The failure tolerance of mechatronic software systems to random and targeted attacks

This paper describes a complex networks approach to study the failure tolerance of mechatronic software systems under various types of hardware and/or software failures. We produce synthetic system architectures based on evidence of modular and hierarchical modular product architectures and known motifs for the interconnection of physical components to software. The system architectures are then subject to various forms of attack. The attacks simulate failure of critical hardware or software. Four types of attack are investigated: degree centrality, betweenness centrality, closeness centrality and random attack. Failure tolerance of the system is measured by a 'robustness coefficient', a topological 'size' metric of the connectedness of the attacked network. We find that the betweenness centrality attack results in the most significant reduction in the robustness coefficient, confirming betweenness centrality, rather than the number of connections (i.e. degree), as the most conservative metric of component importance. A counter-intuitive finding is that "designed" system architectures, including a bus, ring, and star architecture, are not significantly more failure-tolerant than interconnections with no prescribed architecture, that is, a random architecture. Our research provides a data-driven approach to engineer the architecture of mechatronic software systems for failure tolerance.Comment: Proceedings of the 2013 ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2013 August 4-7, 2013, Portland, Oregon, USA (In Print

A Comparison of Functional Models for Use in the Function-Failure Design Method

When failure analysis and prevention, guided by historical design knowledge, are coupled with product design at its conception, shorter design cycles are possible. By decreasing the design time of a product in this manner, design costs are reduced and the product will better suit the customer s needs. Prior work indicates that similar failure modes occur with products (or components) with similar functionality. To capitalize on this finding, a knowledge base of historical failure information linked to functionality is assembled for use by designers. One possible use for this knowledge base is within the Elemental Function-Failure Design Method (EFDM). This design methodology and failure analysis tool begins at conceptual design and keeps the designer cognizant of failures that are likely to occur based on the product s functionality. The EFDM offers potential improvement over current failure analysis methods, such as FMEA, FMECA, and Fault Tree Analysis, because it can be implemented hand in hand with other conceptual design steps and carried throughout a product s design cycle. These other failure analysis methods can only truly be effective after a physical design has been completed. The EFDM however is only as good as the knowledge base that it draws from, and therefore it is of utmost importance to develop a knowledge base that will be suitable for use across a wide spectrum of products. One fundamental question that arises in using the EFDM is: At what level of detail should functional descriptions of components be encoded? This paper explores two approaches to populating a knowledge base with actual failure occurrence information from Bell 206 helicopters. Functional models expressed at various levels of detail are investigated to determine the necessary detail for an applicable knowledge base that can be used by designers in both new designs as well as redesigns. High level and more detailed functional descriptions are derived for each failed component based on NTSB accident reports. To best record this data, standardized functional and failure mode vocabularies are used. Two separate function-failure knowledge bases are then created aid compared. Results indicate that encoding failure data using more detailed functional models allows for a more robust knowledge base. Interestingly however, when applying the EFDM, high level descriptions continue to produce useful results when using the knowledge base generated from the detailed functional models

Surface Characterization of Polycarbonate Parts from Selective Laser Sintering

Surfaces of polycarbonate Selective Laser Sintering parts are investigated to determine the characteristics affecting part quality. Surfaces are obtained from experiments by varying four factors, namely, layer thickness, laser power, part orientation, and build angle. First, spatial modes on SLS surfaces are decomposed using a qualitative spectral analysis in an attempt to find their origins. Thermal modes on the top surfaces of polycarbonate SLS parts result in the other modes being obscured; melting and part curl are concluded to be the dominant modes on these surfaces. Furthermore, surface modes resulting from building the part at an angle to the powder bed are identified and modeled. Then, mathematical measures are computed for the surfaces to determine surface precision quantitatively. An analysis-of-variance study is performed to reveal the trends in surface precision with respect to control factors. Surface precision is shown to change significantly with laser power and part orientation, and trade-offs with part strength are presented.Mechanical Engineerin

Risk Assessment in Early Software Design Based on the Software Function-Failure Design Method

Potential software failures present a sizable risk element in the design and development of many systems. In this paper, we augment the Software Function-Failure Design method, which is capable of predicting potential software failures in the very early stages of design, with the Risk in Early Design technique. This synergistic combination allows a risk assessment to be conducted at an early time in the software development process when traditional techniques are not applicable. The results are concise risk statements regarding the potential failure of functionalities with likelihood and consequence quantifications that can be used as part of a risk management program. The process is illustrated using a software failure database for the NASA Mars Exploratory Rover

