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Redundancy verification of parallel systems related to element MTBF and decision maker perspective involving acceptance risk of system failure
The purpose of integrated hazard analyses, probabilistic risk assessments, failure modes and effects analyses, fault trees and many other similar tools is to give managers of a program some idea of the risks associated with their program. All risk tools establish a set of undesired events and then try to evaluate the risk to the program by assessing the severity of the undesired event and the likelihood of that event occurring. Some tools provide qualitative results, some provide quantitative results and some do both. However, in the end the program manager and his/her team must decide which risks are acceptable and which are not. Even with a wide array of analysis tools available, risk acceptance is often a controversial and difficult decision making process. And yet, today's space exploration programs are moving toward more risk based design approaches. Thus, risk identification and good risk assessment is becoming even more vital to the engineering development process. This paper explores how known and unknown information influences risk-based decisions by looking at how the various parts of our personalities are affected by what they know and what they don't know. This paper then offers some criteria for consideration when making risk-based decisions.
Over the last three decades, NASA has been diligent in qualifying systems for human space flight. As the Agency transitions from operating the Space Shuttle, its employees must learn to accept higher risk levels to generate the data needed to certify its next human space flight system. The Marshall Center s Engineering workforce is developing the Ares I crew launch vehicle and designing the Ares V cargo launch vehicle for safety, reliability, and cost-effective operations. This presentation will provide a risk retrospective, using first-hand examples from the Delta Clipper-Experimental Advanced (DC-XA) and the X-33 single-stage-to-orbit flight demonstrators, while looking ahead to the upcoming Ares I-X uncrewed test flight. The DC-XA was successfully flown twice in 26 hours, setting a new turnaround-time record. Later, one of its 3 landing gears did not deploy, it tipped over, and was destroyed. During structural testing, the X-33 s advanced composite tanks were unable to withstand the forces to which it was subjected and the project was later cancelled. These are examples of successful failures, as the data generated are captured in databases used by vehicle designers today. More recently, the Ares I-X flight readiness review process was streamlined in keeping with the mission's objectives, since human lives are not at stake, which reflects the beginning of a cultural change. Failures are acceptable during testing, as they provide the lessons that actually lead to mission success. These and other examples will stimulate the discussion of when to accept risk in aerospace projects.
A panel of experts was convened in 2010 to help define acceptable injury risk levels for space vehicle launches, landings, and abort scenarios. Classifications of spaceflight-relevant injuries were defined using four categories ranging from minor to severe injury. Limits for each injury category were agreed to, dependent on the expected number of crew exposures in a given vehicle and on whether the flight was considered nominal or off-nominal. Somers et al. captured the findings of this summit in a NASA technical memorandum. This panel was recently re-convened (December 1, 2014) to determine whether the previous recommended injury limits were applicable to newly-designed commercial space flight vehicles. In particular, previous limits were based in part on the number of crew exposures per vehicle and also were sensitive to a definition of nominal and off-nominal vehicle performance. Reconsideration of these aspects led to a new consensus on a definition of injury risk.
The perspective of those assuming risk has become increasingly important to launch agencies. The IAASS white paper "An ICAO for Space?" proposed four ultimate goals of any international regulatory framework. The first of these was to "Ensure that citizens of all nations are equally protected from "unreasonable levels" of risk from overflight by missiles, launch vehicles and returning spacecraft". A key component of this concept is the issue of what is an "unreasonable level" of risk from the perspective of those assuming the risk.
The human exploration of Mars will impose unavoidable health risks from galactic cosmic rays (GCR) and possibly solar particle events (SPE). It is the goal of NASA's Space Radiation Health Program to develop the capability to predict health risks with significant accuracy to ensure that risks are well below acceptable levels and to allow for mitigation approaches to be effective at reasonable costs. End-to-End risk assessment is the approach being followed to understand proton and heavy ion damage at the molecular, cellular, and tissue levels in order to predict the probability of the major health risk including cancer, neurological disorders, hereditary effects, cataracts, and acute radiation sickness and to develop countermeasures for mitigating risks.
