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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

PV degradation curves: non‐linearities and failure modes

Abstract Photovoltaic (PV) reliability and durability have seen increased interest in recent years. Historically, and as a preliminarily reasonable approximation, linear degradation rates have been used to quantify long‐term module and system performance. The underlying assumption of linearity can be violated at the beginning of the life, as has been well documented, especially for thin‐film technology. Additionally, non‐linearities in the wear‐out phase can have significant economic impact and appear to be linked to different failure modes. In addition, associating specific degradation and failure modes with specific time series behavior will aid in duplicating these degradation modes in accelerated tests and, eventually, in service life prediction. In this paper, we discuss different degradation modes and how some of these may cause approximately linear degradation within the measurement uncertainty (e.g., modules that were mainly affected by encapsulant discoloration) while other degradation modes lead to distinctly non‐linear degradation (e.g., hot spots caused by cracked cells or solder bond failures and corrosion). The various behaviors are summarized with the goal of aiding in predictions of what may be seen in other systems. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Jordan, Dirk C.↗
DOE OSTI2015

Predicting the Occurrence of Mixed Mode Failure Associated With Hydraulic Fracturing, Part 2 Water Saturated Tests

Seven water-saturated triaxial extension experiments were conducted on four sedimentary rocks. This experimental condition was hypothesized more representative of that existing for downhole hydrofracture and thus it may improve our understanding of the phenomena. In all tests the pore pressure was 10 MPa and confirming pressure was adjusted to achieve tensile and transitional failure mode conditions. Using previous work in this LDRD for comparison, the law of effective stress is demonstrated in extension using this sample geometry. In three of the four lithologies, no apparent chemo-mechanical effect of water is apparent, and in the fourth lithology test results indicate some chemo-mechanical effect of water.

58 GEOSCIENCES↗
DOE OSTI2021

Characterizing possible failure modes in physics-informed neural networks (characterizing-pinns-failure-modes) v0.1.0

This software provides an implementation of the physics-informed neural network setup for different differential equations. It provides a way to solve differential equations using machine learning, via a physical constraint term in the loss function. Physics-informed neural networks are a popular approach, but here, the failures of such an approach are characterized and better solutions and training procedures are provided.

Krishnapriyan, Aditi↗
DOE OSTI2016

Minimizing treatment planning errors in proton therapy using failure mode and effects analysis

Purpose: Failure mode and effects analysis (FMEA) is a widely used tool to evaluate safety or reliability in conventional photon radiation therapy. However, reports about FMEA application in proton therapy are scarce. The purpose of this study is to apply FMEA in safety improvement of proton treatment planning at their center. Methods: The authors performed an FMEA analysis of their proton therapy treatment planning process using uniform scanning proton beams. The authors identified possible failure modes in various planning processes, including image fusion, contouring, beam arrangement, dose calculation, plan export, documents, billing, and so on. For each error, the authors estimated the frequency of occurrence, the likelihood of being undetected, and the severity of the error if it went undetected and calculated the risk priority number (RPN). The FMEA results were used to design their quality management program. In addition, the authors created a database to track the identified dosimetric errors. Periodically, the authors reevaluated the risk of errors by reviewing the internal error database and improved their quality assurance program as needed. Results: In total, the authors identified over 36 possible treatment planning related failure modes and estimated the associated occurrence, detectability, and severity to calculate the overall risk priority number. Based on the FMEA, the authors implemented various safety improvement procedures into their practice, such as education, peer review, and automatic check tools. The ongoing error tracking database provided realistic data on the frequency of occurrence with which to reevaluate the RPNs for various failure modes. Conclusions: The FMEA technique provides a systematic method for identifying and evaluating potential errors in proton treatment planning before they result in an error in patient dose delivery. The application of FMEA framework and the implementation of an ongoing error tracking system at their clinic have proven to be useful in error reduction in proton treatment planning, thus improving the effectiveness and safety of proton therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

