Dynamic Maintenance Research Articles

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

Application of dynamic maintenance strategy model based on group information and reliability

Application of dynamic maintenance strategy model based on group information and reliability

Workplace inclusion: Exploring employer perceptions of hiring employees with disability

AbstractThe unemployment rate for people with disability in Australia has remained unchanged for decades, despite policy and strategy focus. Therefore, understanding perceptions of those making hiring decisions is important. This research used a qualitative approach interviewing 13 participants who made hiring decisions. Reflexive thematic analysis uncovered four themes about altruistic hiring motivations, organisational culture barriers, sharing of disability during the hiring process, and negative emotions towards disability. There were distinct perspectives between people with and without experience of disability. People without experience tended to encourage early sharing of disability in the hiring process, cite organisational culture as a barrier, and shared strong negative emotions towards people with disability. People with experience of disability tended to prioritise autonomy of people with disability in sharing during the hiring process, and an intersectional approach to improve organisational culture. Regardless of disability experience, participants tended to share altruistic motives for hiring people with disability, despite this potentially contributing to the maintenance of power dynamics. Future research should continue to explore personal attributes and decision making of employers, ideally conducted by people with lived experience of disability. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

Improving production and maintenance planning with meta-learning-based failure prediction

Digital manufacturing systems allow for dynamic production and maintenance planning adaptation based on internal and external evolving changes. In this context, data-driven predictive techniques can improve maintenance planning. However, dynamic production environments require the development of novel methods capable of efficiently managing the extensive data generated by manufacturing systems and processes while providing valuable insights for decision-making. Thus, this research proposes an integrated production and maintenance planning (IPMP) approach for digital manufacturing systems featuring a dynamic simulation-based optimization and a meta-learning-based failure prediction, which selects the best prognostic method to predict machine failures in real-time. The proposed model was applied to the Minifab case, in the semiconductor industry, providing a reduction of 20.9 % in lead time and 25.9 % in tardy jobs compared to setups with static dispatching rules and single-prognostic-method failure prediction. Such findings support the pertinence of the proposed novel approach in the area of production planning and control, contributing to the improvement of business performance, reliability, and competitiveness.

The HMG-box module in FACT is critical for suppressing epigenetic variegation of heterochromatin in fission yeast.

Chromatin condensation state is the key for retrieving genetic information. High-mobility group protein (HMG) proteins exhibit DNA-binding and bending activities, playing an important role in the regulation of chromatin structure. We have shown that nucleosomes tightly packaged into heterochromatin undergo considerable dynamic histone H2A-H2B maintenance via the direct interaction between HP1/Swi6 and facilitate chromatin transcription (FACT), which is composed of the Spt16/Pob3 heterodimer and Nhp6. In this study, we analyzed the role of Nhp6, an HMG box protein, in the FACT at heterochromatin. Pob3 mutant strains showed derepressed heterochromatin-dependent gene silencing, whereas Nhp6 mutant strains did not show significant defects in chromatin regulation or gene expression, suggesting that these two modules play different roles in chromatin regulation. We expressed a protein fusing Nhp6 to the C-terminus of Pob3, which mimics the multicellular FACT component Ssrp1. The chromatin-binding activity of FACT increased with the number of Nhp6 fused to Pob3, and the heterochromatin formation rate was promoted more strongly. Furthermore, we demonstrated that this promotion of heterochromatinization inhibited the heterochromatic variegation caused by epe1+ disruption. Heterochromatic variegation can be observed in a variety of regulatory steps; however, when it is caused by fluctuations in chromatin arrangement, it can be eliminated through the strong recruitment of the FACT complex.

Deep Learning-Based Optimal Scheduling Scheme for Distributed Wind Power Systems

For maintenance of distributed wind power networks, it remains important to realize intelligent operation scheduling strategies for wind power equipments according to their working status. As a consequence, this paper proposes a deep learning-based optimal scheme for distributed wind power networks. First of all, an adaptive status assessment model is constructed to identify time-varying operation status for unit components. Then, based on the predicted operation risk of unit components, a preventive maintenance decision model is formulated to realize flexible decision-making of maintenance tasks. Finally, a dynamic maintenance task scheduling model based on extreme learning machine (ELM) neural network is designed. The ELM neural network-based scheduling approach is expected to use a historical strategy library to assist in revising realtime voltage control strategy. Also, we conduct some experiments to evaluate the performance of the proposed method through simulation modeling. The obtained results show that real-time voltage control accuracy for wind power networks with an incomplete observation area is improved.