The NASA Governance model directed the formation of three Technical Authorities, Engineering; Safety and Mission Assurance; and Health and Medical, to ensure that risks are identified and adjudicated efficiently and transparently in concert with the spaceflight programs and projects. The Health and Medical Technical Authority (HMTA) has been implemented at the Johnson Space Center (JSC) and consists of the Chief Medical Office (CMO), the Deputy CMO, and HMTA Delegates. The JSC HMTA achieves the goals of risk identification and adjudication through the discharge of the appropriate technical expertise to human space flight programs and projects and the escalation of issues within program and technical authority boards. The JSC HMTA relies on subject matter experts (SMEs) in the Space Life Sciences Directorate at JSC as well as experts from other Centers to work crew health and performance issues at the technical level, develop requirements, oversee implementation and validation of requirements, and identify risks and non-compliances. Once a risk or potential noncompliance has been identified and reported to the programs or projects, the JSC HMTA begins to track it and closely monitor the program's or project's response. As a risk is developed or a non-compliance negotiated, positions from various levels of decision makers are sought at the program and project control boards. The HMTA may support a program or project position if it is satisfied with the decision making and vetting processes (ex. the subject matter expert voiced his/her concerns and all dissenting opinions were documented) and finds that the position both acknowledges the risk and cost of the mitigation and resolves the issue without changing NASA risk posture. The HMTA may disagree with a program or project position if the NASA risk posture has been elevated or obfuscated. If the HMTA does disagree with the program or project position, it will appeal to successively higher levels of authority so that risk acceptance and risk trades will be acknowledged and sanctioned at the highest appropriate level; this includes Program Managers, Mission Directorate Associate Administrators and the Agency Administrator.
One of the more misunderstood areas of metrology is the Test Uncertainty Ratio (TUR) and its cousin, the Test Accuracy Ratio (TAR). There have been many definitions over the years, but why are these definitions important to a discussion on measurement decision risk? The importance lies in the clarity of communication. Problems can immediately arise in the application (or misapplication) of the definition of these terms. In other words, while it is important to understand the definitions, it is more important to understand concepts behind the definitions and to be precise in how they are applied. The objective of any measurement is a decision. Measurement Decision Risk is a way to look at the quality of a measurement and although it is not a new concept, it has generated a lot of attention since its addition as a requirement in the new U.S. National Standard, ANSI/NCSL Z540.3-2006. In addition to Measurement Decision Risk as the prime method of managing measurement risk, Z540.3 has also added, as a fall-back, an explicit definition for TUR. The impact these changes might have on calibration service providers if these requirements are levied on them has become the topic of much discussion and in some cases concern. This paper looks at the concepts behind the definitions and how they relate to Measurement Decision Risk. Using common examples, this paper will also provide a comparison of various elements of risk related to measurement science using the concepts of TAR, TUR, accuracy ratios, and Consumer Risk (False Accept Risk). The goal is to provide a better understanding of their relevance to the measurement decision process.
One of the more misunderstood areas of metrology is the Test Uncertainty Ratio (TUR) and the Test Accuracy Ratio (TAR). There have been many definitions over the years, but why are these definitions important to a discussion on measurement decision risk? The importance lies in the clarity of communication. Problems can immediately arise in the application (or misapplication) of the definition of these terms. In other words, while it is important to understand the definitions, it is more important to understand concepts behind the definitions and to be precise in how they are applied. The objective of any measurement is a decision. Measurement Decision Risk is a way to look at the quality of a measurement, and although it is not a new concept, it has generated a lot of attention since its addition as a requirement in the new U.S. National Standard, ANSIINCSL Z540.3-2006. In addition to Measurement Decision Risk as the prime method of managing measurement risk, Z540.3 has added, as a fall-back, an explicit definition for TUR. The impact these new requirements may have on calibration service providers has become the topic of much discussion and in some cases concern. This paper will look at the concepts behind the definitions and how they relate to Measurement Decision Risk. Using common examples, this paper will also provide a comparison of various elements of risk related to measurement science using the concepts of TAR, TUR, accuracy ratios, and Consumer Risk (False Accept Risk). The goal of this paper is to provide a better understanding of their relevance to the measurement decision process.
