In-flight detection of non-catastrophic equipment failures in a space vehicle guidance- and-navigation system.
Noncatastrophic guidance and navigation equipment failures detected in flight, using two separate statistical tests
ORBITAL RENDEZVOUS↗
SEARCH · Engineering Papers
Results for “Equipment Failure”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Equipment Failure Analysis
Tennessee Eastman uses NASTRAN to minimize lost production by pinpointing the causes of equipment failures and preventing recurrences. An example of the program's utility involves a large centrifugal fan which developed cracks during start-ups and shutdowns. This information prompted redesign of the fan. Tennessee Eastman has made extensive use of NASTRAN, both as failure analysis tool and as an aid in redesigning production hardware.
Source record↗
Detecting Process Equipment Failures Using Acoustic Data and Machine Learning
Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.
46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
Determining Mission Effects of Equipment Failures
NASA plans call for long duration deep space missions with human crews. Because of light-time delay and other considerations, increased autonomy is needed. Crews on next-generation missions will likely be small, perhaps with as few as four members. A small crew is not likely to possess the full range of expertise needed to deal with unexpected failures and anomalies. Applied artificial intelligence technologies have developed decision support tools with the potential to fill the gap, but these tools need to be integrated to provide a smooth operational capability. In this paper we describe such an integration involving anomaly detection, diagnosis, system effect propagation, and plan repair.
Morris, Paul↗
EDP outputs of Surveyor failure reporting system - Ground equipment.
Electronic data processing system for Surveyor ground equipment failure reporting system
Daush, A. A., Jr.↗
Underground Coal Mining
Computer program models coal-mining production, equipment failure and equipment repair. Underground mine is represented as collection of work stations requiring service by production and repair crews alternately. Model projects equipment availability and productivity, and indicates proper balance of labor and equipment. Program is in FORTRAN IV for batch execution; it has been implemented on UNIVAC 1108.
Hill, G. M.↗
Plant growth during the Greenhouse II experiment on the Mir orbital station
We carried out three experiments with Super Dwarf wheat in the Bulgarian/Russian growth chamber Svet (0.1 m2 growing area) on the Space Station Mir. This paper mostly describes the first of these NASA-supported trials, began on Aug. 13, 1995. Plants were sampled five times and harvested on Nov. 9 after 90 days. Equipment failures led to low irradiance (3, then 4 of 6 lamp sets failed), instances of high temperatures (ca. 37 degrees C), and sometimes excessive substrate moisture. Although plants grew for the 90 d, no wheat heads were produced. Considering the low light levels, plants were surprisingly green, but of course biomass production was low. Plants were highly disoriented (low light, mirror walls?). Fixed and dried samples and the root module were returned on the U.S. Shuttle Atlantis on Nov. 20, 1995. Samples of the substrate, a nutrient-charged zeolite called Balkanine, were taken from the root module, carefully examined for roots, weighed, dried, and reweighed. The Svet control unit and the light bank were shipped to Moscow. An experiment validation test (EVT) of plant growth and experimental procedures, carried out in Moscow, was highly successful. Equipment built in Utah to measure CO2, H2O vapor, irradiance, air and leaf (IR) temperature, O2, pressure, and substrate moisture worked well in the EVT and in space. After this manuscript was first prepared, plants were grown in Mir with a new light bank and controller for 123 d in late 1996 and 39 days in 1996/1997. Plants grew exceptionally well with higher biomass production than in any previous space experiment, but the ca. 280 wheat heads that were produced in 1996 contained no seeds. Ethylene in the cabin atmosphere was responsible. c2002 Published by Elsevier Science Ltd on behalf of COSPAR.
Mir Project↗
Development of KSC program for investigating and generating field failure rates. Reliability handbook for ground support equipment
Field failure rates and confidence factors are presented for 88 identifiable components of the ground support equipment at the John F. Kennedy Space Center. For most of these, supplementary information regarding failure mode and cause is tabulated. Complete reliability assessments are included for three systems, eight subsystems, and nine generic piece-part classifications. Procedures for updating or augmenting the reliability results are also included.
Bloomquist, C. E.↗
Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation
The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.
