and use the simulation models—the transfer of the methods to real engineering practice will be severely hindered if they must be performed by persons that are expert in domain safety as well as advanced computer simulation-based methods. This paper addresses both problems in the context of the functional failure identification and propagation (FFIP) method. One industrially established risk assessment method, hazard and operability study (HAZOP), is harnessed to systematically obtain the deviations from design intent in the application under study. An information system presents a user interface that is understandable to HAZOP professionals, so that their inputs are transparently entered to a data model that captures the deviations. From the data model, instructions for configuring FFIP simulation models are printed in a form that is understandable for FFIP experts. The method is demonstrated for discovering a hazard resulting from system-wide fault propagation in a boiling water reactor case.

The paper presents a formal representation for modeling function structure graphs in a consistent, grammatically controlled manner, and for performing conservation-based formal reasoning on those models. The representation consists of a hierarchical vocabulary of entities, relations, and attributes, and 33 local grammar rules that permit or prohibit modeling constructs thereby ensuring model consistency. Internal representational consistency is verified by committing the representation to a Protégé web ontology language (OWL) ontology and examining it with the Pellet consistency checker. External representational validity is established by implementing the representation in a Computer Aided Design (CAD) tool and using it to demonstrate that the grammar rules prohibit inconsistent constructs and that the models support physics-based reasoning based on the balance laws of transport phenomena. This representation, including the controlled grammar, can serve, in the future, as a basis for additional reasoning extensions.

We present a framework for modeling and analysis of real-world business workflows. We present a formalized core subset of the business process modeling and notation (BPMN) and then proceed to extend this language with probabilistic nondeterministic branching and general-purpose reward annotations. We present an algorithm for the translation of such models into Markov decision processes (MDP) expressed in the syntax of the PRISM model checker. This enables precise quantitative analysis of business processes for the following properties: transient and steady-state probabilities, the timing, occurrence and ordering of events, reward-based properties, and best- and worst- case scenarios. We develop a simple example of medical workflow and demonstrate the utility of this analysis in accurate provisioning of drug stocks. Finally, we suggest a path to building upon these techniques to cover the entire BPMN language, allow for more complex annotations and ultimately to automatically synthesize workflows by composing predefined subprocesses, in order to achieve a configuration that is optimal for parameters of interest.

This paper validates that a previously published formal representation of function structure graphs actually supports the reasoning that motivated its development in the first place. In doing so, it presents the algorithms to perform those reasoning, provides justification for the reasoning, and presents a software implementation called Concept Modeler (ConMod) to demonstrate the reasoning. Specifically, the representation is shown to support constructing function structure graphs in a grammar-controlled manner so that logical and physics-based inconsistencies are prevented in real-time, thus ensuring logically consistent models. Further, it is demonstrated that the representation can support postmodeling reasoning to check the modeled concepts against two universal principles of physics: the balance laws of mass and energy, and the principle of irreversibility. The representation in question is recently published and its internal ontological and logical consistency has been already demonstrated. However, its ability to support the intended reasoning was not validated so far, which is accomplished in this paper.

Emergent behavior is a unique aspect of complex systems, where they exhibit behavior that is more complex than the sum of the behavior of their constituent parts. This behavior includes the propagation of faults between parts, and requires information on how the parts are connected. These parts can include software, electronic and mechanical components, hence requiring a capability to track emergent fault propagation paths as they cross the boundaries of technical disciplines. Prior work has introduced the functional failure identification and propagation (FFIP) simulation framework, which reveals the propagation of abnormal flow states and can thus be used to infer emergent system-wide behavior that may compromise the reliability of the system. An advantage of FFIP is that it is used to model early phase designs, before high cost commitments are made and before high fidelity models are available. This has also been a weakness in previous research on FFIP, since results depend on arbitrary choices for the values of model parameters and timing of critical events. Previously, FFIP has used a discrete set of flow state values and a simple behavioral logic; this has had the advantage of limiting the range of possible parameter values, but it has not been possible to model continuous process dynamics. In this paper, the FFIP framework has been extended to support continuous flow levels and linear modeling of component behavior based on first principles. Since this extension further expands the range of model parameter values, methods and tools for studying the impact of parameter value changes are introduced. The result is an evaluation of how the FFIP results are impacted by changes in the model parameters and the timing of critical events. The method is demonstrated on a boiling water reactor model (limited to the coolant recirculation and steam outlets) in order to focus the analysis of emergent fault behavior that could not have been identified with previously published versions of the FFIP framework.

Speed perception is an important task depending mainly on optic flow that the driver must perform continuously to control his/her vehicle. Unfortunately, it appears that in some driving simulators speed perception is under estimated, leading into speed production higher than in real conditions. Perceptual validity is then not good enough to study driver’s behavior. To solve this problem, a technique has recently seen the light, which consists of modifying the geometric field of view (GFOV) while keeping the real field of view (FOV) constant. We define our visual scale factor as the ratio between the GFOV and the FOV. The present study has been carried out on the SAAM dynamic driving simulator and aims at determining the precise effect of this visual scale factor on the speed perception. Twenty subjects have reproduced two speeds (50 and 90 km/h) without knowing the numerical values of these consigns, with five different visual scale factors: 0.70, 0.85, 1.00, 1.15, and 1.30. We show that speed perception significantly increases when the visual factor increases. A 0.15 modification of this factor is enough to obtain a significant effect. Furthermore, the relative variation of the speed perception is proportional to the visual scale factor. Besides, the modification of the geometric field of view remained unnoticed by all the subjects, which implies that this technique can be easily used to make drivers to reduce their speed in driving simulation conditions. However, this technique may also modify perception of distances.

This paper presents a formalization of the notion of function as operation on flows as advanced in the Functional Basis approach of Stone and Wood. We first analyze the modeling of functions in this approach and identify the notions that are ontological significant for their formalization within the foundational ontology DOLCE. Then, we build the logical system in which this engineering notion of function is formally translated and connected to the ontology. Furthermore, we posit a number of constraints for a correct interpretation of the formal system and also provide a web ontology language version. We conclude with an assessment of our results and a discussion of our larger project aimed at analysing functional descriptions of technical artifacts, and at translating functional descriptions using different engineering notions of function.

The role of virtual environments (VEs) is crucial in efficient design and operation of unmanned vehicles. VEs are extensively used in operator training for tele-operation, planning using programming by demonstration, and hardware and software designs. VE for unmanned sea surface vehicles (USSV) requires a 6 degree of freedom dynamics simulation in the time domain. In order to be interactive, the VE requires real-time performance of the underlying dynamics simulator. In general, the dynamics simulation of USSVs involves the following four main operations: (1) computation of dynamic pressure head due to fluid flow around the hull under the ocean wave, (2) computation of wet surface, (3) computing the surface integral of the dynamic pressure head over the wet surface, and (4) solving the rigid body dynamics equation. The first three operations depend upon the boat geometry complexity and need to be performed at each time step, making the simulation run very slow. In this paper, we address the problem of physics preserving model simplification for real-time potential flow based simulator for a USSV in the time domain, with an arbitrary hull geometry. This paper reports model simplification algorithms based on clustering, temporal coherence, and hardware acceleration using parallel computing on multiple cores to obtain real time simulation performance for the developed VE.