Prevention Of Failure Research Articles

Numerical study on dynamic response of double drive shafts of dual-coupled axial piston pump under various load ratios

The dynamic response of the double drive shafts, as the core component of the dual-coupled axial piston pump, affects the stability and reliability of the pump. The double drive shafts bear complex dynamic loads accompanied by a certain degree of vibration during the transmission of power, which results in abnormal contact between the double drive shafts and the bearing, as well as between it and the splined coupler, leading to increased wear. In this study, addressing the fatigue failure issue of the double drive shafts in the dual-coupled axial piston pump, a rigid-flexible coupling dynamic model of the dual-coupled axial piston pump is established. The stress and strain conditions of the double drive shafts under various load ratios are analysed, along with the dynamic characteristics between the drive shaft and the barrel block, as well as between it and the spline sleeve in both time and frequency domains, and the variation pattern of the vibration displacement of the double drive shafts. Additionally, the fatigue damage of the double drive shafts under various load ratios is analysed using the fatigue cumulative damage model. The results show that under various load ratios, obvious stress and strain concentrations occur in the neck near the bearing connection and the spline sleeve connection. The safety factor of the double drive shafts is higher than 1.5 in the obtained safety load range. The contact force between the double drive shafts and the barrel block is mainly influenced by the reciprocating motion of the pistons, while the contact force between the double drive shafts and the spline sleeve is affected by the synergistic effect of the reciprocating motion of the piston and the rotation of the drive shaft. Furthermore, the increase in the rear pump load pressure results in irregular trends in the vibration displacement of the double drive shafts, as well as superimposed fatigue damage to the front shaft. This study provides a reference for fatigue failure prevention, noise reduction, and vibration damping of the double drive shafts under various load ratios.

Liver venous deprivation in the prevention of post-resection hepatic failure

The paper presents a clinical observation of successful X-ray endovascular intervention performed in a patient with colorectal cancer metastases in the liver in order to prevent post-resection hepatic failure. Traditional embolization of the right branch of the portal vein was supplemented by occlusion of the right hepatic vein. Venous deprivation enabled a rapid growth of the future liver remnant to be achieved as indicated by CT volumetry results and SPECT-CT data that showed a significant increase in hepatic clearance. The first results demonstrate that liver venous deprivation is to be investigated further in clinical practice.

A cascading failure propagation model for a network with a node emergency recovery function

Cascading failures are ubiquitous in physical networks such as power grids and water networks and have become a trending research topic in the field of complex networks. In real cases, once a node in a network fails, the entire system can completely collapse, causing considerable economic losses and casualties. Implementing an appropriate node repair strategy can effectively prevent system crashes in complex networks due to cascading failure. This paper presents a network cascading failure propagation model with a node emergency recovery function. Numerical simulations with typical case scenarios are conducted to analyze network cascading failure when the nodes have an emergency recovery probability. The results of the case study show that incorporating an emergency recovery probability for nodes can reduce the maximum failure probability of nodes in a network and reduce the risk of network failure. As the recovery probability increases, the network failure risk probability gradually decreases. The proposed network cascading failure propagation model with a node emergency recovery function can accurately reflect the dynamic behavior of complex network cascades and provide a reference for the control and prevention of cascading failures in actual networks.

A bearing fault diagnosis approach based on an improved neural network combined with transfer learning

Abstract In modern industrial systems, bearing failures account for 30%–40% of industrial machinery faults. Traditional convolutional neural network suffers from gradient vanishing and overfitting, resulting in a poor diagnostic accuracy. To address the issues, a new bearing fault diagnosis approach was proposed based on an improved AlexNet neural network combined with transfer learning. After decomposition and noise-reduction, reconstructed vibration signals were transformed into 2D images, then input into the improved AlexNet for training and follow-up transfer learning. Program auto-tuning and image-enhancing techniques were employed to increase the diagnostic accuracy in this study. The approach was verified with the datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and the Association for Mechanical Failure Prevention Technology (MFPT). The results showed that the diagnostic accuracies by normal learning were more than 97% for CWRU and JNU datasets, and 100% for MFPT dataset. After transfer learning, the accuracies all reached above 99.5%. The proposed approach was demonstrated to be able to effectively diagnose the bearing faults.

