Critical Engineering Systems Research Articles

Damage Localization and Severity Assessment in Composite Structures Using Deep Learning Based on Lamb Waves

In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures. In contrast, this study aims to perform damage detection, severity assessment, and localization using independent DL models. Three DL models, namely the artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU), are compared. To assess their damage detection and localization capabilities. Moreover, zero-mean Gaussian noise is introduced as a data augmentation technique to address the variability and noise inherent in LW signals, improving the generalization capability of the DL models. The proposed framework is validated on a composite plate with four piezoelectric transducers, one at each corner, and achieves high accuracy in both damage localization and severity assessment, offering an effective solution for real-time structural health monitoring. This dual-function approach provides a scalable data-driven method to evaluate composite structures, with applications in predictive maintenance and reliability assurance in critical engineering systems.

How to Be Certain: Using Known Relations and Trust Discount to Determine Confidence About the Degree of Uncertainty

Ensuring certainty in the rapidly evolving digital twin environments amidst inherent uncertainty is paramount in decision-making for critical engineering systems. This paper presents a novel approach for quantifying and tracking confidence levels within a network of sensors and structural models, specifically addressing uncertainty arising from faulty sensors. By leveraging known relations and implementing a trust discount mechanism, our methodology offers a fundamental framework for navigating in the presence of uncertainties. A quasi-real-time case study on a cantilever beam model is simulated to demonstrate the efficacy of the proposed approach. We showcase the ability of our method to accurately assess and adapt to varying levels of uncertainty introduced by faulty sensors. Our findings highlight the importance of incorporating trust dynamics and established relationships within digital twin environments to achieve improved certainty despite imperfect data. This research contributes to the theoretical underpinnings of digital twin technology and offers practical insights for its application across diverse domains.

A novel modeling framework for a degrading system subject to hierarchical inspection and maintenance policy

Most engineering systems exhibit degrading performance in their designated service times as influenced by their working conditions. Thus, inspection and maintenance actions are crucial for maintaining their performance. Among these, critical engineering systems, such as compressors and safety valves, are typically subject to a multi-level inspection scheme consisting of partial and full inspections. In this study, inspired by this inspection scheme, we propose a hierarchical maintenance model to incorporate collected system performance information from these inspection interventions. Partial inspections verify whether the system is functional or not, and then partial preventive maintenance is performed; a more accurate system state is known at the instant of full inspections, which serve as windows for the conduction of condition-based maintenance. The proposed maintenance policy is then applied to a system undergoing monotone stochastic degradation. Subsequently, the average cost per unit time in the operational phase and the sensitivity analysis with respect to variables, such as the preventive maintenance threshold, inspection intervals, and the maintenance effect factors, are investigated. Theoretical results and numerical examples are obtained to examine the applicability of the generalized model.

Probabilistic invertible neural network for inverse design space exploration and reasoning

<abstract> <p>Invertible neural network (INN) is a promising tool for inverse design optimization. While generating forward predictions from given inputs to the system response, INN enables the inverse process without much extra cost. The inverse process of INN predicts the possible input parameters for the specified system response qualitatively. For the purpose of design space exploration and reasoning for critical engineering systems, accurate predictions from the inverse process are required. Moreover, INN predictions lack effective uncertainty quantification for regression tasks, which increases the challenges of decision making. This paper proposes the probabilistic invertible neural network (P-INN): the epistemic uncertainty and aleatoric uncertainty are integrated with INN. A new loss function is formulated to guide the training process with enhancement in the inverse process accuracy. Numerical evaluations have shown that the proposed P-INN has noticeable improvement on the inverse process accuracy and the prediction uncertainty is reliable.</p> </abstract>

Bayesian Networks based approach to enhance GO methodology for reliability modeling of multi-state consecutive-k-out-of-n: F system

As a success-oriented system reliability and safety analysis technique, GO methodology is widely used in the modeling and analysis scenarios of critical safety engineering systems with signal flow, such as nuclear, chemical industry, electric power transportation, wireless network and microwave communication network. Consecutive-k-out-of-n systems are also widely applied in these fields, but due to their relatively complex logic, there are no corresponding graphical modeling and calculation methods. In this paper, the basic GO methodology is extended to support the modeling and analysis of multi-state linear and circular consecutive-k-out-of-n: F system (MLC(k,n) &MCC(k,n)) models. Two new operators are defined in graphical modeling to represent the two types of complex system logic. The multi-state logic semantics of the new operators and mapping rules to Bayesian networks (BN) are given. And the programmable process is presented to transform the extended GO model into BN, and the reliability of the MLC(k,n)/MCC(k,n) is calculated. Finally, three cases are used to verify i) the correctness of the method supporting multi-state model, ii) the efficiency and ability of the algorithm, and iii) the forward calculation and reverse diagnosis of the MLC(k,n) and MCC(k,n) systems with external shared components.

