Maintenance Strategies Research Articles

A REVIEW ON TOTAL PRODUCTIVE MAINTENANCE (TPM)

Total Productive Maintenance (TPM) is transforming manufacturing landscapes. This review investigates its impact, revealing how TPM fosters a culture of continuous improvement through employee involvement and proactive maintenance strategies. By analyzing 30 research papers, it unveils the potent link between TPM and increased productivity alongside reduced waste. Highlighting Overall Equipment Effectiveness (OEE) as a crucial metric, the review emphasizes the importance of 5S as a foundational step for successful TPM implementation. With global adoption surging, TPM is solidifying its position as a world-class strategy for achieving peak manufacturing performance." Keywords: Total Productive Maintenance (TPM), OEE, Improve Productivity, 5S.

Cutaneous Squamous Cell Carcinoma: An Updated Review

Representing the second most common skin cancer, the incidence and disease burden of cutaneous squamous cell carcinoma (cSCC) continues to increase. Surgical excision of the primary site effectively cures the majority of cSCC cases. However, an aggressive subset of cSCC persists with clinicopathological features that are indicative of higher recurrence, metastasis, and mortality risks. Acceleration of these features is driven by a combination of genetic and environmental factors. The past several years have seen remarkable progress in shaping the treatment landscape for advanced cSCC. Risk stratification and clinical management is a top priority. This review provides an overview of the current perspectives on cSCC with a focus on staging, treatment, and maintenance strategies, along with future research directions.

Optimal predictive selective maintenance for fleets of mission-oriented systems

ABSTRACT In many settings, fleets of assets must perform series of missions with in-between finite breaks. For such fleets, a widely used maintenance strategy is the fleet selective maintenance (FSM). Under resource constraints, the FSM problem selects an optimal subset of feasible maintenance actions to be performed on a subset of components to minimise the maintenance cost while ensuring high system reliability during the upcoming mission. The majority of extant FSMP models are focussed on traditional physics-based reliability models. With recent advances in Machine Learning (ML) and Deep Learning (DL) algorithms, data-driven methods have shown accuracy in predicting remaining useful life (RUL). This paper proposes a predictive FSM strategy for fleets of complex and large multicomponent systems. It relies on a concurrent ML/DL and optimisation approach where a clustering algorithm is used to determine the health states of components and a probabilistic RUL prognostics model is used for component reliability assessment. An optimisation model is developed to solve the predictive FSM problem to ensure high reliability of all systems within the fleet. An efficient two-phase solution approach is developed to solve this complex optimisation problem. Numerical experiments show the validity of the approach and highlight the improved maintenance plans achieved.

Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time–frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.

Comparative Analysis of the Medical Equipment Management Process in Colombo Gampaha and Kaluthara Districts

Comparative Analysis of the Medical Equipment Management Process in Colombo Gampaha and Kaluthara Districts

Optimal Preventive Maintenance Policy for Equipment Rented under Free Leasing as a Contributor to Sustainable Development

Leasing has proven to be a business model that is perfectly suited to the circular economy. It significantly contributes to sustainable development by enabling the reuse of machinery and equipment after each lease period and by including preventive maintenance and overhauls within and between lease terms. This helps to extend the life cycle of equipment, promote value recovery, and reduce waste. This paper examines an imperfect preventive maintenance (PM) strategy applied to equipment rented under the terms of “free leasing”. In free leasing, the lessor makes the equipment available to the customer for a specified period of time without charging rent. In return, the customer is required to purchase the equipment’s consumables exclusively from the lessor. The lessor is also responsible for the maintenance of the equipment at the customer’s premises. The greater the quantity of consumables used by the customer, the more the equipment will deteriorate. Consequently, the lessor must be able to determine the most effective approach to preventive maintenance, ensuring that it aligns with the customer’s planned usage rate while maximizing profit. This work proposes a PM strategy to be adopted by the lessor during the free lease period. This strategy involves the performance of imperfect PM actions just before the start of the lease period and then periodically. Different packages of preventive actions can be applied each time, with each package having a different cost depending on the level of effectiveness in terms of rejuvenating the equipment. Minimal repairs are performed in the event of equipment failure. The decision variables are the PM period to be adopted and the maintenance efficiency level to be chosen for each preventive intervention. The objective is to determine, for a given customer with an estimated consumption rate profile of consumables, the optimal values of these decision variables so that the lessor maximizes their profit. A mathematical model is developed to express the lessor’s average profit over each lease period. A solution procedure is developed for small instances of the problem, and an Artificial Bee Colony algorithm is implemented for larger instances. A numerical example and a sensitivity analysis are presented.