A chance-constrained net revenue model for online dynamic predictive maintenance decision-making

Most models for predictive maintenance (PdM) decision-making focus on the expected value of a system performance metric (e.g., the expected cost rate). However, focusing solely on such expected values may overlook the significant risks of resulting PdM decisions. Indeed, it would be practically valuable to make the optimal PdM decision by considering the least probability of achieving a target system performance. Furthermore, emphasizing only maintenance costs without accounting for operational revenues can render the maintenance strategies unappealing, particularly when the revenues across system lifecycles are significant. This study introduces a method that employs a stochastic net revenue model and chance-constrained programming to address these issues in PdM decision-making, wherein the preventive maintenance cost is proportional to the system's current degradation state. By exemplifying system degradation with the Gamma process, we derive the probability density function of stochastic net revenue and present a Bayesian method to simultaneously update the drift and diffusion parameters of the Gamma process model. The effectiveness and practical applicability of our proposed chance-constrained net revenue model and parameter updating method are demonstrated through a numerical example and a case study.

Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis

Under a distributed information system, the scale of various operational components such as applications, operating systems, databases, servers, and networks is immense, with intricate access relationships. The silo effect of each professional is prominent, and the linkage mechanism is insufficient, making it difficult to locate the infrastructure components that cause exceptions under a particular application. Current research only plays a role in local scenarios, and its accuracy and generalization are still very limited. This paper proposes a novel fault location method based on dynamic operation maps and alarm common point analysis. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, connection common points (physical and logical), and access flow common points. This method, compared with knowledge graph approaches, eliminates the complex process of knowledge graph construction, making it more concise and efficient. Furthermore, in contrast to indicator correlation analysis methods, this approach supplements with configuration correlation information, resulting in more precise positioning. Through practical validation, its fault hit rate exceeds 82%, which is significantly better than the existing main methods.

Dynamic grouping maintenance optimization by considering the probabilistic remaining useful life prediction of multiple equipment

For multi-equipment maintenance of modern production equipment, the economic correlation and degradation uncertainty may lead to insufficient or excessive maintenance, increasing maintenance costs. This paper proposes a dynamic grouping maintenance method based on probabilistic remaining useful life (RUL) prediction for multiple equipment. Long short term memory (LSTM) is developed to predict the equipment probability RUL by the Variational Auto-Encoder (VAE) resampling. Then, the dynamic grouping maintenance model is constructed to minimize the maintenance cost rate under the known probabilistic RUL information. The gazelle optimization algorithm (GOA) is used to determine the optimal maintenance time for each equipment. To better verify the effectiveness of the proposed method, a numerical case with six wind turbines is introduced to analyse the performance of GOA. Moreover, the advantages of dynamic grouping maintenance is verified by comparing with independent maintenance, whose maintenance cost rate is reduced by 10.01%.

Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms

Dynamic Topology Organization and Maintenance Algorithms for Autonomous UAV Swarms

Fundamentals of redox regulation in biology.

Oxidation-reduction (redox) reactions are central to the existence of life. Reactive species of oxygen, nitrogen and sulfur mediate redox control of a wide range of essential cellular processes. Yet, excessive levels of oxidants are associated with ageing and many diseases, including cardiological and neurodegenerative diseases, and cancer. Hence, maintaining the fine-tuned steady-state balance of reactive species production and removal is essential. Here, we discuss new insights into the dynamic maintenance of redox homeostasis (that is, redox homeodynamics) and the principles underlying biological redox organization, termed the 'redox code'. We survey how redox changes result in stress responses by hormesis mechanisms, and how the lifelong cumulative exposure to environmental agents, termed the 'exposome', is communicated to cells through redox signals. Better understanding of the molecular and cellular basis of redox biology will guide novel redox medicine approaches aimed at preventing and treating diseases associated with disturbed redox regulation.

Complexity analysis of integrated dynamic lot sizing and maintenance planning problems

We consider a problem where the planning decisions of producing on and maintaining a single machine are integrated. The age of the machine, and thus when maintenance should be performed, is influenced by the production decisions. Decisions are taken on a planning horizon discretized in periods. The computational complexity of several variants of the single-item problem are studied depending on whether (i) Maintenance can be performed on the machine at any point in time in a period or must be performed at the end of a period, (ii) There is a fixed aging component when starting production, and (iii) There is a minimum age before maintenance can be performed on the machine. The multi-item case is proved to be NP-hard.