Event Detection in Aerospace Systems using Centralized Sensor Networks: A Comparative Study of Several Methodologies

Recent advances in micro electromechanical systems technology, digital electronics, and wireless communications have enabled development of low-cost, low-power, multifunctional miniature smart sensors. These sensors can be deployed throughout a region in an aerospace vehicle to build a network for measurement, detection and surveillance applications. Event detection using such centralized sensor networks is often regarded as one of the most promising health management technologies in aerospace applications where timely detection of local anomalies has a great impact on the safety of the mission. In this paper, we propose to conduct a qualitative comparison of several local event detection algorithms for centralized redundant sensor networks. The algorithms are compared with respect to their ability to locate and evaluate an event in the presence of noise and sensor failures for various node geometries and densities

Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment

Complex engineered systems can carry risk of high failure consequences, and as a result, resilience-the ability to avoid or quickly recover from faults-is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures

Failure Analysis in Conceptual Phase toward a Robust Design: Case Study in Monopropellant Propulsion System

As a system becomes more complex, the uncertainty in the operating conditions increases. In such a system, implementing a precise failure analysis in early design stage is vital. However, there is a lack of applicable methodology that shows how to implement failure analysis in the early design phase to achieve a robust design. The main purpose of this paper is to present a framework to design a complex engineered system resistant against various factors that may cause failures, when design process is in the conceptual phase and information about detailed system and component is unavailable. Within this framework, we generate a population of feasible designs from a seed functional model, and simulate and classified failure scenarios. We also develop a design selection function to compare robust score for candidate designs, and produce a preference ranking. We implement the proposed method on the design of an aerospace monopropellant propulsion system

A case for trading risk in complex conceptual design trade studies

Complex conceptual system design trade studies traditionally consider risk after a conceptual design has been created. Further, one person is often tasked with collecting risk information and managing it from each subsystem. This paper proposes a method to explicitly consider and trade risk on the same level as other important system-level variables during the creation of conceptual designs in trade studies. The proposed risk trading method advocates putting each subsystem engineer in control of risk for each subsystem. A risk vector is proposed that organizes many different risk metrics for communication between subsystems. A method of coupling risk models to dynamic subsystem models is presented. Several risk visualization techniques are discussed. A trade study example is presented based upon a simplified spacecraft model. Results from introducing the risk trading methodology into a simulated Collaborative Design Center are presented. The risk trading method offers an approach to more thoroughly consider risk during the creation of conceptual designs in trade studies.Keywords: Complex system design, Risk, Collaborative Design Center risk trading, Trade stud

A Comparison of Creativity and Innovation Metrics and Sample Validation Through in-­class Design Projects

This paper introduces a new perspective/direction on assessing and encouraging creativity in concept design for application in engineering design education and industry. This research presents several methods used to assess the creativity of similar student designs using metrics and judges to determine which product is considered the most creative. Two methods are proposed for creativity concept evaluation during early design, namely the Comparative Creativity Assessment (CCA) and the Multi-Point Creativity Assessment (MPCA) methods. A critical survey is provided along with a comparison of prominent creativity assessment methods for personalities, products, and the design process. These comparisons culminate in the motivation for new methodologies in creative product evaluation to address certain shortcomings in current methods. The paper details the creation of the two creativity assessment methods followed by an application of the CCA and MPCA to two case studies drawn from engineering design classes.Keywords: engineering design, creativity assessment, innovation, concept desig

Resiliency analysis for complex engineered system design

Resilience is a key driver in the design of systems that must operate in an uncertain operating environment, and is a key metric to assess the capacity for systems to perform within the specified performance envelop despite disturbances to their operating environment. This paper describes a graph spectral approach to calculate the resilience of complex engineered systems. The resilience of the design architecture of complex engineered systems is deduced from graph spectra. This is calculated from adjacency matrix representations of the physical connections between components in complex engineered systems. Furthermore, we propose a new method to identify the most vulnerable components in the design and design architectures that are robust to transmission of failures. Non-linear dynamical system (NLDS) and epidemic spreading models are used to compare the failure propagation mean time transformation. Using these metrics, we present a case study based on the Advanced Diagnostics and Prognostics Testbed (ADAPT), which is an Electrical Power System (EPS) developed at NASA Ames as a subsystem for the Ramp System of an Infantry Fighting Vehicle (IFV).Keywords: Complex System Design, Failure Density, Failure Propagation, Robust Desig