A streamlined failure mode and effects analysis

Purpose: Explore the feasibility and impact of a streamlined failure mode and effects analysis (FMEA) using a structured process that is designed to minimize staff effort. Methods: FMEA for the external beam process was conducted at an affiliate radiation oncology center that treats approximately 60 patients per day. A structured FMEA process was developed which included clearly defined roles and goals for each phase. A core group of seven people was identified and a facilitator was chosen to lead the effort. Failure modes were identified and scored according to the FMEA formalism. A risk priority number,RPN, was calculated and used to rank failure modes. Failure modes with RPN > 150 received safety improvement interventions. Staff effort was carefully tracked throughout the project. Results: Fifty-two failure modes were identified, 22 collected during meetings, and 30 from take-home worksheets. The four top-ranked failure modes were: delay in film check, missing pacemaker protocol/consent, critical structures not contoured, and pregnant patient simulated without the team's knowledge of the pregnancy. These four failure modes hadRPN > 150 and received safety interventions. The FMEA was completed in one month in four 1-h meetings. A total of 55 staff hours were required and, additionally, 20 h by the facilitator. Conclusions: Streamlined FMEA provides a means of accomplishing a relatively large-scale analysis with modest effort. One potential value of FMEA is that it potentially provides a means of measuring the impact of quality improvement efforts through a reduction in risk scores. Future study of this possibility is needed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

WE-G-BRA-08: Failure Modes and Effects Analysis (FMEA) for Gamma Knife Radiosurgery

Purpose: To perform a failure modes and effects analysis (FMEA) study for Gamma Knife (GK) radiosurgery processes at our institution based on our experience with the treatment of more than 13,000 patients. Methods: A team consisting of medical physicists, nurses, radiation oncologists, neurosurgeons at the University of Pittsburgh Medical Center and an external physicist expert was formed for the FMEA study. A process tree and a failure mode table were created for the GK procedures using the Leksell GK Perfexion and 4C units. Three scores for the probability of occurrence (O), the severity (S), and the probability of no detection (D) for failure modes were assigned to each failure mode by each professional on a scale from 1 to 10. The risk priority number (RPN) for each failure mode was then calculated (RPN = OxSxD) as the average scores from all data sets collected. Results: The established process tree for GK radiosurgery consists of 10 sub-processes and 53 steps, including a sub-process for frame placement and 11 steps that are directly related to the frame-based nature of the GK radiosurgery. Out of the 86 failure modes identified, 40 failure modes are GK specific, caused by the potential for inappropriate use of the radiosurgery head frame, the imaging fiducial boxes, the GK helmets and plugs, and the GammaPlan treatment planning system. The other 46 failure modes are associated with the registration, imaging, image transfer, contouring processes that are common for all radiation therapy techniques. The failure modes with the highest hazard scores are related to imperfect frame adaptor attachment, bad fiducial box assembly, overlooked target areas, inaccurate previous treatment information and excessive patient movement during MRI scan. Conclusion: The implementation of the FMEA approach for Gamma Knife radiosurgery enabled deeper understanding of the overall process among all professionals involved in the care of the patient and helped identify potential weaknesses in the overall process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Review of Failure Modes Applicable to Prestressed Concrete Containments

Prestressed concrete containment structures are subject to performance loss primarily due to aging and degradation. Many nuclear power plants (NPPs) have extended operating licenses beyond the design life of 40 years and some are considering operation for up to 80 years. The focus of this review is to determine which modes of performance loss, or 'failure modes', are most applicable to prestressed concrete containment vessels (PCCVs) beyond the age of 40 years. A list of failure modes taken from Crystal River Nuclear Plant Special Inspection Report is analyzed for applicability to aging nuclear containment structures. Each failure mode is described and discussed in detail. A table is provided to highlight the severity of each failure mode. This page left blank

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

SU-F-T-246: Evaluation of Healthcare Failure Mode And Effect Analysis For Risk Assessment

Purpose: To evaluate the differences between the Veteran Affairs Healthcare Failure Modes and Effect Analysis (HFMEA) and the AAPM Task Group 100 Failure and Effect Analysis (FMEA) risk assessment techniques in the setting of a stereotactic radiosurgery (SRS) procedure were compared respectively. Understanding the differences in the techniques methodologies and outcomes will provide further insight into the applicability and utility of risk assessments exercises in radiation therapy. Methods: HFMEA risk assessment analysis was performed on a stereotactic radiosurgery procedure. A previous study from our institution completed a FMEA of our SRS procedure and the process map generated from this work was used for the HFMEA. The process of performing the HFMEA scoring was analyzed, and the results from both analyses were compared. Results: The key differences between the two risk assessments are the scoring criteria for failure modes and identifying critical failure modes for potential hazards. The general consensus among the team performing the analyses was that scoring for the HFMEA was simpler and more intuitive then the FMEA. The FMEA identified 25 critical failure modes while the HFMEA identified 39. Seven of the FMEA critical failure modes were not identified by the HFMEA and 21 of the HFMEA critical failure modes were not identified by the FMEA. HFMEA as described by the Veteran Affairs provides guidelines on which failure modes to address first. Conclusion: HFMEA is a more efficient model for identifying gross risks in a process than FMEA. Clinics with minimal staff, time and resources can benefit from this type of risk assessment to eliminate or mitigate high risk hazards with nominal effort. FMEA can provide more in depth details but at the cost of elevated effort.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-421: Failure Mode and Effects Analysis (FMEA) of Xoft Electronic Brachytherapy for the Treatment of Superficial Skin Cancers