Technological innovations and optimized work methods in subsea maintenance and production

Subsea maintenance and production represent critical aspects of offshore operations, vital for sustaining energy production and ensuring operational efficiency. However, these endeavors face numerous challenges, including the complexities of deepwater environments, harsh weather conditions, and the high costs associated with traditional maintenance methods. To address these challenges, this paper explores the integration of technological innovations and optimized work methods in subsea operations. Technological innovations play a pivotal role in revolutionizing subsea maintenance and production. Remote monitoring and control systems enable real-time data collection and decision-making, enhancing operational visibility and efficiency. Autonomous underwater vehicles (AUVs) offer the capability to conduct inspections and repairs in remote and hazardous environments, reducing human intervention and associated risks. Robotics and automation further streamline maintenance tasks, improving accuracy and reducing downtime. Advanced materials and coatings enhance equipment durability and corrosion resistance, prolonging asset lifecycles and reducing maintenance requirements. In parallel, optimized work methods offer strategic approaches to enhance subsea operations' effectiveness. Predictive maintenance strategies leverage data analytics and machine learning to anticipate equipment failures, enabling proactive interventions and minimizing downtime. Condition-based monitoring facilitates real-time assessment of asset health, enabling timely maintenance interventions and cost savings. Integrated asset management systems provide holistic insights into asset performance and facilitate informed decision-making. Lean operations and continuous improvement methodologies further optimize workflows, driving operational excellence and cost efficiency. Through case studies and industry examples, this paper highlights the successful implementation of technological innovations and optimized work methods in subsea maintenance and production. Furthermore, it explores future trends, regulatory considerations, and the importance of industry collaborations in shaping the future of subsea operations. Ultimately, the integration of these approaches offers a pathway towards enhanced operational efficiency, reduced costs, and sustainable subsea operations. Keywords: Technological Innovations, Optimized Work Methods, Subsea Maintenance and Production.

Clinico-biological factors predicting the benefit of the LV5FU2 maintenance strategy as a first-line therapy in patients with metastatic pancreatic cancer

Abstract Introduction Predictive markers of LV5FU2 maintenance benefit after first-line induction with FOLFIRINOX in patients with metastatic pancreatic cancer are necessary to select patients who will not be harmed by this strategy. Patients and Methods We focused on patients who received 12 cycles of FOLFIRINOX (arm A, N = 88) or 8 cycles of FOLFIRINOX followed by LV5FU2 maintenance in controlled patients (arm B, N = 91) from the PRODIGE-35 trial. Prognostic factors and predictors of efficiency were identified by using Cox regression. Median progression-free survival (PFS), overall survival (OS), and time to deterioration of quality of life (TTD-QoL) were evaluated. Results Poor independent prognostic factors were primary tumor in place, age <65 years and the presence of liver metastases for PFS, a baseline neutrophil/lymphocyte ratio (NLR) ≥5 and CA19.9 ≥500 UI/L for OS, independent of the treatment arm. Patients with one metastatic site had a longer PFS in arm A, whereas patients with ≥2 metastatic sites had a longer PFS in arm B. We also identified predictors of OS and TTD-QoL in arm B but these differences were not statistically significant. Conclusion Except for patients with one metastatic site who benefited more from 12 cycles of FOLFIRINOX, a maintenance strategy with LV5FU2 should be widely offered to mPC patients whose survival and QoL are preserved after 4 months of FOLFIRINOX. (ClinicalTrials.gov: NCT02352337).

Understanding desiccation tolerance and sex-specific responses in Bryum argenteum: insights from phenological phases and physiological analyses.

