Potential Failure Research Articles

Enhancing manufacturing efficiency and quality through automation and deep learning: addressing redundancy, defects, vibration analysis, and material strength optimization

This article explores the integration of automation and deep learning in modern manufacturing to address critical challenges such as redundancy, defects, vibration analysis, and material strength. As manufacturing processes evolve, the need for more sophisticated methods to optimize production efficiency and product quality becomes paramount. Automation, coupled with deep learning techniques, offers powerful tools for enhancing manufacturing processes. These technologies enable predictive maintenance, reducing downtime by identifying potential equipment failures before they occur. Furthermore, deep learning algorithms can analyse complex data sets to detect defects in products with greater accuracy and speed than traditional methods. Vibration analysis, a key aspect of predictive maintenance, benefits from automated systems that monitor and diagnose issues in real-time, preventing costly disruptions. Additionally, deep learning models can assess material strength and predict potential failures, ensuring that products meet rigorous safety and quality standards. The synergy between automation and deep learning not only streamlines manufacturing processes but also enhances the ability to adapt to changing conditions, thereby minimizing operational inefficiencies. This article highlights the transformative impact of these technologies on the manufacturing industry, illustrating their potential through case studies and practical examples. By addressing key challenges such as redundancy and defects, automation and deep learning contribute to the creation of more reliable, efficient, and resilient manufacturing systems. The insights provided in this study underscore the importance of continued innovation in integrating these technologies to maintain a competitive edge in the rapidly evolving manufacturing landscape.

On Not Speaking

Abstract This article explores the challenges of intercultural dialogue between European and non-European epistemologies through a study of Heidegger’s conversation with a Japanese thinker. It discusses the inherent challenges and potential failures in such dialogues, emphasizing the differences in language and thought systems, and concludes by considering the ethical implications of these dialogues and the necessity of an “ethics of not speaking”.

Data Analysis and Algorithm Innovation in Power System IntelligentMonitoring and Early Warning Technology

Data analysis and algorithm innovation play a pivotal role in enhancing power system intelligent monitoring and early warning technology. With the increasing complexity of modern power grids, the integration of advanced data analytics enables real-time monitoring, fault detection, and predictive maintenance. By leveraging machine learning algorithms, anomaly detection techniques, and big data analytics, power systems can efficiently identify potential risks and failures before they escalate into serious issues. These innovations not only improve grid reliability and resilience but also optimize resource utilization. Early warning mechanisms based on intelligent algorithms provide timely alerts, allowing for preventive measures that ensure the stability and safety of the power network. This approach fosters a smarter, more adaptive power infrastructure capable of meeting growing energy demands while minimizing downtime and disruptions.This paper presents a comprehensive investigation into the development and efficacy of an intelligent early warning system for power systems. Leveraging machine learning algorithms, IoT sensors, and cloud computing frameworks, the system aims to enhance real-time monitoring capabilities and facilitate proactive intervention and maintenance. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the system achieves an average accuracy of 95%, precision of 92%, recall of 94%, and F1 score of 92% across multiple iterations. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems.

Software data objects application integrity modeling in medication dispensing errors predictions using machine learning algorithms in workflow-based pharmacy software systems

The study focuses on using the software objects created at each stage of the workflow-based systems (and in our article we can focus on the pharmacy-based medication dispensing software systems) to predict the anomalies or potential issues in that software object constructed at each stage of the workflow using the historical issues recorded in that specific workflow stage using linear regression, decision trees, and neural network algorithms. By utilizing historical data and integrity metrics, the research aims to forecast potential failures and maintain the reliability of software applications. The focus is on employing algorithms such as linear regression, decision trees, and neural networks to enhance predictive accuracy and facilitate informed decisions or recommendations to the workflow users (pharmacists) about the potential medication dispensing error the software objects constructed at that state or workflow could cause and advise to take necessary corrections.

