Proof Test Data Research Articles

Development of machine learning based classifier for the pressure test result prediction of type IV composite overwrapped pressure vessels

The stringent safety regulations of type IV composite overwrapped pressure vessels (COPVs) for commercial vehicles mandate a certification process involving pressurization up to 1050 bar, with the critical requirement of withstanding burst pressures of 1570 bar. Analyzing proof test data is crucial to enhance and ensure tank safety regarding burst pressure. In this study, we developed various machine learning classifiers for structure health monitoring and damage prediction of COPVs. The classifiers were trained using a substantial amount of acoustic emission data collected during burst and pressure cycling tests. The test results were employed as label inputs during the training process. Statistical features were extracted per time unit and trained using Naive Bayes, Logistic Regression, Decision Tree, XGBoost, and TabNet models. Upon training the data collected from the burst pressure test, TabNet, Decision Tree, and XGBoost achieved classification accuracies above 0.94. Notably, TabNet demonstrated also the best performance for the pressure cycling test with an accuracy of 0.98. Furthermore, TabNet provided visualizations of feature sensitivity in relation to classification results. This study marks the first development of a machine learning classifier for predicting the damage state of COPV tanks in commercial applications pertaining to required safety tests.

Design bond stress parameters for rock anchors in Brisbane

Little published information is available on bond stress parameters at the grout-ground interface for the design of ground anchors within Brisbane rocks. In the absence of data, a designer will typically fall back to ‘universal’ correlations with measurable parameters such as Uniaxial Compressive Strength (UCS) or descriptions of rock type to nominate design bond stress values. In doing so, there is often little understanding of the limitations of such correlations or how applicable those correlations are for the rocks encountered within the local region. A study of Proof Test data from testing of sacrificial ground anchors constructed within materials from the Brisbane Tuff and Neranleigh Fernvale Beds Stratigraphic Units for an infrastructure project in Brisbane has been carried out to consider bond stress values at the grout-ground interface. Materials within the bond zone of ground anchors constructed in Brisbane Tuff and Neranleigh Fernvale Beds units have been classified into different rock units based on rock substance strength and Geological Strength Index. Details of anchor construction and testing procedures are presented, together with the adopted approach to test interpretation. Data from Proof Testing of ground anchors bonded into these materials is then interpreted and evaluated for each unit, with relationships developed for each rock type for ultimate and yield bond stress values at the grout-ground interface as a function of rock substance strength (UCS) and rock mass strength (based on Hoek and Brown,2018). For both rock types, grout-ground interface bond stresses increase with rock strength and quality, with better correlations evident based on rock mass strength than for UCS data. Comparisons of the interpreted bond stress relationships based on UCS are made for both rock types to published information for ground anchors and shaft adhesion parameters for cast-in-situ piles. Suggestions are made for amendments to the Proof Anchor test method to reduce the potential for premature termination of the test and consequent underestimation of the bond stress, and to obtain consistency between Proof and Production test methods.

Dynamic-risk-informed safety barrier management: An application to cost-effective barrier optimization based on data from multiple sources

An integrated approach for performance assessment and management of safety barriers in a systemic manner is needed concerning the prevention and mitigation of major accidents in chemical process industries. Particularly, the effects of safety barriers on system risk reduction should be assessed in a dynamic manner to support the decision-making on safety barrier establishments and improvements. A simulation approach, named Simulink-based Safety Barrier Modeling (SSBM), is proposed in this paper to conduct dynamic risk assessment of chemical facilities with the consideration of the degradation of safety barriers. The main functional features of the SSBM include i) the basic model structures of SSBM can be determined based on bow-tie diagrams, ii) multiple data (periodic proof test data, continuous condition-monitoring data, and accident precursor data) may be combined to update barrier failure probabilities and initiating event probabilities, iii) SSBM is able to handle uncertainty propagation in probabilistic risk assessment by using Monte Carlo simulations, and iv) cost-effectiveness analysis (CEA) and optimization algorithms are integrated to support the decision-making on safety barrier establishments and improvements. An illustrative case study is demonstrated to show the procedures of applying the SSBM on dynamic risk-informed safety barrier management and validate the feasibility of implementing the SSBM for cost-effective safety barrier optimization.

Finite Element Model Tuning Using Analytical Sensitivity Values

This paper presents an efficient approach for tuning finite element models to match the measured ground vibration test and proof test data. Frequencies, mode shapes, total weight, location of the center of gravity, and static deformation computed from the finite element model are matched to the measured data. The model tuning procedure used in this work is based on solving an optimization problem in which the errors for the considered metrics, between the finite element prediction and the measured data, are minimized. Analytical sensitivity values of performance indices are computed using the NASTRAN-generated sensitivity values together with the in-house computer programs, which allow for faster computational time and the use of gradient-based optimizers. The method is applied to the Aerostructures Test Wing 4 model. The study shows that the military standard and the NASA standard for comparing analytical and experimental modal data are all satisfied. The final finite element model correlates well with the test data. The flutter speed decreases by 8.91% after model tuning compared with the original Aerostructures Test Wing 4 design.