Desiccation tolerance (DT) is crucial for survival in arid environments, where organisms develop strategies in reproduction, maintenance, and defense to cope with water scarcity. Therefore, investigating the relationship between reproduction and DT is essential to understand the ecology and adaptive strategies of species. This study explores the connection between the development of male and female gametangia in the moss Bryum argenteum and the decrease in DT during the progression of phenological phases in gametangia and protonema. Samples collected from a dry tropical forest in Brazil were cultivated, cloned, and subjected to desiccation. Subsequently, the physiological parameters of shoots and protonemata were analysed. Shoot and protonema regeneration were monitored for 28 days after the physiological analyses. Both phases were subjected to control and desiccation treatments. Significant effects of desiccation and sex on the physiological parameters and regeneration capacity of shoots and protonemata were found. Male shoots generally exhibited lower values in Fv/Fm, and ϕPSII, while females demonstrated higher values and better recovery after desiccation. Protonemata also showed variation in Fv/Fm over time and sex, with no significant differences in ϕPSII between them. Desiccated male shoots had higher mortality rates and produced fewer new shoots. Regarding the females, the regeneration patterns varied between the desiccation-exposed groups and the control, with decreased shoot production, and some protonemata growing into filaments without forming shoots. These findings improve our understanding of bryophytes ecological responses to desiccation stress and provide insights into their adaptive strategies in challenging environments, such as the possible rarity of males in dioicous mosses populations.

An I–V characteristic reconstruction-based partial shading diagnosis and quantitative evaluation for photovoltaic strings

The partial shading condition (PSC) is the most common abnormality occurred in photovoltaic (PV) systems. Accurate quantitative evaluation of the shaded area and the severity of the shading is of potential importance in optimizing the maintenance strategy for PV systems. In this paper, we propose a PSC diagnosis and quantitative evaluation method by analyzing the measured string current–voltage (I–V) characteristic obtained from the PV inverter with the I–V scanning function, which includes pre-diagnosis of the system abnormality based on the operational power deviation, the diagnosis of PSCs based on the derivatives characteristics of the PV string, and the quantitative evaluation based on the I–V characteristic reconstruction. The quantitative evaluation of PSCs is the main focus in this paper, where the I–V characteristics of the unshaded PV modules in the partially shaded PV string are reconstructed according to different mismatch levels, respectively. The number of shaded PV modules and the corresponding severity of the partial shadings are estimated according to the reconstructed I–V characteristics. The simulation and experimental results verify that both the proposed diagnosis and quantitative evaluation method is effective with decent accuracy, especially for severe mismatch conditions. Experimental results show that the maximal mean absolute error of the quantified shaded area and quantified shaded rate are approximately 1.4446 units of 1/3 PV modules and 0.026, respectively.

Vibration analysis for early pitting detection during operation

The economic efficiency of machinery operation is significantly impacted by maintenance strategies. In the realm of condition-based or predictive maintenance strategies, the early detection of fatigue-induced damages is crucial. Therefore, this study focuses on the early detection of pitting damages during operation. Experimental investigations are conducted on a test gearbox acquiring acceleration data for different sizes of pitting damages under diverse operating conditions. A successful detection of the pitting damages during operation is achieved at a very early stage of progression, with a minimum size of 0.41% of an active tooth flank area. The utilization of design of experiments techniques facilitates the identification of factors influencing the detectability of pitting damages. The obtained results are analysed to elucidate the physical basis for the reliable detection of pitting damages across diverse operating conditions.

Air leaks fault detection in maintenance using machine learning

PurposeThe objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.Design/methodology/approachEffective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.FindingsBased on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.Practical implicationsEarly air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.Originality/valueTo the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.

Abstract GS01-05: Pembrolizumab + Olaparib vs Pembrolizumab + Chemotherapy After Induction With Pembrolizumab + Chemotherapy for Locally Recurrent Inoperable or Metastatic TNBC: Randomized Open-Label Phase 2 KEYLYNK-009 Study