Balancing plant safety and efficiency through innovative engineering practices in oil and gas operations

In the oil and gas industry, balancing plant safety with operational efficiency is a critical challenge that requires innovative engineering practices. This paper explores how advanced engineering techniques can enhance both safety and efficiency in oil and gas operations. By integrating cutting-edge technologies and methodologies, companies can achieve a harmonious balance between protecting personnel, equipment, and the environment, while also optimizing productivity and resource utilization. Key innovative engineering practices discussed include the implementation of real-time monitoring systems and predictive analytics to enhance safety and operational efficiency. Real-time monitoring, powered by Internet of Things (IoT) sensors, allows for continuous tracking of plant conditions, detecting anomalies before they escalate into significant issues. Predictive analytics, utilizing machine learning algorithms, further aids in forecasting potential failures and scheduling maintenance proactively, thereby minimizing downtime and operational disruptions. The paper also examines the role of risk-based approaches and safety management systems in improving plant safety. Techniques such as quantitative risk assessment (QRA) and safety integrity level (SIL) analysis are pivotal in identifying potential hazards and designing mitigation strategies. These practices ensure that safety measures are proportionate to the actual risk, avoiding over-engineering while addressing critical safety concerns. Moreover, the integration of automation and digital twin technology is discussed as a means to enhance both safety and efficiency. Automation reduces human error and improves precision in operations, while digital twins provide a virtual replica of physical assets, enabling simulation and optimization of plant operations in a risk-free environment. Case studies illustrate successful implementations of these practices, highlighting their impact on reducing incidents and improving operational performance. The paper concludes by emphasizing the need for ongoing innovation and collaboration between engineers, safety experts, and operational managers to continuously advance safety standards and operational efficiency in oil and gas operations.

Reflective and ruminative types of self-consciousness among young adults with perfectionistic personality traits

Introduction and objective: Motives for self-observation can be diverse. This study aims to determine whether young adults with perfectionistic personality traits are closer to the ruminative or reflective type of private self-consciousness. Materials and methods: The study involved 183 healthy individuals aged 18–30 years (M = 20.87, SD = 3.24), with women accounting for 57%. The Big Three Perfectionism Scale (BTPS) by Smith and the Rumination-Reflection Questionnaire (RRQ) by Trapnell and Campbell were used. Results: The results indicate that perfectionism is positively related to the ruminative type of self-consciousness. Reflexivity, as cognitive curiosity about oneself, represents a more mature way of functioning for which perfectionism is unnecessary. In contrast, perfectionism is strongly associated with critical self-examination and has much in common with the ruminative form of self-consciousness. Self-critical perfectionism thus becomes a strategy whereby increased rumination, i.e. ruminating about past and potential failures, leads individuals to believe they are preventing future failures. Conversely, grandiose perfectionism protects against rumination. Individuals do not perceive their mistakes and, therefore, do not ruminate about them. Conclusions: Based on the Three Big Perfectionism Scale, the results confirm the association of the ruminative type of private self-consciousness with perfectionism and its absence between the reflective type and perfectionism.

Improved GAN-based deep learning approach for strain field prediction and failure analysis of precast bridge slab joints

Nonlinear finite element analysis (FEA) is crucial for understanding complex stress-strain behaviors and assessing potential structural failure risks. Building on the recent success of deep learning (DL) methods in physical field predictions, there has been growing interest in forecasting internal force fields of structures beyond the existing data scope. In this study, a data-driven strain field analysis model based on an improved Generative Adversarial Network (GAN) is proposed. To enrich the input data, this novel approach incorporates the Signed Distance Field (SDF) derived from the characteristics of the original Finite Element (FE) models. The generator, based on U-Net, is enhanced with channel attention mechanisms and loss penalty terms, forming the core of the improved GAN. The effectiveness of the proposed method is demonstrated using two parallel datasets comprising 3-Headed bar tension models across two planes. Subsequently, the generated strain field is subjected to feature extraction using the 2D-Principal Component Analysis (2D-PCA) method, followed by structural failure determination through the K-Nearest Neighbor (KNN) algorithm. The results indicate that the proposed improved approach achieves a significant reduction in Root Mean Square Error (RMSE) of 20.71 % and 13.21 % across the two datasets, with the structural failure determination method reaching an F1-score of 0.971. Thus, the intelligent strain field analysis method introduced here effectively predicts strain distributions and promptly identifies failure states, offering valuable insights for further numerical analysis and structural design.