Investigation of Valve Failures Using Statistical Analysis of Proof Test Data

This research work examined pressure relief valve (PRV) failures by using statistical analysis of proof test data obtained from the computerized maintenance management system (CMMS) of Liquefied Natural Gas (LNG) facilities site. This study comparatively investigated the 30 months approved inspection interval for the proof test of PRVs by the Department of Petroleum Resources (DPR) of Nigeria. In this research, failure rate and failure behaviour of PRVs were evaluated and analyzed using bathtub curve. Aging effect of PRV using regression analysis and the cause of failures were also analysed. Least square multiple linear regression was the statistical tool used for the analysis with the aid of EXCEL. After thorough analysis, the study depicted drop in failure rate at different times (years) at the bathtub curve. Also, the statistical analysis found that the ageing effect from five spring loaded PRVs evaluated was at most 0.4% per year with 95% confidence and the amount of variance explained per time was minimal (R-Square 7.6%). It was recommended that the inspection interval (30 months) as approved by DPR for proof test of PRVs was optimal with regard to the required integrity condition of the PRVs since test frequencies yielded better result and consequently less risk. Also, further testing and investigation should be pursued to determine if longer intervals between inspections were possible. Furthermore, analysis from the investigation of cause of failure showed that corrosion was a cause of PRV failure and contact area factors such as sticking and cold welding

Analysis of spring operated pressure relief valve proof test data: Findings and implications

Ten years of research into spring operated pressure relief valve (SOPRV) proof test data are reviewed. Previously unpublished results are also presented. The principle findings are: proof test data alone are insufficient to properly analyze SOPRV safety behavior; root cause analysis (RCA) of all failed SOPRV is also required; probability of initial failure is a real and concerning phenomenon in both new (not previously installed) as well as used (previously installed) SOPRV but does not affect all SOPRV equally; SOPRV failure rates and safety performances depend on many variables including set pressure, SOPRV design and manufacturing specifics, and end user safety and maintenance practices; certain SOPRV require shorter proof test intervals (2–3 years) to maintain desired safety ratings but others can endure much longer proof test intervals with only small additional safety risks. Understanding all of the parameters which impact SOPRV safety performance and collecting safety performance data (including RCA of failed SOPRV) at the individual facility level are the keys to optimizing commissioning and pre‐installation proof testing and proof test intervals, and potentially to extending turnaround times. © 2018 American Institute of Chemical Engineers Process Saf Prog 37: 467–477, 2018

Investigation of Adhesion Formation in New Stainless Steel Trim Spring Operated Pressure Relief Valves

Examination of proof test data for new (not previously installed) stainless steel (SS) trim spring operated pressure relief valves (SOPRV) reveals that adhesions form between the seat and disk in about 46% of all such SOPRV. The forces needed to overcome these adhesions can be sufficiently large to cause the SOPRV to fail its proof test (FPT) prior to installation. Furthermore, a significant percentage of SOPRV which are found to FPT are also found to “fail to open” (FTO) meaning they would not relief excess pressure in the event of an overpressure event. The cases where adhesions result in FTO or FPT appear to be confined to SOPRV with diameters less than or equal to 1 in. and set pressures less than 150 pounds per square inch gauge (psig) and the FTO are estimated to occur in 0.31% to 2.00% of this subpopulation of SS trim SOPRV. The reliability and safety implications of these finding for end users who do not perform pre-installation testing of SOPRV are discussed.

Evaluating Risk and Safety Integrity Levels for Pressure Relief Valves Through Probabilistic Modeling

The probability of failure on demand (PFD) for spring-operated pressure relief valves (SORVs) is estimated by applying the Fréchet and Weibull probability distributions using proof test data from the United States Department of Energy's Savannah River Site (SRS) in Aiken, South Carolina. The data can be accessed through the Center for Chemical Process Safety (CCPS) Process Equipment Reliability Database (PERD). The probability distributions enable the evaluation of risk, estimation of ANSI/ISA-84.00.01 Safety Integrity Levels (SILs), and the impact of potential modifications of the maintenance plan. Current SRS practices are reviewed, and recommendations are made for risk-based adjustments to the maintenance plan. Subsets of valves are identified in which maintenance times can be extended and in which increased safety margins may be needed.

Accounting for Proof Test Data in a Reliability-Based Design Optimization Framework

This paper investigates the use of proof (or acceptance) test data during the reliability based design optimization of structural components. It is assumed that every component will be proof tested and that the component will only enter into service if it passes the proof test. The goal is to reduce the component weight, while maintaining high reliability, by exploiting the proof test results during the design process. The proposed procedure results in the simultaneous design of the structural component and the proof test itself and provides the designer with direct control over the probability of failing the proof test. The procedure is illustrated using two analytical example problems and the results indicate that significant weight savings are possible when exploiting the proof test results during the design process.