15 GEOTHERMAL ENERGY↗
International Space Station (ISS) Payload Autonomous Operations Past, Present and Future
Draper Laboratorys Timeliner is a scripting and automation system that runs onboard computers in the International Space Station (ISS). Timeliner is fully integrated with ISS and can be used to automate ISS operations tasks. Some of the most challenging aspects of operating a payload in low earth orbit are communication delays, ground equipment failures, and human errors. How does a Payload Developer (PD) know their equipment is operating nominally and collecting science in the most efficient way possible or even powered at any given time? During a ground Loss of Signal (LOS) data outage, PDs have no insight into their experiments state, and benefit greatly from Timeliner scripts executing on ISS to perform telemetry monitoring and commanding operations. This paper will discuss current software designs, and new operational uses for Timeliner. Existing Timeliner capabilities discussed will include: autonomous EXPRESS Rack activation and deactivation; autonomous science data downlinks; Minus Eighty Degree Freezer (MELFI) Dewar autonomous safing; JAXA and ESA module autonomous payload facility safing; as well as many others. New operational concepts discussed will include allowing Timeliner on the payload computer to issue core commands (Thermal, Power, Fire Detection), creation of new ground tools that will monitor the current status of Payload Racks as well as all the messaging from autonomous scripts executing, decreasing approval time for Timeliner bundles, and opening up the Payload MDM Enhanced Processor Integrated Communications Card (EPIC) interface. The EPIC interface could provide a new crew interface for PL Timeliner execution. The new interface could operate on either a Payload Computer System (PCS) or a Station Support Computer (SSC) that is plugged into either the Payload LAN or the Operations LAN which will make communicating to the PL MDM more flexible and greatly increase band width for communication.
Space Mission Automation↗
A system for recording failures
Test equipment for recording failures of contacts to open or close
FAILURE MODE↗
Estimation of economic losses
If the concentration of carbon fibers (CF) is high after their dispersion due to an aircraft fire, there is a significant possibility that a number of residential, commercial, and industrial establishments might be affected by electronic equipment failure. Estimating economic losses from CF release involves characterizing an entire spectrum of buildings and electronic equipment within a given community. A number of simplified assumptions were made to reduce the data collection requirements to manageable proportions. A limited number of facility categories were identified, and assumed to be relatively homogeneous. The detailed examination of potential losses on an industry-by-industry basis is an important area for future investigation.
Source record↗
Reliability analysis of the F-8 digital fly-by-wire system
The F-8 Digital Fly-by-Wire (DFBW) flight test program intended to provide the technology for advanced control systems, giving aircraft enhanced performance and operational capability is addressed. A detailed analysis of the experimental system was performed to estimated the probabilities of two significant safety critical events: (1) loss of primary flight control function, causing reversion to the analog bypass system; and (2) loss of the aircraft due to failure of the electronic flight control system. The analysis covers appraisal of risks due to random equipment failure, generic faults in design of the system or its software, and induced failure due to external events. A unique diagrammatic technique was developed which details the combinatorial reliability equations for the entire system, promotes understanding of system failure characteristics, and identifies the most likely failure modes. The technique provides a systematic method of applying basic probability equations and is augmented by a computer program written in a modular fashion that duplicates the structure of these equations.
Brock, L. D.↗
Statistical and Machine Learning Approaches to Analyzing Pipeline Incidents in the United States (2010–2024)
This study applies machine learning methods to analyze natural gas pipeline incidents in the United States using the Pipeline and Hazardous Materials Safety Administration (PHMSA) Gas Distribution Incident Dataset (2010–2024). The dataset includes over 600 variables describing incident characteristics, infrastructure attributes, and contributing factors associated with unintentional gas releases. The objective is to assess whether these features can reliably predict the underlying cause of pipeline failures. Multinomial logistic regression and Random Forest models were developed to classify incident causes, including excavation damage, corrosion, equipment failure, and natural forces. Results show that excavation damage is both the most frequent and most predictable cause, with models achieving strong performance for this category. However, when excavation damage is excluded, model accuracy declines significantly, with some models performing near random levels. Across all approaches, severe class imbalance and limited variability in key predictors constrain predictive performance. Pipeline age and diameter emerge as the most influential variables, but they provide insufficient discriminatory power to distinguish among less frequent failure types. These findings indicate that non-excavation-related incidents are rare, heterogeneous, and weakly represented in the dataset, limiting the effectiveness of machine learning classification. Overall, this study highlights the structural limitations of the PHMSA dataset for predictive modeling and underscores the need for improved data balance and feature enrichment. The results reinforce excavation damage prevention as the most impactful strategy for reducing pipeline incidents.