Possibilities of Reliable Ultrasonic Detection of Subwavelength Pipework Cracks

An occurrence of cracks in pipework could lead to potentially very dangerous malfunction in some critical engineering systems such as power plants. There is a clear trend of replacing traditional manual testing with non-invasive in-situ methods that should detect crack formation in its early stage. Such as approach would enable replacing of unhealthy pipe components during the regular periodic outages. Ultrasonic testing is known to be a rather mature and reliable technology. However, it suffers from serious problems in detection of the cracks of subwavelength size. This paper attempts to soften aforementioned problems by investigating the influence of a duration of the unipolar excitation signal on the achieved resolution. In addition, the transducer input electricalimpedance of NDT transmitter was measured by using different excitation pulses and their levels and the results are compared with those obtained using traditional frequency sweeping method at low excitation levels. Finally, use of some advanced signal processing algorithms that might lead to the automatic detection of subwavelength voids, in scenarios with low signal-to-noise ratio, is discussed.

Risk analysis virtual ENvironment for dynamic event tree-based analyses

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also known as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used DPRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). Since 2012, the Idaho National Laboratory (INL) is developing its own tool to perform Dynamic PRA: RAVEN (Risk Analysis and Virtual ENvironment). RAVEN has been designed in a high modular and pluggable way to enable easy integration of different programming languages (i.e., Python, C++) and coupling with other application including, among the others, several thermal–hydraulic and severe accident codes (e.g., RELAP5-3D, MELCOR, MAAP5, TRACE, etc.). RAVEN is aimed to provide a framework/container of capabilities for engineers and scientists to analyze the response of systems, physics and multi-physics, employing advanced numerical techniques and algorithms. Moreover, RAVEN models stochastic events, such as components failures, and performs uncertainty quantification (UQ). Such stochastic modeling is employed by using sampling strategies among which both MC and DET algorithms, which are going to be employed in this paper. In addition, RAVEN processes the large amount of data generated by sampling the physical models using data-mining based algorithms and risk assessment techniques. This paper provides an overview of the DET methodologies that have been deployed within the RAVEN framework, showing the potential of such techniques for the analysis of complex systems. A brief background of classical methodologies and their limitation is also reported and represent the motivation for the deployment of such dynamic technique. In addition, results from a pressurized water reactor loss of coolant accident scenario, using RELAP5-3D as physical model, are reported.

Safety modelling of marine systems using neutrosophic logic

Precisely assessing risk associated with critical and complex engineering systems operating in an environment with high amount of uncertainty is extremely difficult. Taking proper decisions to reduce risk level depends on how accurately the risks associated with the system are assessed. Risk assessment and safety modelling of complex systems were studied using various tools such as crisp sets, Bayesian belief network, fuzzy sets, classical logic and fuzzy logic. Neutrosophic set which is an extension of fuzzy set represents the real world better than fuzzy set due to the inclusion of the hesitancy component in the set. This research focuses on developing a neutrosophic logic–based model employing neutrosophic IF-THEN rules using linear trapezoidal neutrosophic number and analytical hierarchy process for qualitative and subjective analysis of human knowledge. The model is validated using an illustrative example for safety analysis of marine system.

Reliability analysis of standby systems with multi‐state elements subject to constant transition rates

AbstractStandby redundancy has been extensively applied to critical engineering systems to enhance system reliability. Researches on reliability evaluation for standby systems focus more on systems with binary‐state elements. However, multi‐state elements with different performances have played a significant role in engineering systems. This paper presents an approach for reliability analysis of standby systems composed of multi‐state elements with constant state transition rates and absorbing failure states. The approach allows modelling different standby systems beyond cold, warm and hot ones by taking into account differences in possible maintenance of elements in standby and operation modes and dependence of elements' operational behavior on their initial state at the time of activation. An iterative algorithm for reliability evaluation based on element state probabilities is suggested. Illustrating examples of evaluating reliability of different types of homogeneous and heterogeneous standby systems are demonstrated.

Remaining useful life prediction for engineering systems under dynamic operational conditions: A semi-Markov decision process-based approach

For critical engineering systems such as aircraft and aerospace vehicles, accurate Remaining Useful Life (RUL) prediction not only means cost saving, but more importantly, is of great significance in ensuring system reliability and preventing disaster. RUL is affected not only by a system’s intrinsic deterioration, but also by the operational conditions under which the system is operating. This paper proposes an RUL prediction approach to estimate the mean RUL of a continuously degrading system under dynamic operational conditions and subjected to condition monitoring at short equi-distant intervals. The dynamic nature of the operational conditions is described by a discrete-time Markov chain, and their influences on the degradation signal are quantified by degradation rates and signal jumps in the degradation model. The uniqueness of our proposed approach is formulating the RUL prediction problem in a semi-Markov decision process framework, by which the system mean RUL can be obtained through the solution to a limited number of equations. To extend the use of our proposed approach in real applications, different failure standards according to different operational conditions are also considered. The application and effectiveness of this approach are illustrated by a turbofan engine dataset and a comparison with existing results for the same dataset.