Improving Physical Abilities And The Most Efficient Methods In Adults Over 40 Years Old

Only a small percentage of adults engage in regular physical activity, which means a decline in quality of life and a very poor result compared to Europe (Osiński, 2013). The purpose of this literature review is to find the most contemporary methods from the most ranked scientific works on the improvement of physical abilities and the most efficient methods in adults over 40 years old. Digital databases such as Scopus, Medline, JStore, Embase, Cochrane Central, PsycInfo, Schoolar, and SPORTDiscus were used in this literature review. Inclusion criteria are: individuals must be healthy and average age at least 40 years old. Many other studies such as (Skelton et al., 1994; Foldvari et al., 2000; Bean et al., 2007) report that physical functions are more related to muscle power than to muscle strength or mass. Strength training has many benefits on physical function in older adults compared to traditional strength training (Tschopp et al., 2011; Da Rosa., 2019). Harmonization or proper use of variables such as exercise selection, breaks, number of sets, number of repetitions, frequency affect the increase in the level of muscular fitness. Strength, muscular endurance, muscular strength and muscular hypertrophy are the characteristics that should be trained (Ratamess et al., 2009). While in an all-inclusive program resistance training improves the cardiovascular system (Fleck. 1988), limits the risk of coronary heart disease (Goldberg. 1989; Hurley & Kokkinos, 1987), helps prevent osteoporosis (Layne & Nelson. 1999), removes the risk of diabetes (Miller et al., 1984), promotes maintaining a healthy weight and losing excess weight (Evans. 1999), reduces the risk of cancer in the spine (Koffler et al., 1992), helps in psychological/mental well-being (Ewart. 1989) as well as improves dynamic stability and maintenance of functional capacity (Evans. 1999). In conclusion from the study of Balachandran et al. (2022) found that strength training leads to improvements in physical function more than traditional strength training. Keywords: fitness, aging, power, strength, exercises

Leveraging IoT for Smart Grids and Energy Management in Electrical Systems: Applications, Benefits, and Challenges

The integration of the Internet of Things (IoT) into electrical systems is transforming traditional power grids into intelligent, data-driven networks, capable of real-time monitoring, control, and optimization. This transformation, often referred to as the development of “smart grids,” presents opportunities for improving energy efficiency, enhancing reliability, and ensuring sustainability. IoT facilitates dynamic demand response, predictive maintenance, and seamless integration of renewable energy sources, positioning it as a key enabler of the energy transition. This paper provides a comprehensive overview of the role of IoT in electrical systems, with an emphasis on smart grids, predictive maintenance, and energy management in smart buildings. It explores IoT’s architecture, benefits, and challenges, including cybersecurity concerns and the lack of industry-wide standardization. The study also incorporates real-world data and graphical representations to illustrate the impact of IoT in optimizing power systems. Additionally, this paper discusses future prospects for IoT in electrical systems and suggests solutions to overcome existing challenges.

A Lithium-Ion Battery Degradation Prediction Model With Uncertainty Quantification for Its Predictive Maintenance

Battery degradation modeling in the presence of uncertainty is a key but challenging issue in the applications of battery predictive maintenance. This paper develops a capacity prediction model with uncertainty quantification for lithium-ion batteries and proposes a dynamic maintenance strategy which can help to make an optimized decision at each battery cycle stage. To be specific, after using the one-dimensional convolution neural network (1dCNN), deep representative features hidden in original measured signals are extracted. Then, the bi-directional long short-term memory (Bi-LSTM) is applied to estimate the battery capacities, while the quantile regression (QR) layer is embedded into the construction of the Bi-LSTM network to obtain the capacities for different quantiles. Next, the kernel density estimation (KDE) is utilized to derive the probability density of the predicted points at each battery cycle stage. Thus, the combination of 1dCNN, Bi-LSTM, QR and KDE, named 1dCNN-BiLSTMQR-KDE, forms an efficacious capacity prediction model with reliable uncertainty management. Finally, the costs of different decisions at each battery cycle stage are evaluated, and the decision with the lower cost will be chosen. The whole proposition is verified on battery degradation datasets from NASA, and the comparison with other methods show that the proposed method is competitive.

Temporal network of experience sampling methodology identifies sleep disturbance as a central symptom in generalized anxiety disorder

BackgroundA temporal network of generalized anxiety disorder (GAD) symptoms could provide valuable understanding of the occurrence and maintenance of GAD. We aim to obtain an exploratory conceptualization of temporal GAD network and identify the central symptom.MethodsA sample of participants (n = 115) with elevated GAD-7 scores (Generalized Anxiety Disorder 7-Item Questionnaire [GAD-7] ≥ 10) participated in an online daily diary study in which they reported their GAD symptoms based on DSM-5 diagnostic criteria (eight symptoms in total) for 50 consecutive days. We used a multilevel VAR model to obtain the temporal network.ResultsIn temporal network, a lot of lagged relationships exist among GAD symptoms and these lagged relationships are all positive. All symptoms have autocorrelations and there are also some interesting feedback loops in temporal network. Sleep disturbance has the highest Out-strength centrality.ConclusionsThis study indicates how GAD symptoms interact with each other and strengthen themselves over time, and particularly highlights the relationships between sleep disturbance and other GAD symptoms. Sleep disturbance may play an important role in the dynamic development and maintenance process of GAD. The present study may develop the knowledge of the theoretical model, diagnosis, prevention and intervention of GAD from a temporal symptoms network perspective.