Optimizing individualized therapy decision-making in multiple myeloma (MM): integration and impact of the Revised Myeloma Comorbidity Index in the MM-tumor board

AbstractMultiple Myeloma (MM) is a hematological disease predominantly affecting elderly patients. The complexity of current treatment necessitates individualized approaches. Therein, functional assessment (FA) tools, such as the Revised Comorbidity Index (R-MCI) at our University- and Comprehensive Cancer Center Freiburg, play a crucial role. This study aimed to determine (a) the implementation of the R-MCI in our MM-tumor board (MM-TB), (b) its impact on treatment guidance at baseline and (c) potential changes during follow-up. This exploratory study investigated R-MCI coverage and distribution in a cohort of patients with multiple TB presentations. Among them, a follow-up patient cohort undergoing subsequent MM-therapy was analyzed to determine treatment adjustments and changes in patients’ condition measured by R-MCI alterations. During our 3-year assessment period, 565 patients were presented in our MM-TB, totaling 1256 TB-presentations. In the multiple TB presentation cohort, the median number of TB presentations was 3 (range: 2–12). R-MCI scores within the MM-TB were available in 94%, whereas in 6%, the R-MCI had not been integrated. Among these, potential failure to identify the need for treatment modifications was determined. In the follow-up cohort, patient characteristics were typical for referral/university centers. Dose reductions were performed in 55% and were more prevalent among patients with ≥ 4 vs. lesser TB presentations. Most patients (55%) showed a fitness stabilization or improvement via follow-up R-MCI. R-MCI integration in MM-TB exceeded > 90%, indicating its successful integration for treatment support. Our results underscore its value in guiding therapy decisions, providing a comprehensive assessment beyond age considerations.

INNOVATIVE EXPERIMENTAL TESTING PROGRAM OF DIRECT SHEAR TEST IN SOIL MECHANICS

The research work aims at analyzing for the first time the data set obtained on cohesive soil samples following the publication of the Romanian Invention Patent RO 134239. The standard test method for the direct shear test provides the shear strength parameter – internal friction angle in consolidated drained condition - of either undisturbed or remolded soil samples forcing the shear plane at the midsection of the sample in the horizontal direction. The samples are provided in parallelepipedal shape (6 cm x 6 cm x 2 cm) and the displacement rate in horizontal direction is 0.1 mm/min. The new equipment patented in Romania changes the direction of shearing, from horizontal to vertical, and the soil samples are of cubic shape (6 cm x 6 cm x 6 cm). The experimental program involves testing both the parallelepipedal and cubic samples using the same motorized mechanism, with simultaneous readings from their respective micro-comparators. The UU test is performed without allowing consolidation and shearing at 1.0 mm/min. For the CD test, samples are consolidated under vertical loads for 24 hours before shearing at 0.1 mm/min. The shear stresses for cubic samples were higher than those for parallelepipedal samples, with residual stresses reflecting this trend. For cubic samples, both the peak and residual shear stresses trend lines indicated higher cohesion (c) and lower internal friction angle (𝜙) for UU tests and CD tests in contrast to parallelepipedal samples in both testing conditions. The innovative testing program allows for variability in shear strength parameters along the soil failure surface in both natural and compacted soil structures. This differentiation divides the soil condition into drained and undrained states at the initiation, emergence points, and the point of maximum depth along the failure surface. This approach is significant for accurately assessing soil shear resistance and potential failure mechanisms. The study's findings suggest a nuanced approach to parameter selection for slope stability analysis, ensuring accurate representation of both cohesion and internal friction in stability models.