New Measurement Service for Determining Pressure Sensitivity of Type LS2aP Microphones by the Reciprocity Method.

A new National Institute of Standards and Technology (NIST) measurement service has been developed for determining the pressure sensitivities of American National Standards Institute and International Electrotechnical Commission type LS2aP laboratory standard microphones over the frequency range 31.5 Hz to 20 000 Hz. At most frequencies common to the new service and the old service, the values of the expanded uncertainties of the new service are one-half the corresponding values of the old service, or better. The new service uses an improved version of the system employed by NIST in the Consultative Committee for Acoustics, Ultrasound, and Vibration (CCAUV) key comparison CCAUV.A-K3. Measurements are performed using a long and a short air-filled plane-wave coupler. For each frequency in the range 31.5 Hz to 2000 Hz, the reported sensitivity level is the average of data from both couplers. For each frequency above 2000 Hz, the reported sensitivity level is determined with data from the short coupler only. For proof test data in the frequency range 31.5 Hz to 2000 Hz, the average absolute differences between data from the long and the short couplers are much smaller than the expanded uncertainties.

Analysis of pressure relief valve proof test data

AbstractThis article reports on our statistical analysis of pressure relief valve (PRV) proof test data for the failure mode, fail‐to‐open, i.e., the PRV remains closed when actual pressure reaches or exceeds 150% of set pressure. Three data sets, from two Fortune 500 operating companies, which met the intent of the quality assurance of proof test data as documented by the Center for Chemical Process Safety Process Equipment Reliability Database (CCPS PERD)1 initiative, were analyzed. Although the original intent of our analysis focused solely on estimating the failure rate during the “useful life”2 of the equipment, it became apparent that the probability of failure on initial installation or reinstallation after proof test, and the need to address what constituted end of useful life were very significant. This article provides three important findings that are summarized as follows: The statistical analysis of each data set predicted a 1–1.6% probability of initial failure where initial failure is understood to be at the time of initial installation or reinstallation after a proof test. This implies that most of the failures found during the useful life via proof test are pre‐existing failures from the time of installation or reinstallation rather than failures that occurred randomly after installation or reinstallation of the PRV. Our calculations, based on the three independent data sets, led to consistent estimates of PRV useful‐life failure rates between 10−8 and 10−7 failures/h. Additionally, we compared our estimates from data analysis to the prediction of useful‐life failure rate for a particular PRV model using the Failure Modes Effects and Diagnostics Analysis (FMEDA) method. The prediction was consistent with the data estimates. The data further indicated that the low useful‐life failure rate was not supported beyond a 4–5 year proof test interval as the threshold of wear‐out seemed to be approached. The importance of these finding cannot be overestimated. When taking credit for a PRV in a risk assessment and in calculating the probability of failure on demand, both the initial probability of failing to open, as well as the probability of PRV failure due to the useful‐life failure rate, must be taken into account (the article discusses how to do this). Even then, the results are only defensible when it can be demonstrated that the proof test occurs before wear‐out (i.e., end of useful life) begins. © 2009 American Institute of Chemical Engineers Process Saf Prog, 2009

Examination of Proof Test Extrapolation for Drilled Shafts

Abstract The difference between a load test and a proof test is that in a load test, the foundation is loaded to failure, whereas in a proof test, it is usually tested to the design load times the desired margin of safety, and failure may not necessarily be reached. Paikowsky and Tolosko (1999) presented and examined methodologies for obtaining the ultimate capacity from proof test data for driven piles. Load test data on drilled shafts supporting the H-3 freeway viaduct on the island of Oahu in Hawaii are used to test six extrapolation techniques by incrementally truncating the load versus settlement data and then comparing the predicted with measured capacities. With some limitations, several of these methods can be used to provide very reliable estimates of capacity of drilled shafts from proof tests, verifying the recommendations of Paikowsky and Tolosko. The more reliable methods and their limitations are identified and ways of maximizing the accuracy are suggested. With increased confidence in the use of extrapolation techniques, substantial savings can be realized in the construction industry and the engineering community. Also, in deriving the top-down load-settlement curves for the shafts tested with the Osterberg load cell, a simple method is proposed to account for the elastic compression of the shaft.

Alternative Prooftester for High‐Strength Optical Fibers

Reproducible, nondestructive methods of prooftesting continuous, multikllometer lengths of high‐strength (>1.4 GPa) optical fiber are important in high‐performance applications of lightguide technology. An alternative prooftester based on the concept of continuously imparting a constant strain rather than a constant weight to the optical fiber has been built and tested. The constant‐strain design is somewhat simpler and has certain advantages over a widely used prooftester. The constant‐strain method is explained, and prooftest data that substantiate the advantages of the design are presented.