Purpose: To perform a failure mode and effects analysis (FMEA) of the process for treating superficial skin cancers with the Xoft Axxent electronic brachytherapy (eBx) system, given the recent introduction of expanded quality control (QC) initiatives at our institution. Methods: A process map was developed listing all steps in superficial treatments with Xoft eBx, from the initial patient consult to the completion of the treatment course. The process map guided the FMEA to identify the failure modes for each step in the treatment workflow and assign Risk Priority Numbers (RPN), calculated as the product of the failure mode’s probability of occurrence (O), severity (S) and lack of detectability (D). FMEA was done with and without the inclusion of recent QC initiatives such as increased staffing, physics oversight, standardized source calibration, treatment planning and documentation. The failure modes with the highest RPNs were identified and contrasted before and after introduction of the QC initiatives. Results: Based on the FMEA, the failure modes with the highest RPN were related to source calibration, treatment planning, and patient setup/treatment delivery (Fig. 1). The introduction of additional physics oversight, standardized planning and safety initiatives such as checklists and time-outs reduced the RPNs of these failure modes. High-risk failure modes that could be mitigated with improved hardware and software interlocks were identified. Conclusion: The FMEA analysis identified the steps in the treatment process presenting the highest risk. The introduction of enhanced QC initiatives mitigated the risk of some of these failure modes by decreasing their probability of occurrence and increasing their detectability. This analysis demonstrates the importance of well-designed QC policies, procedures and oversight in a Xoft eBx programme for treatment of superficial skin cancers. Unresolved high risk failure modes highlight the need for non-procedural quality initiatives such as improved planning software and more robust hardware interlock systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-627: Failure Modes and Effect Analysis for Monthly Quality Assurance of Linear Accelerator

Purpose: To develop and implement a failure mode and effect analysis (FMEA) on routine monthly Quality Assurance (QA) tests (physical tests part) of linear accelerator. Methods: A systematic failure mode and effect analysis method was performed for monthly QA procedures. A detailed process tree of monthly QA was created and potential failure modes were defined. Each failure mode may have many influencing factors. For each factor, a risk probability number (RPN) was calculated from the product of probability of occurrence (O), the severity of effect (S), and detectability of the failure (D). The RPN scores are in a range of 1 to 1000, with higher scores indicating stronger correlation to a given influencing factor of a failure mode. Five medical physicists in our institution were responsible to discuss and to define the O, S, D values. Results: 15 possible failure modes were identified and all RPN scores of all influencing factors of these 15 failue modes were from 8 to 150, and the checklist of FMEA in monthly QA was drawn. The system showed consistent and accurate response to erroneous conditions. Conclusion: The influencing factors of RPN greater than 50 were considered as highly-correlated factors of a certain out-oftolerance monthly QA test. FMEA is a fast and flexible tool to develop an implement a quality management (QM) frame work of monthly QA, which improved the QA efficiency of our QA team. The FMEA work may incorporate more quantification and monitoring fuctions in future.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

SU-F-T-247: Collision Risks in a Modern Radiation Oncology Department: An Efficient Approach to Failure Modes and Effects Analysis

Purpose: Collisions between treatment equipment and patients are potentially catastrophic. Modern technology now commonly involves automated remote motion during imaging and treatment, yet a systematic assessment to identify and mitigate collision risks has yet to be performed. Failure modes and effects analysis (FMEA) is a method of risk assessment that has been increasingly used in healthcare, yet can be resource intensive. This work presents an efficient approach to FMEA to identify collision risks and implement practical interventions within a modern radiation therapy department. Methods: Potential collisions (e.g. failure modes) were assessed for all treatment and simulation rooms by teams consisting of physicists, therapists, and radiation oncologists. Failure modes were grouped into classes according to similar characteristics. A single group meeting was held to identify implementable interventions for the highest priority classes of failure modes. Results: A total of 60 unique failure modes were identified by 6 different teams of physicists, therapists, and radiation oncologists. Failure modes were grouped into four main classes: specific patient setups, automated equipment motion, manual equipment motion, and actions in QA or service mode. Two of these classes, unusual patient setups and automated machine motion, were identified as being high priority in terms severity of consequence and addressability by interventions. The two highest risk classes consisted of 33 failure modes (55% of the total). In a single one hour group meeting, 6 interventions were identified. Those interventions addressed 100% of the high risk classes of failure modes (55% of all failure modes identified). Conclusion: A class-based approach to FMEA was developed to efficiently identify collision risks and implement interventions in a modern radiation oncology department. Failure modes and interventions will be listed, and a comparison of this approach against traditional FMEA methods will be presented.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Practical Implementation of Failure Mode and Effects Analysis for Safety and Efficiency in Stereotactic Radiosurgery

