A Structural Integrity Assessment Methodology for Pressurized Vessels

2005 ◽  
Vol 128 (4) ◽  
pp. 541-546 ◽  
Author(s):  
Raymond K. Yee ◽  
Mike Kapper
Keyword(s):  
Power Plants ◽  
Structural Integrity ◽  
Design Tool ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Assessment Methodology ◽  
Nondestructive Examination ◽  
Integrity Assessment ◽  
Useful Life ◽  
Steam Drum

Pressurized vessels such as a steam drum in a typical power plant can often experience in-service cracking. Structural integrity assessment methodology can be a useful tool to determine the suitability of a vessel for service. This methodology may include fitness-for-service and remaining useful life analyses of a vessel based on the nondestructive examination (NDE) results and operating conditions. In this paper, the structural integrity assessment methodology applied to a steam drum case study is described. The analysis procedure, material property determination, stress analysis, limiting flaw size evaluation, and remaining useful life evaluation for the drum are discussed. A thermal shock design tool is briefly introduced. Recommendations for appropriate action are also presented. The assessment methodology employed in this paper can be applied to other similar pressurized vessels and structures in power plants.

Fitness-for-Service and Remaining Useful Life Assessment of a Steam Drum

2003 ◽  
Author(s):  
Raymond K. Yee
Keyword(s):  
Power Plant ◽  
Small Sample ◽  
Analysis Procedure ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Life Assessment ◽  
Life Evaluation ◽  
Useful Life ◽  
Size Evaluation ◽  
Steam Drum

A steam drum in a typical power plant has experienced in-service cracking. Nondestructive examinations (NDE) were performed and a small sample was collected from the drum to evaluate the extent of the cracking that had occurred in the drum shell. Fitness-for-service and remaining useful life analyses of the drum were performed based on the NDE results and operating conditions. In this paper, the fitness-for-service analyses of the steam drum are described. The analysis procedure, material property determination, stress analysis, limiting flaw size evaluation, and remaining useful life evaluation for the drum are discussed. Recommendations for appropriate action are also presented.

Evolution of Section XI of the ASME Boiler and Pressure Vessel Code

2000 ◽  
Vol 122 (3) ◽  
pp. 234-241 ◽  
Author(s):  
Owen F. Hedden
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Structural Integrity ◽  
Nuclear Regulatory Commission ◽  
Operating Conditions ◽  
Nondestructive Examination ◽  
The Us ◽  
Regulatory Commission ◽  
Technical Basis ◽  
Year 2000

This article will describe the development of Section XI from a pamphlet-sized document to the lengthy and complex set of requirements, interpretations, and Code Cases that it has become by the year 2000. Section XI began as a set of rules for inservice inspection of the primary pressure boundary system of nuclear power plants. It has evolved to include other aspects of maintaining the structural integrity of safety class pressure boundaries. These include procedures for component repair/replacement activities, analysis of revised and new plant operating conditions, and specialized provisions for nondestructive examination of components and piping. It has also increased in scope to cover other Section III construction: Class 2, Class 3 and containment structures. First, to provide a context for the discussions to follow, the differences in administration and enforcement between Section XI and the other Code Sections will be explained, including its dependence on the US Nuclear Regulatory Commission. The importance of interpretations and Code Cases then will be discussed. The development of general requirements and requirements for each class of structure will be traced. The movement of Section XI toward a new philosophy, risk-informed inspection, will also be discussed. Finally, an annotated bibliography of papers describing the philosophy and technical basis behind Section XI will be provided. [S0094-9930(00)01703-0]

Prognostics and RUL Estimations of SAC305, SAC105 and SnAg Solders Under Temperature and Vibration Using Long Short-Term Memory (LSTM) Deep Learning

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Short Term ◽  
Term Memory ◽  
Rectangular Pattern ◽  
Useful Life ◽  
Long Short Term Memory

Abstract Prognostics and Remaining Useful Life (RUL) estimations of complex systems are essential to operational safety, increased efficiency, and help to schedule maintenance proactively. Modeling the remaining useful life of a system with many complexities is possible with the rapid development in the field of deep learning as a computational technique for failure prediction. Deep learning can adapt to multivariate parameters complex and nonlinear behavior, which is difficult using traditional time-series models for forecasting and prediction purposes. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM) network is used to predict the remaining useful life of the PCB at different conditions of temperature and vibration. This technique can identify the different underlying patterns in the time series that can predict the RUL. This study involves feature vector identification and RUL estimations for SAC305, SAC105, and Tin Lead solder PCBs under different vibration levels and temperature conditions. The acceleration levels of vibration are fixed at 5g and 10g, while the temperature levels are 55°C and 100°C. The test board is a multilayer FR4 configuration with JEDEC standard dimensions consists of twelve packages arranged in a rectangular pattern. Strain signals are acquired from the backside of the PCB at symmetric locations to identify the failure of all the packages during vibration. The strain signals are resistance values that are acquired simultaneously during the experiment until the failure of most of the packages on the board. The feature vectors are identified from statistical analysis on the strain signals frequency and instantaneous frequency components. The principal component analysis is used as a data reduction technique to identify the different patterns produced from the four strain signals with failures of the packages during vibration. LSTM deep learning method is used to model the RUL of the packages at different individual operating conditions of vibration for all three solder materials involved in this study. A combined model for RUL prediction for a material that can take care of the changes in the operating conditions is also modeled for each material.