Abstract Background: Pembrolizumab (pembro) plus chemotherapy (chemo) showed statistically significant improvements in PFS and OS vs placebo + chemo in patients (pts) with previously untreated locally recurrent inoperable or metastatic triple-negative breast cancer (TNBC) whose tumors expressed PD-L1 with a combined positive score (CPS) ≥10 in the phase 3 KEYNOTE-355 study. Tolerable and effective maintenance regimens after induction therapy are needed to sustain clinical benefit. The PARP inhibitor olaparib is an established maintenance therapy for multiple platinum-sensitive tumor types and prior data suggest that PARP inhibitors combined with PD-1/PD-L1 inhibitors could provide an improved therapeutic effect. KEYLYNK-009 (NCT04191135) is a randomized open-label phase 2 study to evaluate the efficacy and safety of maintenance pembro + olaparib vs pembro + chemo in pts with locally recurrent inoperable or metastatic TNBC who had clinical benefit from induction with 1L pembro + platinum-based chemo. Methods: Eligible pts with measurable, locally recurrent inoperable or metastatic TNBC that had not been previously treated with chemo in the metastatic setting received induction therapy for up to 6 cycles of pembro 200 mg + chemo (carboplatin AUC 2 + gemcitabine 1000 mg/m2 on days 1 and 8) every 3 wk (Q3W). Pts with complete response (CR), partial response (PR), or stable disease (SD) after 4-6 treatment cycles were randomized 1:1 to receive pembro 200 mg Q3W + olaparib 300 mg twice daily or pembro + chemo (continued induction regimen). Olaparib or chemo was continued until progression or unacceptable toxicity; pembro was continued for up to 35 cycles (including induction treatment), progression, or unacceptable toxicity. Pts were stratified by induction response (CR or PR vs SD, by RECIST v1.1), tumor PD-L1 status (CPS ≥1 vs CPS < 1), and tumor genomic status (tBRCAm vs tBRCAwt). Dual primary endpoints were PFS per RECIST v1.1 by BICR and OS from randomization in all pts. Secondary endpoints included PFS and OS from randomization in pts with PD-L1 CPS ≥10 tumors and in pts with tBRCAm, and safety. Results: Of the 460 pts who received induction therapy, 271 were randomly assigned to pembro + olaparib (n=135) or pembro + chemo (n=136). At the Dec 15, 2022 data cutoff, median follow-up was 17.2 mo. Maintenance pembro + olaparib did not significantly improve PFS or OS vs pembro + chemo after induction therapy (Table). Median PFS was longer with pembro + olaparib vs pembro + chemo in pts with tBRCAm, but not in pts with PD-L1 CPS ≥10 tumors; a similar trend was observed for OS (Table). In 268 treated pts, treatment-related AEs (TRAEs) occurred in 114 of 135 pts (84.4%) for pembro + olaparib and in 128 of 133 pts (96.2%) for pembro + chemo. Grade ≥3 TRAEs occurred in 44 pts (32.6%) for pembro + olaparib (0 deaths) and in 91 pts (68.4%) for pembro + chemo (2 [1.5%] deaths), with 12 (8.9%) vs 26 (19.5%) discontinuations for TRAEs, respectively. Conclusions: The primary endpoints of PFS and OS were not met in an unselected population of pts with advanced TNBC. However, directionally favorable improvements in PFS and OS were observed in pts with tBRCAm with pembro + olaparib vs pembro + chemo, representing a potential maintenance strategy. Further data are required to confirm this effect. No new safety signals were observed, and there were fewer TRAEs in the pembro + olaparib group vs the pembro + chemo group. PFS and OS were evaluated from the time of randomization. *Based on stratified Cox regression model with Efron’s method of tie-handling with treatment as a covariate. NR = not reached. Citation Format: Hope Rugo, Mark Robson, Seock-Ah Im, Florence Dalenc, Eduardo Yañez Ruiz, Young-Hyuck Im, Sergii Kulyk, Oleksandr Dudnichenko, Néstor Llinás-Quintero, Shigehira Saji, Yasuo Miyoshi, Nadia Harbeck, Li Fan, Jaime Mejia, Vassiliki Karantza, David Cescon. Pembrolizumab + Olaparib vs Pembrolizumab + Chemotherapy After Induction With Pembrolizumab + Chemotherapy for Locally Recurrent Inoperable or Metastatic TNBC: Randomized Open-Label Phase 2 KEYLYNK-009 Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr GS01-05.