WITHDRAWN: Research on pipeline corrosion prediction based on RF-PSO-BP Algorithm

The transportation of oil and gas relies heavily on pipelines, and pipeline corrosion is a major factor affecting pipeline reliability. It can lead to pipeline failure and other damage. Pipeline corrosion prediction is of great importance for pipeline integrity management and failure prevention. A physical law intervening RF(Random Forest)-PSO(Particle Swarm Optimization)-BP(Back Propagation Neural Network) algorithm is proposed to predict pipeline corrosion rate. The DeWaard physical model is first fitted to the data, and the fitted physical model predicts pipeline corrosion to form a new feature, which is then combined with the features extracted by the RF algorithm to form a new feature that is used as an input metric for the data-driven model. Secondly, the already constructed features are divided into training set and testing set. The training set is used to train the PSO-BP model, and the test set is used to test the accuracy of the model. The model is evaluated using the metrics such as MAE, MBE, MAPE, and R2. To show the superiority of the proposed model, the proposed model is compared with other models. The results show that the proposed model has some advantages in both feature analysis and corrosion prediction, and it has some theoretical guidance for pipeline protection.

Effect on cardiovascular outcome of sodium-glucose co-transporter-2 (SGLT2) inhibitors among patients with cancer treated with anthracycline: A systematic review and meta-analysis.

e24001 Background: Sodium-glucose-co-transporter-2 (SGLT2) inhibitors have shown benefit in reducing cardiovascular outcomes in diabetes patients. Anthracycline therapy is associated with a risk of cardiomyopathy. However, the impact of SGLT2 inhibitors in the prevention of cardiomyopathy and heart failure in cancer patients undergoing anthracycline treatment remains unclear. Thus, we conducted a systematic review and meta-analysis to explore the effect of the prevention of cardiovascular outcomes in patients with cancer and diabetes who had received anthracycline therapy. Methods: We systematically reviewed Medline and EMBASE databases from inception to January 2024 for studies focusing on cancer patients with a history of anthracycline therapy. Eligible studies had to report relative risk (RR) with 95% confidence intervals for the clinical endpoints of mortality outcomes and the risk of HF exacerbation, comparing cohorts with and without SGLT2 inhibitor use. Results: Our study included 4 retrospective cohort studies in the meta-analysis (n = 6,708, 24% received SGLT2). There was significantly lower all-cause mortality in the SGLT2 inhibitors group (pooled RR of 0.52, 95% CI 0.35-0.77, I2 64%). However, there were no differences in the risk of heart failure exacerbation (pooled RR of 0.67, 95% CI 0.39-1.14, I2 17%). Conclusions: Our study found that anthracycline-treated cancer patients using SGLT2 inhibitors experienced lower all-cause mortality compared to the control group. A randomized clinical trial is necessary to further elucidate these findings.

MKK4 inhibitor: the hope for liver failure prevention and potential small liver graft transplantation

MKK4 inhibitor: the hope for liver failure prevention and potential small liver graft transplantation

Proactive System Maintenance: Machine Learning Models for Predicting and Preventing Potential System Failures

Abstract: In modern industrial systems, the prevention of failures and downtime is of paramount importance for ensuring efficiency and productivity. Proactive system maintenance approaches leverage machine learning (ML) models to predict potential failures before they occur, enabling pre-emptive actions to be taken [1]. In this paper, we present a comprehensive review of existing research on proactive system maintenance, focusing on the development and application of ML algorithms for fault prediction and prevention. We discuss various machine learning techniques, data sources, feature engineering methods, and evaluation metrics employed in this domain [8]. Furthermore, we propose novel algorithms and strategies for enhancing the effectiveness of proactive maintenance systems. Through experimentation and case studies, we demonstrate the feasibility and benefits of utilizing machine learning for proactive maintenance in diverse industrial settings.