03 NATURAL GAS↗
Deterioration of ZnO/SiO2 diode packages in high humidity
A case study is reported in which the ZnO/SiO2 glass used to package a power rectifier combined with the design to produce a catastropic corrosion failure of the system. Metallic Zn inclusions, present in the glass, played a critical role in creating a conductive path for corrosion currents. Actual equipment failure was the result of an open circuit trace created by corrosion. It is concluded that the presence of Zn inclusions in the glass of this type of package may result in long-term reliability problems for equipment used in high humidity environments.
Evans, John↗
Informing Plant Asset Reliability and Availability Through AI-Driven Analysis of Operator Logs
The availability and reliability of nuclear power plant (NPP) structures, systems, and components (SSCs) are critical parameters for NPP safety. Tracking these parameters is necessary but costly and labor-intensive, requiring the collection and evaluation of SSC event data such as shutdowns, startups, and failures. To show how these events are needed for the parameters an example is given: one measure of reliability is based on the number of equipment failure events and the number of run hours (i.e., the time from a startup event to a shutdown event). Here, this work investigates using artificial intelligence (AI) to mine NPP operator log entry texts for SSC event data. Four AI approaches were explored for identifying these events, including natural language processing (NLP) methods, generative AI, generative AI combined with NLP, and topic modeling. A key challenge addressed with all four approaches is the brevity of operator log entries. Among these four a neural network–based NLP method was shown to be the most promising for this application, achieving F1 scores of 86.0% for shutdowns, 92.2% for startups, and 80.4% for failures on a subject-matter-expert-curated dataset from NPP operator logs, compared to a baseline of 66.6% for a random classifier. This shows that NLP methods can perform better than generative AI. Additionally, the NLP methods combined with generative AI were shown to perform better than generative AI alone. Generative AI was most successful at providing the background information for the NLP methods to use. This work demonstrates the potential to use AI to automate parameter collection from NPP operator log entries and other records.
97 - MATHEMATICS AND COMPUTING↗
Checklists and Monitoring in the Cockpit: Why Crucial Defenses Sometimes Fail
Checklists and monitoring are two essential defenses against equipment failures and pilot errors. Problems with checklist use and pilots failures to monitor adequately have a long history in aviation accidents. This study was conducted to explore why checklists and monitoring sometimes fail to catch errors and equipment malfunctions as intended. Flight crew procedures were observed from the cockpit jumpseat during normal airline operations in order to: 1) collect data on monitoring and checklist use in cockpit operations in typical flight conditions; 2) provide a plausible cognitive account of why deviations from formal checklist and monitoring procedures sometimes occur; 3) lay a foundation for identifying ways to reduce vulnerability to inadvertent checklist and monitoring errors; 4) compare checklist and monitoring execution in normal flights with performance issues uncovered in accident investigations; and 5) suggest ways to improve the effectiveness of checklists and monitoring. Cognitive explanations for deviations from prescribed procedures are provided, along with suggestions for countermeasures for vulnerability to error.
Dismukes, R. Key↗
Design of the Brine Evaporation Bag for Increased Water Recovery in Microgravity
The existing water recovery system on the International Space Station (ISS) is limited to 75% reclamation; consequently, long duration space missions are currently unfeasible due to the large quantity of water necessary to sustain the crew. The Brine Evaporation Bag (BEB) is a proposed system to supplement the existing water recovery system aboard the ISS that can to increase water recovery to 99%. The largest barrier to high water recovery is mineral scaling inside the water recovery equipment, which leads to equipment failure; therefore, some water must remain to keep the minerals dissolved. This waste stream is liquid brine containing salts, acids, organics, and water. The BEB is designed to recover this remaining water while protecting the equipment from scale. The BEB consists of a sealed bag containing a hydrophobic membrane that allows water vapor and gas to pass through. It is operated under vacuum, heated, and continuously filled with brine to boil away the water. The water vapor is recovered and the solids are contained inside the bag for disposal. The BEB can dry the brine to a solid block. Ongoing work includes improving the design of the BEB and the evaporator to prevent leaks, maximize the rate of water removal, and minimize energy use and weight. Additional testing will determine whether designs are heat- or mass-transfer limited and the optimal water recovery rate.
Brine Evaporation Bag↗