Lifetime estimation of nitrile butadiene rubber O-rings under storage conditions using time-varying copula

With several attractive properties, nitrile butadiene rubber O-rings have been widely used as compression seal components in various critical engineering systems. Therefore, it is important to obtain an accurate lifetime estimation of the nitrile butadiene rubber O-ring. In this study, aiming to estimate the lifetime of a nitrile butadiene rubber O-ring under storage conditions, a reliability model is developed by considering two performance characters, namely, compression set and compressive stress relaxation. With a long lifetime in storage, a constant-stress accelerated degradation testing is conducted to obtain the degradation model for each performance character. In addition, the statistical dependency between the two performance characters is considered, and a bivariate time-varying copula model is used to capture this relationship. An efficient two-stage approach is proposed to construct a suitable time-varying copula function. By comparing with previously reported time-varying copulas, it can be proved that the time-varying copula constructed using the proposed approach achieves a better goodness of fit.

Accelerated failure identification sampling for probability analysis of rare events

Critical engineering systems generally demand high reliability while considering uncertainties, which makes failure event identification and reliability analysis based on computer simulation codes computationally very expensive. Rare event can be defined as a failure event that has a small probability of failure value, normally less than 10ź5. This paper presents a new approach, referred to as Accelerated Failure Identification Sampling (AFIS), for probability analysis of rare events, enabling savings of vast computational efforts. To efficiently identify rare failure sample points in probability analysis, the proposed AFIS technique will first construct a Gaussian process (GP) model for system performance of interest and then utilize the developed model to predict unknown responses of Monte Carlo sample points. Second, a new quantitative measure, namely "failure potential", is developed to iteratively search sample points that have the best chance to be a failure sample point. Third, the identified sample points with highest failure potentials are evaluated for the true performance and then used to update the GP model. The failure identification process will be iteratively preceded and the Monte Carlo simulation will then be employed to estimate probabilities of rare events if the maximum failure potential of existing Monte Carlo samples falls below a given target value. Two case studies are used to demonstrate the effectiveness of the developed AFIS approach for rare events identification and probability analysis.

Semi-Automatic FMEA Supporting Complex Systems with Combinations and Sequences of Failures

Failure Modes and Effects Analysis (FMEA) is a well established safety analysis technique used for the assessment of safety critical engineering systems in the automotive industry. Although FMEA ha ...

Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment

Traditional fault tree (FT) analysis is widely used for reliability and safety assessment of complex and critical engineering systems. The behavior of components of complex systems and their interactions such as sequence- and functional-dependent failures, spares and dynamic redundancy management, and priority of failure events cannot be adequately captured by traditional FTs. Dynamic fault tree (DFT) extend traditional FT by defining additional gates called dynamic gates to model these complex interactions. Markov models are used in solving dynamic gates. However, state space becomes too large for calculation with Markov models when the number of gate inputs increases. In addition, Markov model is applicable for only exponential failure and repair distributions. Modeling test and maintenance information on spare components is also very difficult. To address these difficulties, Monte Carlo simulation-based approach is used in this work to solve dynamic gates. The approach is first applied to a problem available in the literature which is having non-repairable components. The obtained results are in good agreement with those in literature. The approach is later applied to a simplified scheme of electrical power supply system of nuclear power plant (NPP), which is a complex repairable system having tested and maintained spares. The results obtained using this approach are in good agreement with those obtained using analytical approach. In addition to point estimates of reliability measures, failure time, and repair time distributions are also obtained from simulation. Finally a case study on reactor regulation system (RRS) of NPP is carried out to demonstrate the application of simulation-based DFT approach to large-scale problems.

WITHDRAWN: Performance modeling in critical engineering systems using RAM analysis

The publisher regrets that this article is an accidental duplication of an article that has already been published, doi: 10.1016/j.ress.2007.03.039 . The duplicate article has therefore been withdrawn.

Performance modeling in critical engineering systems using RAM analysis

Reliability, availability and maintainability (RAM) analysis of system is helpful in carrying out design modifications, if any, required to achieve minimum failures or to increase mean time between failures (MTBF) and thus to plan maintainability requirements, optimize reliability and maximize equipment availability. To this effect, the paper presents the application of RAM analysis in a process industry. Markovian approach is used to model the system behavior. For carrying out analysis, transition diagrams for various subsystems are drawn and differential equations associated with them are formulated. After obtaining the steady state solution the corresponding values of reliability and maintainability are estimated at different mission times. The computed results are presented to plant personnel for their active consideration. The results proved helpful to them for analyzing the system behavior and thereby to improve the system performance considerably by adopting and practicing suitable maintenance policies/strategies.