Prospects for the Application of Nephroprotectors of Plant Origin Based on Lespedeza bicolor

Chronic kidney disease is a significant risk factor for the development of various non communicable diseases, including cardiovascular diseases. Every year there is a tendency to increase the number of patients with urinary system diseases. Chronic kidney disease is a global medical and social problem that leads to early disability of the working population. Today, scientists are focusing considerable attention on the search for new drugs with hypo azotheamine activity, as it is important to slow down the progress of chronic kidney disease. Herbal medicines are promising for the prevention and treatment of such pathologies. Plants with proven significant hypo-azotemic activity and safety include representatives of the genus Lespedeza, in particular, Lespedeza capitata and Lespedeza bicolor. The objective: to review the literature on the chemical composition and biological action of Lespedeza capitata, Lespedeza bicolor and the prospects for the use of a domestic medicinal product based on the herb Lespedeza bicolor as a hypoazotemic agent. Results. According to the literature, the chemical composition of Lespedeza capitata and Lespedeza bicolor differs slightly and is represented by flavonoids, procyanidins, hydroxycinnamic acids, etc. The therapeutic effect of these plants is associated primarily with the glycosides quercetin, luteolin (isorientin, vitexin) and kaempferol (robinin). It has been found that the complex of biologically active substances included in the preparation has nephro- and hepatoprotective, hypo-azotemic, antidiabetic, anti-inflammatory and diuretic effects and is promising in the treatment of many diseases of the urinary system. Conclusions. Literature sources indicate that the extracts from the shoots of Lespedeza capitata and from the herb Lespedeza bicolor are similar in chemical composition and pharmacological properties. Ethanol extracts based on Lespedeza capitata and Lespedeza bicolor have a long history of use and are characterised by proven hypo-azotemic, nephroprotective and anti-inflammatory effects. They are successfully used for the prevention and treatment of chronic renal failure, acute and chronic nephritis and extrarenal azotemia. The complex of biologically active substances contained in herbal remedies helps to increase renal filtration, reduce azotemia, increase diuresis, enhance sodium excretion and, to a lesser extent, potassium excretion.

Overview of Advanced Liquid Process System: Circulation of tritiated water in oceanic environment

The recent Japanese disposal of Advanced Liquid Processing System (ALPS)-treated water is controversial. ALPS does not filter tritium. The objectives of this paper are (a) to examine how anthropogenic tritium triggers biological health impacts and (b) to review the circulation of tritiated water into the oceanic territory of the Republic of Korea. I reviewed the possibility of the bioaccumulation of tritium in the oceanic environment and its overall health impact. To be of significant status, they require complex conditions. I also reviewed the simulation of the possible path that disposed pollutants will take from Fukushima. The simulation was then compared with the measured level of radionuclides around the Republic of Korea's oceanic territory by year. The data is divided into three parts (east, west, and south) and analyzed to check if the trends show any patterns in the level of tritium in the Republic of Korea. The levels checked in three areas showed a partial uniformity, which may be evidence that radionuclides entered the oceanic territory of the Republic of Korea. However, the level of tritium was extremely low compared to the regulations set by the World Health Organization and the Republic of Korea. Thus, the disposal of tritium will be less likely to affect the natural environment. Nevertheless, it is recommended to monitor disposed tritium by a third party, plan for compensation, and conduct intense research on the circulation of tritium to support the prevention of sudden failure of the emission system.