Selective maintenance of continuously degrading systems with non-identical and stochastically dependent components

Selective maintenance (SM) is often performed on systems having limited maintenance resources to enhance mission reliability. In practical engineering, components within a system are typically distinct and correlated, leading to differences in the degradation processes and sensitivity to working conditions, as well as stochastic dependencies (S-dependencies) among the components. However, in most studies on the SM of continuously degrading systems (CDSs), these features are not considered, thereby limiting the accuracy of reliability evaluations and the efficiency of maintenance strategies. Therefore, considering the differences and S-dependencies of components, we investigate SM optimization for CDSs. Focusing on the diverse and stochastically dependent degradation processes of non-identical components, we first propose an extended degradation rate interaction model integrated with a general stochastic process to describe the multiple degradation processes of components and manifest the sensitivity differences to working conditions in the degradation states and rates. Next, a derivation method is developed to obtain explicit mission reliability functions for systems with arbitrary configurations. Subsequently, an SM optimization model that incorporates the effects of multiple differences and S-dependencies of the components is formulated and used to obtain an optimal maintenance strategy that considers resource and mission reliability constraints. Finally, the effectiveness of the proposed method is demonstrated using two numerical examples.

Nurses' Personality Traits and Perceived Work Environments During Public Health Emergencies: Implications for Nursing Workforce Planning.

The nursing workforce faces substantial challenges, particularly in the aftermath of the COVID-19 era. Developing an effective strategy for workforce maintenance and the strategic deployment of nurses is crucial. This study aimed to explore and categorize nurses' personality traits, with a focus on analyzing differences in their perceptions of the nursing work environment. Between January 2023 and February 2023, a multi-center cross-sectional study was carried out involving nurses from 12 tertiary hospitals actively engaged in frontline COVID-19 response duties. Through cluster sampling, surveys were distributed among eligible nursing staff, comprising a general information questionnaire, the Chinese Big Five Personality Questionnaire-Short Form, subjective evaluations of emergency nursing management, and the Chinese Nursing Work Environment Scale for Public Health Emergencies. Various statistical analyses, such as descriptive analysis, cluster analysis, non-parametric tests, and general linear model analysis, were employed to investigate the correlation between personality types and the perception of nursing work environments. The analysis encompassed 1059 valid questionnaires, reflecting the experiences of frontline nurses. The majority of these nurses possessed 1-5 years of experience, held junior professional titles, volunteered for their roles, and served as attending nurses. Categorization based on personality traits revealed three groups: resilient (35.60%), ordinary (16.15%), and distressed (48.25%) types. Significantly distinct perceptions of nursing work environments emerged among these categories, with resilient and ordinary types expressing notably higher satisfaction compared to the distressed group (H value = 256.487, p < 0.001). This study illustrates the connection between nurses' perceived working environment and their personality traits. Nursing managers should factor in nurses' personality traits when choosing and deploying frontline responders during public health emergencies. Prioritizing resilient-type nurses and crafting a supportive work environment that aligns with nurses' characteristics is indispensable for an effective emergency response.

Using the i-PARIHS theoretical framework to develop evidence implementation strategies for central venous catheter maintenance: a multi-site quality improvement project.

Evidence-based nursing practice can reduce complications associated with central venous catheters (CVCs). In this project, the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework was considered an ideal theoretical instrument to identify facilitators and barriers to implementing evidence-based practice. The project was conducted in pediatric intensive care units in six Chinese tertiary children's hospitals. Twenty-two audit criteria were obtained from best practice recommendations, and a baseline audit was conducted to assess current practice against best practice. Next, the i-PARIHS framework was used to identify facilitators and barriers to best practice and develop improvement strategies. A follow-up audit was then conducted to measure changes in compliance with best practices. Facilitators and barriers were identified at the innovation, recipient, and context levels. A comprehensive CVC maintenance strategy was then developed to apply the best evidence to nurses' clinical work. Of the 22 audit criteria, 17 showed significant improvement compared with the baseline audit. The i-PARIHS framework is an effective tool for developing targeted, evidence-based improvement strategies and applying these to the clinical setting. The quality of the nurses' clinical practice improved during CVC maintenance. However, there is no certainty that these positive results can be maintained, and long-term data are needed to verify this. http://links.lww.com/IJEBH/A185.

