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DOE OSTI2018

Advanced SOFC quality control and the role of manufacturing defects on stack reliability

Atrex Energy rolled out first commercial SOFC generator products in 2013. Following that we embarked on implementation of mass production in order to ramp up productivity and lower the costs in the meantime. To ensure the goal of mass production, an accurate, cost effective and consistent quality control methodology needs to be in place. The aim of this DOE funded project is to devise a machine and methodology for cell quality control using modern non-destructive testing methods and cell rejection criterion based on the empirical determination of the role of manufacturing flaws on stack degradation and reliability. The work scope encompasses two goals aimed at 1) developing automatable techniques for objective flaw detection and 2) developing an understanding of the ramifications of flaws on stack and cell reliability. By accomplishing these two goals it is likely that an advanced stage Quality Control (QC) device can be developed. This project scope encompasses: 1) Invention of a high speed automated instrument for spatial mapping of surface and sub-surface imperfections in production cells (ex situ) using modern imaging techniques. 2) Identification of rejection criteria upon which to screen cells from production by: a. Magnification of the effect of production defects on cell degradation through fabrication and testing of micro-cells which encompass these imperfections. b. The long term testing of full size production cells both as single cells and in small stacks, in order to correlate the (in situ) performance and degradation with the imperfection type (identified ex-situ) and hence be able to classify imperfections as actual defects. We first built a defect library with the cells containing imperfections classified and documented. The cells with various imperfections were tested to evaluate the imperfection potential impact on cell electrochemical performance. Evaluations were conducted on minicell, single cell and stack levels. On minicell level, pinhole, scratch, crack, contamination, and pop-out were investigated. We found: (1) none of the imperfections causes calamity type failure, (2) pinholes do not show measurable effect on minicell performance, (3) imperfection such as electrolyte abrasion, scratch, and pop out cause minicell potential to fluctuate by different degrees; the cells with such imperfections exhibit sensitivity of potential to high fuel utilization and “self healing” behavior. On single cell level we evaluated the effect of long crack and high population pinholes. The cell with a long crack does not show performance compromise at start. There is no potential fluctuation or sensitivity to high fuel utilization or catastrophic failure. The voltage degradation due to thermal cycle is slightly higher than normal cells. The cells with high population pinholes perform surprisingly as well as normal cells over a period of 3,500h. Even when subjected to cyclic load condition, the cells 4 show negligible degradation. Our cells appear to have “self immunization” feature which fends off the adversary effects of certain imperfections. Defects were also evaluated on stack level. Two stacks were made each with ten defect cells. The first stack was tested for 13,2000h under steady load condition while the second was tested for 9,000h under transient load conditions. We found: (1) voltage fluctuation and sensitivity to thermal cycle were not observed in contrast to minicell test, (2) of the various cracks examined, including longitudinal cracks, circumferential crack, crack near closed and open ends, none exhibited distinguishable impact on performance, (3) defect cells exhibited higher degradation rates on average under steady load condition; Individually some defect cells performed similarly or better than reference cells, (4) under steady load condition all the cells degrade monotonically with degradation rate increasing with time, (5) under cyclic (transient) load condition, both defect and reference cells exhibit complex dynamic behaviors, (6) fuel loss (leak) was measured occasionally for stack 1, the cells with crack showed the highest fuel loss increase, nevertheless, no correlation with performance degradation could be established with certainty, and (7) no catastrophic failure ever happened other than incremental degradation. Posttest inspection and microanalysis were carried out; the observations are summarized in what follows: (1) Electrolyte pinholes, some scratches and IC porosity became undetectable after testing. (2) The cathode covering the popout defects is intact without any cracking or delamination. (3) The scratch was covered by cathode in pristine state; Ni oxide or NiO-YSZ filled the space where electrolyte was missing. After >10,000h operation, the cathode that used to cover the scratch was missing but the size of the scratch remains unchanged; there is a fairly dense Ni oxide layer on top of the scratch. (4) All of the cracks remain their original dimensions. Current collector kept longitudinal cracks closed which mitigated the adversary effect of crack on cell performance/durability. (5) The crack at open end showed some deterioration manifested by a small oxidation zone in the vicinity of the crack. Wide spectrum of imaging techniques were surveyed and evaluated to detect tubular SOFC defects. Optical methods, with and without assistance of expression technique, were applied to detect all kinds of defects. Thermograph method in combination of CO2 leak, ultra-sound and electric potential was also extensively evaluated to detect surface and under-surface defects such as short circuit. Based on these works, laser camera has been selected to build the final QC machine vision inspection system. The Gocator laser profile sensor was setup to generate 2D surface intensity images of tubular SOFC surface which were analyzed with Sherlock machine vision software supplied by Teledyne Dalsa. The Gocator output was integrated with the Sherlock software and enabled live images to be imported by a Common Vision Blox driver. The Sherlock software was programmed to detect various surface defects from the surface intensity images generated by the Gocator. These measurements are subsequently used to make pass or fail decisions.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