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Motor fault diagnosis is an important task in the operational monitoring of electrical machines in manufacturing. This study proposes an effective bearing fault diagnosis model for electrical machinery based on machine learning techniques. The proposed model is a combination of three processes: feature extraction of signals collected from the motor based on multi-resolution analysis, fast Fourier transform, and envelope analysis. Next, redundant or irrelevant features are removed using the feature selection technique. A binary salps swarm algorithm combined with an extended repository is the proposed method to remove unnecessary features. As a result, an optimal feature subset is obtained to improve the performance of the classification model. Finally, two classifiers, k-nearest neighbor and support vector machine, are used to classify the fault of the electric motor. There are four input datasets used to evaluate the model performance, and UCI is the benchmark dataset to verify the effectiveness of the proposed feature selection technique. The remaining three datasets include the bearing dataset collected from experiments, with an average classification accuracy of 99.9%, as well as Case Western Reserve University (CWRU) and Machinery Failure Prevention Technology (MFPT), which are public datasets with average classification accuracies of 99.6% and 98.98%, respectively. The experimental results show that this method is more effective in diagnosing bearing faults than other traditional methods and prove its robustness.

MiR-106a targets ATG7 to inhibit autophagy and angiogenesis after myocardial infarction.

Myocardial infarction (MI) is an acute condition in which the heart muscle dies due to the lack of blood supply. Previous research has suggested that autophagy and angiogenesis play vital roles in the prevention of heart failure after MI, and miR-106a is considered to be an important regulatory factor in MI. But the specific mechanism remains unknown. In this study, using cultured venous endothelial cells and a rat model of MI, we aimed to identify the potential target genes of miR-106a and discover the mechanisms of inhibiting autophagy and angiogenesis. We first explored the biological functions of miR-106a on autophagy and angiogenesis on endothelial cells. Then we identified ATG7, which was the downstream target gene of miR-106a. The expression of miR-106a and ATG7 was investigated in the rat model of MI. We found that miR-106a inhibits the proliferation, cell cycle, autophagy and angiogenesis, but promoted the apoptosis of vein endothelial cells. Moreover, ATG7 was identified as the target of miR-106a, and ATG7 rescued the inhibition of autophagy and angiogenesis by miR-106a. The expression of miR-106a in the rat model of MI was decreased but the expression of ATG7 was increased in the infarction areas. Our results indicate that miR-106a may inhibit autophagy and angiogenesis by targeting ATG7. This mechanism may be a potential therapeutic treatment for MI.

Musculoskeletal Failure.

Osteoarthritis, osteoporosis, and sarcopenia are prevalent musculoskeletal disorders that significantly impact the aging population's health and quality of life. Osteoarthritis, characterized by joint inflammation, leads to pain, stiffness, and reduced mobility. Osteoporosis, a condition marked by bone density loss, increases fracture susceptibility, especially in postmenopausal women and older adults. Sarcopenia, the age-related loss of muscle mass and function, contributes to frailty and an increased risk of falls. Combined, osteoarthritis, osteoporosis and sarcopenia constitute "Musculoskeletal Failure." These 3 conditions share common risk factors like aging, genetics, and hormonal changes, as well as unhealthy lifestyle behaviors resulting in systemic chronic inflammation. Healthy lifestyle behaviors, including regular physical activity and a nutritious diet across the lifespan play a crucial role in the prevention and management of musculoskeletal failure. Awareness of the relationship between lifestyle behaviors, systemic chronic inflammation and the development and progression of these 3 common conditions is a key step in prevention, early detection and are essential for addressing the complex interplay of these musculoskeletal disorders. As the global population ages, understanding and effectively preventing and managing osteoarthritis, osteoporosis, and sarcopenia become paramount for promoting healthy aging and mitigating the societal and economic burden associated with these conditions.