A preliminary investigation on enabling digital twin technology for operations and maintenance of urban underground infrastructure

Underground infrastructure plays a kind of crucial role in modern production and living, especially in big cities where the ground space has been fully utilized. In the context of recent advancements in digital technology, the demand for the application of digital twin technology in underground infrastructure has become increasingly urgent as well. However, the interaction and co-integration between underground engineering entities and virtual models remain relatively limited, primarily due to the unique nature of underground engineering data and the constraints imposed by the development of information technology. This research focuses on underground engineering infrastructure and provides an overview of the application of novel information technologies. Furthermore, a comprehensive framework for digital twin implementation, which encompasses five dimensions and combines emerging technologies, has been proposed. It thereby expands the horizons of the intersection between underground engineering and digital twins. Additionally, a practical project in Wenzhou serves as a case study, where a comprehensive database covering the project’s entire life cycle has been established. The physical model is visualized, endowed with functional implications and data analysis capabilities, and integrated with the visualization platform to enable dynamic operation and maintenance management of the project.

Resilience-oriented approach of dynamic production and maintenance scheduling optimisation considering operational uncertainty

ABSTRACT One of the main challenges in operating multistate manufacturing systems (MMSs) is maintaining stable and robust production against various disruptions. Therefore, an urgent need exists for an operation and maintenance (O&M) method that optimises MMS resilience, i.e. the capability of withstanding or recover from disruptions of various sources. Consequently, this study proposes an integrated resilience-oriented production and maintenance scheduling approach for MMSs. This approach enhances MMS resilience by reinforcing its adaptivity to the variation in production requirements. Based on a conceptual investigation of operational uncertainty and its mechanism of disruption, this study devotes to (i) formulating a performance loss-based resilience measurement with consideration of operational uncertainties and (ii) proposing a reinforcement learning-based approach to schedule MMS operation and maintenance activities for MMS components of different performance states. An industrial case study of a ferrite phase shifter manufacturing system is subsequently conducted to validate the proposed approach. Results demonstrate the effectiveness of the proposed approach in the resilience optimisation of MMSs.

A dynamic condition-based maintenance policy for heterogeneous-wearing tools with considering product quality deterioration

The wear of a cutting tool can lead to tool failure and product quality deterioration, and thus timely maintenance of tools is crucial. Meanwhile, the wear of tools from a same population usually exhibits heterogeneous patterns. Therefore, this paper proposes a dynamic condition-based maintenance (CBM) policy for heterogeneous-wearing tools with considering the product quality deterioration caused by tool wear. The tool wear is modelled by an Inverse Gaussian (IG) process, and the wear rate is assumed to be a random variable to characterise the heterogeneity among tool wear processes. The posterior distribution of reciprocal of tool wear rate is dynamically estimated using the online wear data based on a Bayesian approach. Moreover, the impact of tool wear on product quality deterioration is modelled. The IG process is discretized into a discrete time Markov chain (DTMC). Under the frame of the DTMC, a cost function, containing product quality loss, preventive maintenance (PM) cost and corrective maintenance cost, is developed to determine the optimal PM threshold. The cost function updates dynamically with the dynamic estimation of tool wear rate and thus enables the optimal PM threshold to be dynamically revised. The effectiveness of the proposed CBM policy is demonstrated through a case study.

Established and emerging techniques for the study of microglia: visualization, depletion, and fate mapping.

The central nervous system (CNS) is an essential hub for neuronal communication. As a major component of the CNS, glial cells are vital in the maintenance and regulation of neuronal network dynamics. Research on microglia, the resident innate immune cells of the CNS, has advanced considerably in recent years, and our understanding of their diverse functions continues to grow. Microglia play critical roles in the formation and regulation of neuronal synapses, myelination, responses to injury, neurogenesis, inflammation, and many other physiological processes. In parallel with advances in microglial biology, cutting-edge techniques for the characterization of microglial properties have emerged with increasing depth and precision. Labeling tools and reporter models are important for the study of microglial morphology, ultrastructure, and dynamics, but also for microglial isolation, which is required to glean key phenotypic information through single-cell transcriptomics and other emerging approaches. Strategies for selective microglial depletion and modulation can provide novel insights into microglia-targeted treatment strategies in models of neuropsychiatric and neurodegenerative conditions, cancer, and autoimmunity. Finally, fate mapping has emerged as an important tool to answer fundamental questions about microglial biology, including their origin, migration, and proliferation throughout the lifetime of an organism. This review aims to provide a comprehensive discussion of these established and emerging techniques, with applications to the study of microglia in development, homeostasis, and CNS pathologies.