The Effect of Nonconventional Laser Beam Geometries on Stress Distribution and Distortions in Laser Bending of Tubes

Laser forming is a spring-back-free noncontact forming method that has received considerable attention in recent years. Compared to mechanical bending, no hard tooling, dies, or external force is used. Within laser forming, tube bending is an important industrial activity with applications in critical engineering systems such as heat exchangers, hydraulic systems, boilers, etc. Laser tube bending utilizes the thermal stresses generated during laser scanning to achieve the desired bends. The parameters varied to control the process are usually laser power, beam diameter, scanning velocity, and the number of scans. The thermal stresses generated during laser scanning are strongly dependent upon laser beam geometry. The existing laser bending methods use either circular or rectangular beams. These beam geometries sometimes lead to undesirable effects such as buckling and distortion in tube bending. This paper investigates the effects for various laser beam geometries on laser tube bending. Finite element modeling has been used for the study of the process with some results also validated by experiments.

Finite element simulation of laser tube bending: Effect of scanning schemes on bending angle, distortions and stress distribution

Laser forming has received considerable attention in recent years. Within laser forming, tube bending is an important industrial activity, with applications in critical engineering systems like micro-machines, heat exchangers, hydraulic systems, boilers, etc. Laser tube bending utilizes the thermal stresses generated during laser scanning to achieve the desired bends. The parameters to control the process are usually laser power, beam diameter, scanning velocity and number of scans. Recently axial scanning has been used for tube bending instead of commonly used circumferential scans. However the comparison between the scanning schemes has involved dissimilar laser beam geometries with circular beam used for circumferential scanning and a rectangular beam for the axial scan. Thermal stresses generated during laser scanning are strongly dependent upon laser beam geometry and scanning direction and hence it is difficult to isolate the contribution made by these two variables. It has recently been established at the Corrosion and Protection Centre, University of Manchester, that corrosion properties of material during laser forming are affected by the number of laser passes. Depending on the material, the corrosion behaviour is either adversely or favourably affected by number of passes. Thus it is of great importance to know how different scanning schemes would affect laser tube bending. Moreover, any scanning scheme which results in greater bending angle would eliminate the need for higher number of passes, making the process faster. However, it is not only the bending angle which is critical, distortions in other planes are also extremely important. Depending on the use of the final product, unwanted distortions may be the final selection criteria. This paper investigates the effect of scanning direction on laser tube bending. Finite-element modelling has been used for the study of the process with some results also validated by experiments.

Adaptive control of dynamically substructured systems: The single-input single-output case

In recent years, there has been much interest in using the principle of dynamic substructuring as a framework for the testing of critical engineering components and systems. The most significant advantage of the method is that it can offer the opportunity to test full-size non-linear components within a laboratory environment. Such a test would be run in parallel with a real-time numerical simulation of the remaining part of the overall system to be emulated. Potentially, the most significant disadvantage of the method is the very high fidelity of control that is required, in order to achieve near-perfect synchronization of the test rig and the numerical model. This problem is further exacerbated by the presence of unknown and changing dynamic parameters, disturbances, and non-linearities in the test rig. The purpose of this paper is to lay a foundation for the linear control of dynamically sub-structured systems and, leading on from that, robust adaptive control via an extension to the adaptive minimal control synthesis (MCS) algorithm. Comparative simulation results from using the linear and adaptive control strategies are also included in the paper.

State of the Art of Ultradeepwater Production Technologies

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper OTC 17615, "State of the Art of Ultra Deepwater Production Technologies," by J.M. Bell, SPE, Chevron; Y.D. Chin, SPE, Aker Kv ærner; and S. Hanrahan, SPE, Chevron, prepared for the 2005 Offshore Technology Conference, Houston, 2-5 May. The full-length paper summarizes some results of several recent joint industry projects (JIPs) on ultradeepwater field-development technologies. A review of emerging and existing technologies for deepwater development is presented, which would be of particular interest to technology-development personnel and asset-team personnel who are in the appraisal or concept-selection stages of a project. Introduction As offshore production moves from deep water (3,000 to 6,000 ft) to ultradeep water (6,000 to 10,000 ft) and moves farther away from infrastructures, many existing technologies reach their limits or must be improved. Several successful JIPs have demonstrated that in the high-expense and -risk environment of ultradeep water, structured collaboration aligned with business drivers is a successful formula for delivering the technologies required to meet the ultradeepwater challenge. The demand for critical technology and engineering systems to enable production from ultradeep water has triggered breakthroughs in many conventional technologies. The full-length paper focuses on sub-sea, flow-assurance, floating-production-system, and riser technology.