Another piece of the puzzle for the human microbiome: the gut virome under dietary modulation.

The virome is the most abundant and highly variable microbial consortium in the gut. Because of difficulties in isolating and culturing gut viruses and consequently shorting of reference genomes, the virome has remained a relatively elusive aspect of the human microbiome. In recent years, studies on the virome have accumulated growing evidence showing that the virome is diet-modulated and widely involved in regulating health. Here, we review the responses of the gut virome to dietary intake and the potential health implications, presenting changes in the gut viral community, preferences of viral members to particular diets. We further discuss how viral-bacterial interactions and phage lifestyle shifts shape the gut microbiota. We also discuss the specific functions conferred by diet on the gut virome and bacterial community in the context of horizontal gene transfer, as well as the import of new viral members along with the diet. Collating these studies will expand our understanding of the dietary regulation of the gut virome and inspire dietary interventions and health maintenance strategies targeting the gut microbiota.

A causal discovery approach to study key mixed traffic‐related factors and age of highway affecting raveling

AbstractThe relationship between real‐world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to five Dutch porous asphalt (PA) highway sites that have substantial data sets. Findings indicate a nonlinear relationship between traffic volume and raveling, with road age emerging as a shared contributor. The results also suggest that the degree to which different vehicle types contribute as a causal factor for raveling varies with carriageway configurations and lane characteristics. This underlines the need for targeted maintenance strategies. Challenges remain due to confounding correlations among traffic variables, necessitating further development of causal discovery models. This study may not conclusively resolve the debate on to what extent traffic contributes to raveling, but we argue we provide sufficient evidence against rejecting this hypothesis.

Hybrid Approach for Detection and Diagnosis of Short-Circuit Faults in Power Transmission Lines

In this article, the main problem under investigation is the detection and diagnosis of short-circuit faults in power transmission lines. The proposed fault detection (FDD) approach is mainly based on principal component analysis (PCA). The proposed fault diagnosis/identification (FAI) approach is mainly based on sliding-window versions of the discrete Fourier transform (DFT) and discrete Hilbert transform (DHT). The main contributions of this article are (a) a fault detection approach based on principal component analysis in the two-dimensional scores space; and (b) a rule-based fault identification approach based on human expert knowledge, combined with a probabilistic decision system, which detects variations in the amplitudes and frequencies of current and voltage signals, using DFT and DHT, respectively. Simulation results of power transmission lines in Portugal are presented in order to show the robust and high performance of the proposed FDD approach for different signal-to-noise ratios. The proposed FDD approach, implemented in Python, that can be executed online or offline, can be used to evaluate the stress to which circuit breakers (CBs) are subjected, providing information to supervision- and condition-based monitoring systems in order to improve predictive and preventive maintenance strategies, and it can be applied to high-/medium-voltage power transmission lines as well as to low-voltage electronic transmission systems.

A multi-objective particle swarm optimization based on local ideal points

Numerous multi-objective evolutionary algorithms have recently been proposed for the leader selection or archive maintenance process via using reference sets. Despite the potential of this strategy has been demonstrated in the existing literatures, a significant drawback is that this methodology requires additional parameters or predefined reference points, which leads to increasing the complexity in real-world applications and reducing the versatility in addressing various optimization challenges. Novel to this study, a new convergence contribution (CC) evaluator without extra parameters and predefined reference points is presented for convergence evaluation, where the inspiration is drawn from the concept of an ideal point on a Pareto front and the divide-and-conquer technique. Specifically, the local ideal points are introduced in this paper by dynamically fabricating from the approximate Pareto front. Furthermore, the CC evaluator and parallel cell distance (PCD) are cooperatively integrated into a multi-objective particle swarm optimization (MOPSO/CP) to enhance both the global best solution selection and archive maintenance strategies. Comparative experiments on 21 benchmark test functions exhibited that the performances in terms of inverted generational distance and hypervolume of the proposed MOPSO/CP was the best among those of the chosen competitive algorithms. The significant role of the cooperative mechanism and the CC evaluator is further verified by ablation studies. The superiority of the designed algorithm against its competitors is unequivocally highlighted by the experimental results.