This paper presents the objectives-driven, case-based safety and mission success (S&MS) assurance framework being developed by the NASA Office of Safety and Mission Assurance (OSMA), including its motivations and its implementation via a S&MS Assurance Standard that is under development, supplemented by supporting standards including an S&MS Analysis Management Standard that is also under development. A need to evolve NASA’s S&MS assurance framework has emerged in recent years, resulting from the need to accommodate new acquisition models; the need to accommodate evolving systems engineering (SE) practices; the need to stipulate acceptable levels of S&MS risk; the need for improved integration of S&MS into SE; and the need for clearer risk acceptance accountability. The objectives-driven, case-based S&MS assurance framework proposed here is responsive to that need. Its key features include: • The establishment, by NASA Acquirers, of fundamental S&MS performance objectives that define limits of acceptability for the likelihoods that mission technical objectives will be accomplished and that people, assets, and environments put at risk by the mission will not be adversely affected; • The development and approval of Providers’ S&MS plans for meeting Acquirers’ S&MS performance objectives, including commitments to support Acquirer audit, investigation, and reporting needs; • The development, by Providers, of S&MS assurance cases that argue, supported by evidence, that the Provider has met, or is on track to meeting, the fundamental S&MS objectives; • The evaluation, throughout the program/project life cycle, of Provider S&MS assurance cases as the primary S&MS-related technical basis for Acquirer risk acceptance and the granting to the Provider of authority to proceed through the program/project life cycle. This proposed S&MS assurance framework is notable for its lack of prescription of traditional S&MS requirements and strategies such as defined failure tolerances, margins, or analysis requirements. Instead, Providers are given latitude to propose their own strategies for meeting the fundamental S&MS performance objectives, subject to independent review and Acquirer approval. The result is a framework for S&MS assurance that is at once both rigorous and flexible.
This talk is intended to help small project proposers to navigate the process of deciding what risks would be acceptable in their Class D projects, as well as steering away from misinterpretations of risk to mean “doing something different”. This will be largely NASA centric, largely focused on science missions, but there will be more general takeaways.
A reliability-based optimization methodology has been developed to design the raked wing tip of the Boeing 767-400 extended range airliner made of composite and metallic materials. Design is formulated for an accepted level of risk or reliability. The design variables, weight and the constraints became functions of reliability. Uncertainties in the load, strength and the material properties, as well as the design variables, were modeled as random parameters with specified distributions, like normal, Weibull or Gumbel functions. The objective function and constraint, or a failure mode, became derived functions of the risk-level. Solution to the problem produced the optimum design with weight, variables and constraints as a function of the risk-level. Optimum weight versus reliability traced out an inverted-S shaped graph. The center of the graph corresponded to a 50 percent probability of success, or one failure in two samples. Under some assumptions, this design would be quite close to the deterministic optimum solution. The weight increased when reliability exceeded 50 percent, and decreased when the reliability was compromised. A design could be selected depending on the level of risk acceptable to a situation. The optimization process achieved up to a 20-percent reduction in weight over traditional design.
NASA's Space Operations Management Office (SOMO) is working toward a goal of providing an integrated infrastructure of mission and data services for space missions undertaken by NASA enterprises. A significant portion of this effort is focused on reducing the cost of these services. We are interested in the potential of autonomy to reduce operations costs. SOMO services support space missions, but are not part of the mission objectives; therefore the level of acceptable risk is very low. In fact, SOMO could be effective ly prevented from applying autonomy if customers merely perceive it as adding risk to their mission(s). We are interested in this workshop from the standpoint of understanding what can be done to realize the potential cost savings due to autonomy while maintaining acceptable risk and serving the needs of our customers. We would like to present our lessons learned so far in adopting autonomy and automation, which we think will contribute to clarifying the challenges facing the use of such technology. SOMO provides services to a diverse and ambitious set of mission customers. Many of these missions are groundbreaking missions for which communications, data, and other operations requirements sometimes cannot be clearly articulated early in the program. This motivates a need for systems that are robust in the face of unanticipated situations so that customer missions are not unreasonably constrained or impacted by "shortcomings" in SOMO services. One of SOMO's primary goals is to realize a paradigm in which SOMO acts as a service provider to organizations that fly space missions for NASA, other government agencies, and even the commercial sector. These organizations purchase SOMO services "by the pound" as customers. We have to provide systems that are not experiments themselves, but rather stable bases from which to do bold experiments. To this end, SOMO also seeks to work closely with industry to see that robust autonomy technology gets infused into products and services for the space industry and beyond. The potential for application of these technologies spans space-based communications networks (e.g. TDRSS) and ground-based assets including communication and tracking antenna systems, data networks, and control centers. There are several problems that are candidates for the application of autonomy, if it can be made reliable enough, including: antenna control, antenna scheduling, communication link scheduling and operation, navigation, attitude determination, fault detection, isolation, and reconfiguration (for spacecraft or ground assets), and mission-level planning and scheduling. Some attempts have been made to apply autonomy and automation in these areas in the past with varying degrees of success. We will present relevant case histories and the lessons inferred from them. Combining this past experience with anticipated future needs, we can clarify the challenges that must be met in order to realize the benefits of autonomy.