Purpose: To improve the safety and efficiency of a new stereotactic radiosurgery program with the application of failure mode and effects analysis (FMEA) performed by a multidisciplinary team of health care professionals. Methods and Materials: Representatives included physicists, therapists, dosimetrists, oncologists, and administrators. A detailed process tree was created from an initial high-level process tree to facilitate the identification of possible failure modes. Group members were asked to determine failure modes that they considered to be the highest risk before scoring failure modes. Risk priority numbers (RPNs) were determined by each group member individually and then averaged. Results: A total of 99 failure modes were identified. The 5 failure modes with an RPN above 150 were further analyzed to attempt to reduce these RPNs. Only 1 of the initial items that the group presumed to be high-risk (magnetic resonance imaging laterality reversed) was ranked in these top 5 items. New process controls were put in place to reduce the severity, occurrence, and detectability scores for all of the top 5 failure modes. Conclusions: FMEA is a valuable team activity that can assist in the creation or restructuring of a quality assurance program with the aim of improved safety, quality, and efficiency. Performing the FMEA helped group members to see how they fit into the bigger picture of the program, and it served to reduce biases and preconceived notions about which elements of the program were the riskiest.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

WE-H-BRC-03: Failure Mode and Effects Analysis in the First Clinical Implementation of a Novel Stereotactic Breast Radiotherapy Device: GammaPod™

Purpose: GammaPod™, the first stereotactic radiotherapy device for early stage breast cancer treatment, has been recently installed and commissioned at our institution. A multidisciplinary working group applied the failure mode and effects analysis (FMEA) approach to perform a risk analysis. Methods: FMEA was applied to the GammaPod™ treatment process by: 1) generating process maps for each stage of treatment; 2) identifying potential failure modes and outlining their causes and effects; 3) scoring the potential failure modes using the risk priority number (RPN) system based on the product of severity, frequency of occurrence, and detectability (ranging 1–10). An RPN of higher than 150 was set as the threshold for minimal concern of risk. For these high-risk failure modes, potential quality assurance procedures and risk control techniques have been proposed. A new set of severity, occurrence, and detectability values were re-assessed in presence of the suggested mitigation strategies. Results: In the single-day image-and-treat workflow, 19, 22, and 27 sub-processes were identified for the stages of simulation, treatment planning, and delivery processes, respectively. During the simulation stage, 38 potential failure modes were found and scored, in terms of RPN, in the range of 9-392. 34 potential failure modes were analyzed in treatment planning with a score range of 16-200. For the treatment delivery stage, 47 potential failure modes were found with an RPN score range of 16-392. The most critical failure modes consisted of breast-cup pressure loss and incorrect target localization due to patient upper-body alignment inaccuracies. The final RPN score of these failure modes based on recommended actions were assessed to be below 150. Conclusion: FMEA risk analysis technique was applied to the treatment process of GammaPod™, a new stereotactic radiotherapy technology. Application of systematic risk analysis methods is projected to lead to improved quality of GammaPod™ treatments. Ying Niu and Cedric Yu are affiliated with Xcision Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-TeP4-05: An Evaluation of a Low Dose Rate (LDR) Prostate Brachytherapy Procedure Using a Failure Modes and Effects Analysis (FMEA)