Urban heat transitions policy; Zooming in on Dutch natural gas-free neighbourhoods

Heating transitions, where residential and industrial heating demand transitions away from fossil fuels, are an essential part of sustainability transitions. Thus far, the focus on heating transitions is neglected in policy studies, even though policy interventions are not only understood as essential, but also difficult to implement. In this article we seek to understand how policy can support the heating transition in countries with high distribution of natural gas in household consumption. By aligning transition typologies, consisting of a discussion of transition paths and innovation frames, with policy intervention points to existing literature on policy studies of heating transitions, we identify lessons learned and factors necessary for rapid heating transitions. This allows us to understand which type of transition is best stimulated through which intervention point. We then applied this framework to the Dutch natural gas phase out package, to test and validate the framework, as well as identify policy gaps and failures for the Dutch context. Here we were able to highlight important policy gaps and identified potential failures for policy effectiveness. This framework may be used in future studies and by policymakers to understand policy gaps and potential failures, supporting the (re)design of policy packages.

Operational status effect on the seismic risk assessment of oil refineries

The operational status of an oil refinery (type and scale of operations that take place at any time instance) largely determines the amount of fuel produced, circulated within the facility, and stored in tanks. This status is affected by seasonality, periods of peak or low demand, as well as periods of routine maintenance. However, it is an aspect that is typically neglected even though it stands out among the factors that determine the seismic performance of several critical industrial assets, such as the storage tanks, as well as the consequences of any potential failure. An open-source refinery testbed is employed herein to demonstrate the effect of the refinery's operational status on the seismic risk estimates. Alternative realistic operational scenarios are developed following typical industry practices and are arranged over a time period between two refinery major maintenance shutdown events. The most probable damage state is selected for each asset to identify the most vulnerable ones. Based on the type and importance of the impacted assets, the potential consequences are determined at the facility level. Resulting estimates are very different if an earthquake strikes during a regular/high/low-demand period, or a maintenance period. The framework can be utilized to identify the locations within the refinery that may trigger cascading failures and secondary damages, should their assets be damaged by a seismic event. The outcomes can be exploited by stakeholders, risk engineers, and emergency action planners for developing customized and businesslike procedures to enhance the seismic resilience of the facility.

Damage propagation and failure mechanism of single dome historical Masonry Mosque after February 6, 2023, Kahramanmaraş earthquake doublets (Mw = 7.7 and Mw = 7.6)

Many historical masonry mosques and minarets, including Milky Minaret Mosque and Ulu Mosque, which are very close to Ak Minaret Mosque in Malatya, suffered severe damage and collapsed after the Kahramanmaraş earthquake on February 6, 2023. Despite the settled loose soil properties and the presence of a small stream flow near the mosque, Ak Minaret Mosque, a historical masonry mosque with a single dome and square, remains operational even after the aforementioned earthquakes. Additionally, DEMA's strong ground motion station was out of service during both earthquakes. This situation raises questions about the mosque's ability to withstand the seismic load. The basic purpose of this paper is to investigate in detail the seismic performance, damage limits, the reason for unexpectedly less damage due to the earthquakes, the potential failure mode, and possible resisted earthquake loads of the historical Ak Minaret Mosque in Malatya, Türkiye. To achieve this goal, first, dynamic identification was performed on the mosque one year prior to the aforementioned earthquakes. Next, the material properties were determined using both non-destructive and destructive testing methods. Following the dynamic identification, a numerical model was generated by 3-D solid elements, and this 3-D model was calibrated using the dynamic identification tests. The mosque underwent both nonlinear static and nonlinear dynamic analyses. Nine seismic records were selected for the nonlinear dynamic analysis. Five of them were national, including even Kahramanmaraş earthquake records, and three of them were selected from an international database on the basis of fault characteristics and site classification. The analysis results indicate that Ak Minaret Mosque incurred less damage than expected during the Kahramanmaraş earthquakes. This could be due to soil improvement prior to the construction of Ak Minaret Mosque in 1573. Moreover, the effective restoration increased stiffness and maintained the mosque’s stability. Finally, the possible resisted PGA by the mosque was around 0.22 g.

