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All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Space Transportation System Availability Relationships to Life Cycle Cost

Future space transportation architectures and designs must be affordable. Consequently, their Life Cycle Cost (LCC) must be controlled. For the LCC to be controlled, it is necessary to identify all the requirements and elements of the architecture at the beginning of the concept phase. Controlling LCC requires the establishment of the major operational cost drivers. Two of these major cost drivers are reliability and maintainability, in other words, the system's availability (responsiveness). Potential reasons that may drive the inherent availability requirement are the need to control the number of unique parts and the spare parts required to support the transportation system's operation. For more typical space transportation systems used to place satellites in space, the productivity of the system will drive the launch cost. This system productivity is the resultant output of the system availability. Availability is equal to the mean uptime divided by the sum of the mean uptime plus the mean downtime. Since many operational factors cannot be projected early in the definition phase, the focus will be on inherent availability which is equal to the mean time between a failure (MTBF) divided by the MTBF plus the mean time to repair (MTTR) the system. The MTBF is a function of reliability or the expected frequency of failures. When the system experiences failures the result is added operational flow time, parts consumption, and increased labor with an impact to responsiveness resulting in increased LCC. The other function of availability is the MTTR, or maintainability. In other words, how accessible is the failed hardware that requires replacement and what operational functions are required before and after change-out to make the system operable. This paper will describe how the MTTR can be equated to additional labor, additional operational flow time, and additional structural access capability, all of which drive up the LCC. A methodology will be presented that provides the decision makers with the understanding necessary to place constraints on the design definition. This methodology for the major drivers will determine the inherent availability, safety, reliability, maintainability, and the life cycle cost of the fielded system. This methodology will focus on the achievement of an affordable, responsive space transportation system. It is the intent of this paper to not only provide the visibility of the relationships of these major attribute drivers (variables) to each other and the resultant system inherent availability, but also to provide the capability to bound the variables, thus providing the insight required to control the system's engineering solution. An example of this visibility is the need to provide integration of similar discipline functions to allow control of the total parts count of the space transportation system. Also, selecting a reliability requirement will place a constraint on parts count to achieve a given inherent availability requirement, or require accepting a larger parts count with the resulting higher individual part reliability requirements. This paper will provide an understanding of the relationship of mean repair time (mean downtime) to maintainability (accessibility for repair), and both mean time between failure (reliability of hardware) and the system inherent availability.

Rhodes, Russel E.↗
NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repairs Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, it will be necessary to understand how crew repair and maintenance timelines are impacted by mission operations and technology changes. Past work has been done using historical ISS data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available data sets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat, and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA International Space Station (ISS) maintenance and crew time data are logged into two central databases, the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS) respectively. Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. MDC provides a detailed catalog of failure events and an overview of the failure’s required maintenance and OPTimIS provides a description of crew activities and crew time durations dedicated to maintenance. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair times for each failure event. Data is also classified by the outcome of each repair event, whether the failed component was replaced or whether it was repaired in place. The entire maintenance activity dataset is then categorized based on the class of failed component to allow for a statistically significant sample size for each class and to provide accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time To Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗
NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repair Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, understanding how crew repair and maintenance timelines are impacted by mission operations and technology changes is vital for future mission planning. Past work has been done using historical International Space Station (ISS) data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available datasets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat (SH), and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA ISS maintenance and crew time data are logged into two central databases: the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS). Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair time for each failure and repair event. The entire maintenance activity dataset is then categorized based on the class of failed component to ensure a significant sample size for each class and accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time to Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. The authors applied these modeling methods to a case study of a crewed mission to the planned SH and produced expected corrective maintenance crew time distributions. The results produced an expected corrective maintenance crew time at over 24 hours per mission, and a maintenance crew time distribution that reflects the importance of planning for sufficient maintenance requirements each mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗
NASA NTRS1993

Comparative analysis of different configurations of PLC-based safety systems from reliability point of view

The study of a comparative analysis of distinct multiplex and fault-tolerant configurations for a PLC-based safety system from a reliability point of view is presented. It considers simplex, duplex and fault-tolerant triple redundancy configurations. The standby unit in case of a duplex configuration has a failure rate which is k times the failure rate of the standby unit, the value of k varying from 0 to 1. For distinct values of MTTR and MTTF of the main unit, MTBF and availability for these configurations are calculated. The effect of duplexing only the PLC module or only the sensors and the actuators module, on the MTBF of the configuration, is also presented. The results are summarized and merits and demerits of various configurations under distinct environments are discussed.

Tapia, Moiez A.↗
NASA NTRSDate not supplied

Design for Maintenance

Design influence is accomplished through inclusion of Reliability, Maintainability, and Supportability (RMS) as part of the system’s design characteristics from program/project inception to ensure the system(s) can be supported and maintained throughout the life cycle.

Maintainability MTTR Maintenance Integrated Logist↗