Sewer rehabilitation strategy in Berlin

2002 ◽  
Vol 46 (6-7) ◽  
pp. 379-387
Author(s):  
D. Jacobi ◽  
K.-J. Sympher
Keyword(s):  
Structural Integrity ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Remaining Useful Life ◽  
Sewer System ◽  
Sewer Systems ◽  
Catchment Areas ◽  
Sewer Rehabilitation ◽  
Useful Life ◽  
Rehabilitation Strategy

Berlin Wasserbetriebe is in need of a significant and longterm investment in the rehabilitation of its sewer system. With ratification of the European Standard EN 752 Part 5, comprehensive rules have been set out for the rehabilitation of drain and sewer systems: hydraulic performance, environmental impact and structural integrity of complete catchment areas are given equal consideration. Taking this into account, Berliner Wasserbetriebe has developed a sewer rehabilitation strategy. Economic aspects are integrated with a cost-benefit-analysis; the significance of the remaining useful life of a pipe section is examined.

scholarly journals Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

2018 ◽  
Vol 8 (12) ◽  
pp. 2416 ◽  
Author(s):  
Ansi Zhang ◽  
Honglei Wang ◽  
Shaobo Li ◽  
Yuxin Cui ◽  
Zhonghao Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Recurrent Neural Networks ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Training Data ◽  
Good Prediction ◽  
Useful Life

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

Elastic-Plastic Fracture Mechanics Analysis of Cast Stainless Steel Pipes (Influence of Axial Load on Z-Factor of Pipes With Circumferential Crack Under Bending Load)

2013 ◽  
Author(s):  
Masayuki Kamaya ◽  
Kiminobu Hojo
Keyword(s):  
Stainless Steel ◽  
Axial Load ◽  
Power Plants ◽  
Structural Integrity ◽  
Term Operation ◽  
Steel Pipes ◽  
Integrity Assessment ◽  
Plastic Failure ◽  
Elastic Plastic ◽  
Bending Load

Since the ductility of cast austenitic stainless steel pipes decreases due to thermal aging embrittlement after long term operation, not only plastic collapse failure but also unstable ductile crack propagation (elastic-plastic failure) should be taken into account for the structural integrity assessment of cracked pipes. In the ASME Section XI, the load multiplier (Z-factor) is used to derive the elastic-plastic failure of the cracked components. The Z-factor of cracked pipes under bending load has been obtained without considering the axial load. In this study, the influence of the axial load on Z-factor was quantified through elastic-plastic failure analyses under various conditions. It was concluded that the axial load increased the Z-factor; however, the magnitude of the increase was not significant, particularly for the main coolant pipes of PWR nuclear power plants.

scholarly journals Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

2021 ◽  
Vol 7 ◽  
pp. e795
Author(s):  
Pooja Vinayak Kamat ◽  
Rekha Sugandhi ◽  
Satish Kumar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Models ◽  
Vibration Data ◽  
Degradation Data ◽  
Useful Life

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

Integrity Assessment of HP/HT Infield Pipelines: Experiences With a New Methodology Applied in the Norwegian Sea

2008 ◽  
Author(s):  
Birger Etterdal ◽  
Hroar Nes ◽  
Stig Olav Kvarme ◽  
Stian Svardal
Keyword(s):  
Hot Spot ◽  
Operating Conditions ◽  
Pipeline System ◽  
Operating Pressure ◽  
Assessment Methodology ◽  
Long Term Conditions ◽  
Norwegian Sea ◽  
Integrity Assessment ◽  
Analysis Models

The subsea pipeline development for the A˚sgard and Midgard fields in the Norwegian Sea has been challenging due to high operating pressure and temperature (HP/HT pipelines), uneven seabed conditions and the potential for trawl gear interference. A general experience from the first years of operation is that it is not easy to use design information as basis for an integrity assessment of the lines. This is mainly due to the complexity of the global buckling process and the significant load fluctuations applied to the lines. As a consequence of this, analysis models established during design may not represent the actual pipeline behaviour properly, and established design limits do not fit intermediate operational load conditions and configurations observed during surveys. StatoilHydro has developed an integrity assessment methodology where analysis models are calibrated according to the as-surveyed condition, and then exposed to operational cyclic loads in order to predict both intermediate long term conditions and a final design condition. In the assessment of long term fatigue accumulation, process parameters monitored during pipeline operation are used as input. The integrity condition of the HP/HT pipelines is assessed based on a staged approach, depending on the criticality of the considered failure mode. The first level is used for screening and initial ranking. At level two the risk of integrity failure is quantified based on general design criteria, covering relevant operating conditions and the most important input parameters. If the uncertainty related to the assessment of an individual hot-spot location is assumed too high, a detail level three assessment may be specified. The operating condition of the pipeline system is expressed as the risk of failure defined by a limited number of hot-spot locations. The risk matrix concept used for the HP/HT pipelines, provides for a consistent comparison between individual failure modes, between different locations and sections, and between different pipeline systems. StatoilHydro has worked in close cooperation with DNV to develop software tools required to implement this integrity assessment methodology. These tools are now used for integrity assessment and follow-up of all HP/HT pipelines operated by StatoilHydro in the Norwegian Sea. The objective of this paper is to show how the methodology is used in practice, discuss major results and findings, and give general recommendations with respect to operational integrity assessment of HP/HT pipelines.

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