Prevalence of free flap failure in mandibular osteoradionecrosis reconstruction: a systematic review and meta-analysis

Our study aimed to estimate the prevalence of total free flap failure following free flap reconstruction for mandibular osteoradionecrosis (mORN) and assess the impact of potential moderators on this outcome. A comprehensive systematic literature search was independently conducted by two reviewers using the Medline, Scopus, Web of Science and Cochrane Library databases. Quality assessment of the selected studies was performed, and prevalence estimates with 95% confidence intervals (CI) were calculated. Outlier and influential analyses were conducted, and meta-regression analyses was employed to investigate the effects of continuous variables on the estimated prevalence. Ultimately, forty-six eligible studies (involving 1292 participants and 1344 free flaps) were included in our meta-analysis. The findings of our study revealed a prevalence of 3.1% (95% CI 1.3–5.4%) for total free flap failure after reconstruction for mORN. No study was identified as critically influential, and meta-regression analysis did not pinpoint any potential sources of heterogeneity. These findings provide valuable insights for researchers and serve as a foundation for future investigations into the management of mandibular osteoradionecrosis and the prevention of free flap failure in this context.

Effectiveness of bariatric metabolic surgery versus glucagon-like peptide-1 receptor agonists for prevention of congestive heart failure.

Comparative evidence for the effects of bariatric metabolic surgery (BMS) and glucagon-like peptide-1 receptor agonists (GLP-1RA) on cardiovascular outcomes is limited. Here, in an observational, retrospective cohort study, we compared the incidence of congestive heart failure (CHF) in adults living with obesity and diabetes without history of CHF (primary CHF) treated with BMS versus GLP-1RA. The population cohort comprised members of Clalit Health Services with no prior history of ischemic heart disease, ischemic stroke or CHF. During the time period of 2008-2021, patients who underwent their first BMS were matched 1:1 with patients who initiated treatment with GLP-1RA, based on clinical characteristics. The study included 2,205 matched pairs of patients (64.5% female), followed for a median of 6.6 years and up to 12 years. Primary incidence of CHF occurred in 26 (1.2%) patients treated with BMS and in 90 patients treated with GLP-1RA (4.1%) (adjusted hazard ratio 0.43, 95% confidence interval 0.27-0.68). Further adjustment for weight reduction did not significantly diminish this association (hazard ratio adjusted for weight reduction 0.48, 95% confidence interval 0.28-0.82), indicating that the differential effect was not mediated through the relative advantage of BMS in maximal weight reduction. In this study, BMS was associated with a stronger reduction in primary incidence of CHF compared with treatment with GLP-1RA. With the increasing use of highly potent next-generation GLP-1RAs, further comparative long-term studies are warranted.

Regulation of vascular remodeling by immune microenvironment after the establishment of autologous arteriovenous fistula in ESRD patients.

Autogenous arteriovenous fistula (AVF) is the preferred dialysis access for receiving hemodialysis treatment in end-stage renal disease patients. After AVF is established, vascular remodeling occurs in order to adapt to hemodynamic changes. Uremia toxins, surgical injury, blood flow changes and other factors can induce inflammatory response, immune microenvironment changes, and play an important role in the maintenance of AVF vascular remodeling. This process involves the infiltration of pro-inflammatory and anti-inflammatory immune cells and the secretion of cytokines. Pro-inflammatory and anti-inflammatory immune cells include neutrophil (NEUT), dendritic cell (DC), T lymphocyte, macrophage (Mφ), etc. This article reviews the latest research progress and focuses on the role of immune microenvironment changes in vascular remodeling of AVF, in order to provide a new theoretical basis for the prevention and treatment of AVF failure.

Deep learning as a powerful tool in digital photoelasticity: Developments, challenges, and implementation

Stress field evaluation, through fringe order maps, has always been of great importance in various engineering domains, providing essential insights into the mechanical response of loaded structures and enabling the prevention of potential failures, thus ensuring the safety and reliability of critical components. In recent years, the advent of new algorithms has ushered in a transformative era, particularly through computational techniques such as machine learning and, in particular, deep learning. These advanced approaches have proven instrumental in optimizing fringe demodulation processes, providing more accurate predictions, and uncovering complex patterns within structural behavior. This paper reports a careful review of papers and articles discussing the application of deep learning to photoelasticity studies. It explores the impact and applicability of deep learning in the field of digital photoelasticity, highlighting its benefits and advancements. In addition, the challenges associated with the implementation of this technique are examined to provide a critical insight. The paper also discusses experimental results and possible suggestions for future work in this new paradigm.