Contribution to Conditional Maintenance by Vibration Analysis of Rotating Machine Mechanical Failures and Proposed Solutions

Objective: The aim of this study is to analyze the Cement Separator machine and identify potential condition indicators through vibration measurements and analysis. The goal is to diagnose and repair the machine before any material damage occurs. Background: Maintenance Technology: Modern maintenance technologies are crucial for monitoring equipment performance and planning timely maintenance interventions. Vibration Monitoring: As a key component in maintenance programs, especially for mechanical systems, vibration monitoring provides early warnings of faults based on the actual state of the machine. Cement Separator Machine: Issue: The cement separator exhibits significant vibrations despite its low operating speed. Data Sources: The construction and operational details of the machine are available from its operating manual and vibration measurements. Methodology: Practical Vibration Measurements: Extensive measurements are performed in a research laboratory and on-site to monitor the machine's condition. Vibration measuring devices are used to monitor rotating machines at specified intervals according to their condition and international standards. Condition Indicators: Various condition indicators are monitored to detect possible deterioration and the onset of mechanical or electrical failures during the machine's operation. Experimental Studies: Practical experiments validate the theoretical and numerical findings and refine the vibration analysis approach. Strategy: Adequate scheduling of interventions and repairs based on the machine's condition. Continuous monitoring through vibration analysis devices. Expected Outcomes: Early detection of potential mechanical or electrical failures. Sustainable solutions to reduce vibrations and enhance the machine's performance. Improved maintenance scheduling to prevent material damage and extend the machine's lifespan. By integrating vibration monitoring and analysis with practical studies, this research aims to provide a comprehensive diagnosis and repair strategy for the cement separator machine.

Enhanced adhesion and corrosion resistance through the modulation of a metallic Ti underlayer thickness

In this investigation, TiCN/TiN thin solid film was selected as a protective coating and deposited onto AZ31 alloy through reactive magnetron sputtering. To mitigate the potential failure resulting from the significant physical disparity between the ceramic film and the metal substrate, a Ti thin film was employed as a base layer. Following the deposition process, an analysis was conducted on the structural features, residual stress, fracture morphology, adhesion strength, and corrosion characteristics of the film/substrate system utilizing X-ray diffraction, scanning electron microscopy, scratch testing, and electrochemical workstation. The findings indicate that the incorporation of a Ti metallic underlayer significantly improves the adhesion and corrosion resistance of the TiCN film. However, when the thickness of the Ti layer surpasses 0.37 μm (deposition of 15 min), it leads to the formation of a distinct columnar crystal microstructure, increased residual stress, and diminished adhesion and corrosion resistance. Additionally, this study delves into the corrosion failure mechanisms in a chloride environment and explains the principles of residual stress measurement through XRD.

Risk Assessment of ZAP-X→ Radiosurgery System in a Quaternary Care Hospital Using FMEA Approach

Background and Aims: The overarching goal of this study is to conduct a robust risk assessment using the Failure Modes and Effects Analysis (FMEA) approach. By identifying potential failure modes and their potential impacts on patient care and hospital operations, this research seeks to provide actionable insights and mitigation strategies for the safe and effective implementation of the ZAP-X Radiosurgery System. The swift integration of advanced medical technologies, exemplified by the ZAP-X→ Gyroscopic Stereotactic Radiosurgery System, a computer-controlled machine that delivers radiation to an intended target in the brain, necessitates a comprehensive proactive risk assessment before the launch of the system to ensure the safety of patients, staff and operational processes. Method: Applying the FMEA methodology, we meticulously mapped all processes and sub-processes associated with the ZAP-X Radiosurgery System implementation. Our multidisciplinary team, through a discussion on ‘what can go wrong’, identified failure modes. Risks were prioritised based on severity, likelihood and detectability giving them a Risk Priority Number (RPN). The team proposed mitigation strategies, deploying controls to enhance safety and reliability.