In 2020, NASA issued a Request for Information (RFI) from potential sources for Suborbital Crew (SubC) space transportation services. NASA recognizes the advancement of the commercial suborbital space flight industry and is currently working to develop suborbital transportation for Government personnel that could fly on commercially available suborbital vehicles and determine a systems qualification approach for flight risk acceptance. Prior examples of risk assessment such as the purchase of Soyuz missions serve as a foundation for how the US Government’s best technical experts can partner with a successfully flying industry provider to understand risks. More recently, NASA, the FAA, and industry have successfully demonstrated that a company-owned operation with government oversight can successfully launch Government Astronauts safely. This paper explores the challenges and opportunities presented with the use of suborbital vehicles by Government personnel and how industry might leverage the Government’s efforts to maximize the success of the suborbital space industry.
A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty (e.g. uncertainty in a radiation quality parameter) as well as model-form uncertainty associated with differing theoretical or empirical formalisms (e.g. combined dose-rate and radiation quality effects). Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future (including international) models, improve uncertainty quantification for underlying sub-models developed against sparse experimental data, and reduce the impact of subjective bias on risk projections. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met or the level of knowledge doesn’t permit a specific risk or exposure limit to be developed for future space exploration missions. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency as ensemble members are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible exposure limits and permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets; closer agreement is seen at the median + one sigma due to the inherent similarities in available models. Future work, including the addition of new models and methods for statistical correlation between predictive members are discussed to define alternate ways of thinking about risk and ‘acceptable’ uncertainty with respect to NASA’s current permissible exposure limits.
A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty (e.g. uncertainty in a radiation quality parameter) as well as model-form uncertainty associated with differing theoretical or empirical formalisms (e.g. combined dose-rate and radiation quality effects). Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future (including international) models, improve uncertainty quantification for underlying sub-models developed against sparse experimental data, and reduce the impact of subjective bias on risk projections. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met or the level of knowledge doesn’t permit a specific risk or exposure limit to be developed for future space exploration missions. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency as ensemble members are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible exposure limits and permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets; closer agreement is seen at the median + one sigma due to the inherent similarities in available models. Future work, including the addition of new models and methods for statistical correlation between predictive members are discussed to define alternate ways of thinking about risk and ‘acceptable’ uncertainty with respect to NASA’s current permissible exposure limits.
In 2020, NASA issued a Request for Information (RFI) from potential sources for Suborbital Crew (SubC) Space Transportation Services. NASA recognizes the advancement of the commercial suborbital spaceflight industry and is currently working to develop suborbital transportation for Government personnel, that could fly on commercially available suborbital vehicles and determine a systems qualification approach for flight risk acceptance.
During the launch of a rocket under prevailing weather conditions, commanders at Cape Canaveral Air Force station evaluate the possibility of whether wind blown toxic emissions might reach civilian and military personnel in the near by area. In our model, we focused mainly on Hydrogen chloride (HCL), Nitrogen oxides (NOx) and Nitric acid (HNO3), which are non-carcinogenic chemicals as per United States Environmental Protection Agency (USEPA) classification. We have used the hazard quotient model to estimate the number of people at risk. It is based on the number of people with exposure above a reference exposure level that is unlikely to cause adverse health effects. The risk to the exposed population is calculated by multiplying the individual risk and the number in exposed population. The risk values are compared against the acceptable risk values and GO or NO-go situation is decided based on risk values for the Shuttle launch. The entire model is simulated over the web and different scenaria can be generated which allows management to choose an optimum decision.