Possible Role of Gut Microbiota Alterations in Myocardial Fibrosis and Burden of Heart Failure in Hypertensive Heart Disease.

Epidemiological studies have revealed that hypertensive heart disease is a major risk factor for heart failure, and its heart failure burden is growing rapidly. The need to act in the face of this threat requires first an understanding of the multifactorial origin of hypertensive heart disease and second an exploration of new mechanistic pathways involved in myocardial alterations critically involved in cardiac dysfunction and failure (eg, myocardial interstitial fibrosis). Increasing evidence shows that alterations of gut microbiota composition and function (ie, dysbiosis) leading to changes in microbiota-derived metabolites and impairment of the gut barrier and immune functions may be involved in blood pressure elevation and hypertensive organ damage. In this review, we highlight recent advances in the potential contribution of gut microbiota alterations to myocardial interstitial fibrosis in hypertensive heart disease through blood pressure-dependent and blood pressure-independent mechanisms. Achievements in this field should open a new path for more comprehensive treatment of myocardial interstitial fibrosis in hypertensive heart disease and, thus, for the prevention of heart failure.

Mitigation of Soil Erosion and Enhancement of Slope Stability through the Utilization of Lignin Biopolymer.

Human activities have had a profound impact on the environment, particularly in relation to surface erosion and landslides. These processes, which are natural phenomena, have been exacerbated by human actions, leading to detrimental consequences for ecosystems, communities, and the overall health of the planet. The use of lignin (LIG) as a biopolymer soil additive material is regarded as an eco-friendly solution against soil erosion and slope failure which holds immense promise. However, significant research gaps currently hinder a comprehensive understanding of its mechanisms and effectiveness. Experimental studies offer a robust platform to address these gaps by providing controlled conditions for assessing soil stability, exploring mechanisms, and evaluating adaptability. Bridging these research gaps will contribute to the development of innovative and sustainable strategies for mitigating soil erosion and preventing slope failure, thereby promoting environmental resilience and resource conservation. This study aimed to investigate the effect of the LIG biopolymer on mitigation of soil erosion, slope failure and the enhancement of soil strength by conducting laboratory tests (UU triaxial, unconfined compressive strength (UCS), and soaking) as well as flume experiments under uniform rainfall events. The alterations in the engineering characteristics and erosion resistance of silty soil mixed with a LIG additive at concentrations of 1% and 3.0% by weight have been examined. The results show that the LIG-treated samples demonstrated an enhanced resistance to surface erosion and an enhanced prevention of slope failure, as well as improved shear stress, cohesion, stiffness, and resistance to water infiltration.

Leakage diagnosis of natural gas pipeline based on multi-source heterogeneous information fusion

Due to long-term service, natural gas pipelines are prone to corrosion, resulting in pipeline leakage failure and environmental pollution. However, it is challenging to provide an accurate leakage diagnosis for existing single-sensor detection techniques. In this paper, we propose a multi-source heterogeneous information fusion method for the complementary fusion of laser optical sensing and weak magnetic technologies. Firstly, the laser and weak magnetic signals are converted into two-dimensional images using continuous wavelet transform (CWT) and then fused in data-level. Secondly, deep reinforcement learning (DRL) combines the perception ability of deep learning and the decision-making ability of reinforcement learning. Consequently, the deep Q-network (DQN) method is proposed as a novel method for leakage diagnosis of natural gas pipelines. Then, an improved capsule network based on dense block is designed for feature enhancement. Finally, experimental results verify the effectiveness of the proposed method in recognizing the formed leakage and potential leakage. Moreover, the results demonstrate that the proposed method outperforms single-sensor-based and state-of-the-art methods in terms of diagnostic accuracy and cross-domain transfer tasks. This will provide a theoretical basis for pipeline leakage failure prevention and maintenance decision-making.