Dynamics of tumor evolution after Gammaknife radiosurgery for sporadic vestibular schwannoma: defining volumetric patterns characterizing individual trajectory.

Definition of tumor control and treatment failure after Gammaknife radiosurgery (GKRS) for vestibular schwannoma (VS) is still debated. The lack of knowledge on the dynamics of tumor evolution can lead to misinterpretation and subsequent inappropriate second treatment. The aim of this study was to evaluate the post-GKRS dynamics of evolution of tumor volume, and characterize volumetric patterns. We included patients with sporadic VS treated by GKRS with an MRI follow-up of minimum 3 years. A clustering in 2 steps was performed: definition of the patterns of evolution based on a subset of patients with the most comprehensive follow-up, then assignment of the remaining patients on a best fit basis. The minimum length of follow-up was assessed by measuring the consistency of the clusters over time (Adjusted Rand Index and Normalized Mutual Information). An analysis of the discriminant variables was finally performed. 1,607 patients were included (median follow-up: 67 months). Five patterns were defined with one pattern gathering almost all cases of treatment failure. The clustering at 5 years afforded the highest consistency with long-term follow-up. Discriminant variables for clusters were: sex, initial symptoms, delay of diagnosis, Koos grading, fundus invasion, and number of isocenters. The definition of these robust distinct patterns is likely to help tremendously the physicians to distinguish tumor control from potential failure. We advocate for no retreatment decision before 5 years post-GKRS. Further investigations are required to decide if the dynamics of evolution can be predicted at GKRS on an individual basis.

THE ROLE OF THE ENTREPRENEUR IN MODERN AGRIBUSINESS AND CHOOSING THE MOST FAVORABLE ENTREPRENEURIAL STRATEGY

Modern agribusiness, as a permanent process of reallocating financial, physical, and human resources from areas of societal reproduction where they are underutilized and inefficiently used to areas where they will yield much greater effects, always demands a positive optimistic attitude. The entrepreneur, in line with the analyses of self-development management philosophy, must impartially and objectively assess their strengths, weaknesses, available opportunities and threats in their operations. It is crucial to believe in one's abilities and the products or services offered. Agricultural entrepreneurship, as part of production, is characterized by low mobility of assets over time and space. Land as a production factor also has a limiting effect on agricultural entrepreneurial activity. The natural factor, along with the biological nature of the means and objects of work, is a precondition for the conditioned nature of agribusiness operations. The entrepreneur in agribusiness is a person who possesses creativity, and can lead and handle all challenges and risks in the work process. They are brave enough to take the risk of turning their idea into reality. Entrepreneurs are one of the most renowned untapped sources in the world of creativity and growth, making their role in modern agribusiness increasingly relevant in both developed and transition countries. By utilizing their entrepreneurial skills and qualities, the entrepreneurs should apply the best entrepreneurial strategies for the benefit of the company, not allowing uncertainty and potential failure to discourage future steps. On the contrary, they should see opportunities, not problems, in challenges and risks.

Material Composition and Implantation Site Affect in vivo Device Degradation Rate.