Purpose: The aim of the study is the application of a Failure Modes and Effects Analysis (FMEA) to access the risks for patients undergoing a Low Dose Rate (LDR) Prostate Brachytherapy Treatment. Methods: FMEA was applied to identify all the sub processes involved in the stages of identifying patient, source handling, treatment preparation, treatment delivery, and post treatment. These processes characterize the radiation treatment associated with LDR Prostate Brachytherapy. The potential failure modes together with their causes and effects were identified and ranked in order of their importance. Three indexes were assigned for each failure mode: the occurrence rating (O), the severity rating (S), and the detection rating (D). A ten-point scale was used to score each category, ten being the number indicating most severe, most frequent, and least detectable failure mode, respectively. The risk probability number (RPN) was calculated as a product of the three attributes: RPN = O X S x D. The analysis was carried out by a working group (WG) at UPMC. Results: The total of 56 failure modes were identified including 32 modes before the treatment, 13 modes during the treatment, and 11 modes after the treatment. In addition to the protocols already adopted in the clinical practice, the prioritized risk management will be implanted to the high risk procedures on the basis of RPN score. Conclusion: The effectiveness of the FMEA method was established. The FMEA methodology provides a structured and detailed assessment method for the risk analysis of the LDR Prostate Brachytherapy Procedure and can be applied to other radiation treatment modes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

StingRAY Failure Mode, Effects and Criticality Analysis: WEC Risk Registers

Analysis method to systematically identify all potential failure modes and their effects on the Stingray WEC system. This analysis is incorporated early in the development cycle such that the mitigation of the identified failure modes can be achieved cost effectively and efficiently. The FMECA can begin once there is enough detail to functions and failure modes of a given system, and its interfaces with other systems. The FMECA occurs coincidently with the design process and is an iterative process which allows for design changes to overcome deficiencies in the analysis.Risk Registers for major subsystems completed according to the methodology described in "Failure Mode Effects and Criticality Analysis Risk Reduction Program Plan.pdf" document below, in compliance with the DOE Risk Management Framework developed by NREL.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Failure mode and effects analysis based risk profile assessment for stereotactic radiosurgery programs at three cancer centers in Brazil

Purpose: The goal of this study was to evaluate the safety and quality management program for stereotactic radiosurgery (SRS) treatment processes at three radiotherapy centers in Brazil by using three industrial engineering tools (1) process mapping, (2) failure modes and effects analysis (FMEA), and (3) fault tree analysis. Methods: The recommendations of Task Group 100 of American Association of Physicists in Medicine were followed to apply the three tools described above to create a process tree for SRS procedure for each radiotherapy center and then FMEA was performed. Failure modes were identified for all process steps and values of risk priority number (RPN) were calculated from O, S, and D (RPN = O × S × D) values assigned by a professional team responsible for patient care. Results: The subprocess treatment planning was presented with the highest number of failure modes for all centers. The total number of failure modes were 135, 104, and 131 for centers I, II, and III, respectively. The highest RPN value for each center is as follows: center I (204), center II (372), and center III (370). Failure modes with RPN ≥ 100: center I (22), center II (115), and center III (110). Failure modes characterized by S ≥ 7, represented 68% of the failure modes for center III, 62% for center II, and 45% for center I. Failure modes with RPNs values ≥100 and S ≥ 7, D ≥ 5, and O ≥ 5 were considered as high priority in this study. Conclusions: The results of the present study show that the safety risk profiles for the same stereotactic radiotherapy process are different at three radiotherapy centers in Brazil. Although this is the same treatment process, this present study showed that the risk priority is different and it will lead to implementation of different safety interventions among the centers. Therefore, the current practice of applying universal device-centric QA is not adequate to address all possible failures in clinical processes at different radiotherapy centers. Integrated approaches to device-centric and process specific quality management program specific to each radiotherapy center are the key to a safe quality management program.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Continued Discussion of Failure Mode Modeling and Overall Component Reliability: Is the Data Missing or Censored?

This paper is a continuation of a paper presented at the 13th Probabilistic Safety Assessment and Management Conference, in which a discussion of modeling failure modes of complex components was presented; see Paulos and Smith (2016). The paper demonstrated how the typical method of treating failure modes as being exponential in nature may yield optimistic predictions when estimating how improvements to components will perform in the real world. Instead of relying on textbook methods, it is more accurate to model the failure modes as a race in time; unfortunately, this does not give a closed-form solution. This work uses a simulation to solve for the model of the world, and is compared to the standard methodology of treating the failure modes as exponential random failures. The standard method is shown to have optimistic predictions, which will lead to prediction errors when failure modes were removed or “fixed.” The failure mode methodology presented in the first paper treated the data as being censored when the test stopped. In this paper, we will compare the results from treating the data as both censored and missing, and comparisons will be shown showing which method is more accurate and under what conditions.

97 MATHEMATICS AND COMPUTING↗