Successful tissue engineering requires biomedical devices that initially stabilize wounds, then degrade as tissue is regenerated. However, the material degradation rates reported in literature are often conflicting. Incorporation of in situ monitoring functionality into implanted devices would allow real time assessment of degradation and potential failure. This necessitates introduction of contrast agent as most biomedical devices are composed of polymeric materials with no inherent contrast in medical imaging modalities. In the present study, computed tomography (CT)-visible radiopaque composites were created by adding 5-20wt% tantalum oxide (TaO x ) nanoparticles into polymers with distinct degradation profiles: polycaprolactone (PCL), poly(lactide-co-glycolide) (PLGA) 85:15 and PLGA 50:50, representing slow, medium and fast degrading materials respectively. Radiopaque phantoms, mimicking porous tissue engineering devices, were implanted into mice intramuscularly or intraperitoneally, and monitored via CT over 20 weeks. Changes in phantom volume, including collapse and swelling, were visualized over time. Phantom degradation profile was determined by polymer matrix, regardless of nanoparticle addition and foreign body response was dictated by the implant site. In addition, degradation kinetics were significantly affected in mid-degrading materials, transitioning from linear degradation intramuscularly to exponential degradation intraperitoneally, due to differences in inflammatory responses and fluid flow. Nanoparticle excretion from degraded phantoms lagged behind polymer, and future studies will modulate nanoparticle clearance. Utilizing in situ monitoring, this study seeks to unify literature and facilitate better tissue engineering devices, by highlighting the relative effect of composition and implant site on important materials properties.

Investigation of the Effects Caused by Current Interruption Devices of Lithium Cells at High Overvoltages

A faulty voltage measurement can lead to the overcharging of a Li-Ion cell, resulting in gas formation and heating inside the cell, which can trigger thermal runaway. To mitigate this risk, cylindrical cells are equipped with a Current Interrupt Device (CID), which functions as a pressure relief valve, disconnecting the electrical circuit within the cell when internal pressure rises. However, this disconnection causes the cell to suddenly become highly resistant, posing a significant issue in series-connected cells. In such configurations, a portion or even the entire system voltage may drop across the disconnected cell, substantially increasing the likelihood of an electric arc. This arc could ignite any escaping flammable gases, leading to catastrophic failures. In a series of tests conducted on three different cell chemistries—NMC (Nickel Manganese Cobalt), NCA (Nickel Cobalt Aluminum), and LFP (Lithium Iron Phosphate)—it was found that the safe operation of the CID cannot be guaranteed for system voltages exceeding 120 V. Although comparative tests at double the nominal cell voltage did not exhibit the same behavior, these findings suggest that current safety standards, which recommend testing at double the nominal voltage, may not adequately address the risks involved. The tests further revealed that series connections of cells with CIDs are inherently dangerous, as, in the worst-case scenario, the entire system voltage can be concentrated across a single cell, leading to potential system failure.

A data-driven underground gas storage production system string failure prediction model for time-varying reliability analysis

In underground gas storage (UGS) systems, the production tubing string system undergoes complex thermo-mechanical loading conditions, leading to fatigue damage accumulation and potential failure risks. To accurately assess the fatigue reliability and mitigate risks, it is crucial to develop a data-driven physics-informed uncertainty modeling approach that captures the coupled thermo-mechanical effects and accounts for various uncertainties. This study proposes a data-driven physics-informed modeling method based on thermo-mechanical coupling for fatigue reliability assessment and risk mitigation in critical UGS systems. A quantitative analysis method is introduced, which relies on small-scale multi-stage loading experiments and combines the stochastic degradation process with time-series reliability theory. To analyze the fatigue life of joints under complex loading conditions, a modified S-N curve model is developed. This model takes into account the influences of the dimension coefficient, stress concentration factors, surface finish coefficients, and thermal load. It considers the uncertainties associated with different temperature loads and surface finish on fatigue life. By integrating physics-informed modeling, uncertainty quantification, and fatigue damage accumulation analysis, this approach provides a comprehensive framework for fatigue reliability assessment and risk mitigation in critical UGS systems. It enables informed decision-making for safe and reliable operations, as well as optimized maintenance strategies, ultimately enhancing the overall system performance and mitigating potential failures.