Component Maintenance Strategies and Risk Analysis for Random Shock Effects Considering Maintenance Costs

Maintenance can improve a system’s reliability in a long operation period or when a component has failed. The reliability modeling method that uses the stochastic process degradation model to describe the system degradation process has been widely used. However, the existing reliability models established using stochastic processes only consider the internal degradation process, and do not fully consider the impact of external random shocks on their reliability modeling. Furthermore, the existing theory of importance does not consider the actual factors of maintenance cost. In this paper, based on the reliability modeling of random processes, the degradation rate under the influence of random shocks is introduced into the time scale function to solve the impact of random shocks on product reliability, and two cost importance measures are proposed to guide the maintenance selection of the components under limited resources in the system.Finally, a subsystem of an aircraft hydraulic system is analyzed to verify the proposed method’s performance

Importance Measure-Based Maintenance Strategy Considering Maintenance Costs

Maintenance is an important way to ensure the best performance of repairable systems. This paper considers how to reduce system maintenance cost while ensuring consistent system performance. Due to budget constraints, preventive maintenance (PM) can be done on only some of the system components. Also, different selections of components to be maintained can have markedly different effects on system performance. On the basis of the above issues, this paper proposes an importance-based maintenance priority (IBMP) model to guide the selection of PM components. Then the model is extended to find the degree of correlation between two components to be maintained and a joint importance-based maintenance priority (JIBMP) model to guide the selection of opportunistic maintenance (OM) components is proposed. Also, optimization strategies under various conditions are proposed. Finally, a case of 2H2E architecture is used to demonstrate the proposed method. The results show that generators in the 2E layout have the highest maintenance priority, which further explains the difference in the importance of each component in PM

Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model

Recommendation system (RS) plays significant roles in matching users information needs for Internet applications, and it usually utilizes the vanilla neural network as the backbone to handle embedding details. Recently, the large language model (LLM) has exhibited emergent abilities and achieved great breakthroughs both in the CV and NLP communities. Thus, it is logical to incorporate RS with LLM better, which has become an emerging research direction. Although some existing works have made their contributions to this issue, they mainly consider the single key situation (e.g. historical interactions), especially in sequential recommendation. The situation of multiple key-value data is simply neglected. This significant scenario is mainstream in real practical applications, where the information of users (e.g. age, occupation, etc) and items (e.g. title, category, etc) has more than one key. Therefore, we aim to implement sequential recommendations based on multiple key-value data by incorporating RS with LLM. In particular, we instruct tuning a prevalent open-source LLM (Llama 7B) in order to inject domain knowledge of RS into the pre-trained LLM. Since we adopt multiple key-value strategies, LLM is hard to learn well among these keys. Thus the general and innovative shuffle and mask strategies, as an innovative manner of data argument, are designed. To demonstrate the effectiveness of our approach, extensive experiments are conducted on the popular and suitable dataset MovieLens which contains multiple keys-value. The experimental results demonstrate that our approach can nicely and effectively complete this challenging issue.Comment: Accepted by CIKM2023 workshop at GenRec'2

Reliability analysis and recovery measure of an urban water network

Urban water networks are important infrastructures for cities. However, urban water networks are vulnerable to natural disasters, causing interruptions in water. A timely analysis of the reliability of urban water networks to natural disasters can reduce the impact of natural disasters. In this paper, from the perspective of network reliability, the reliability analysis method of urban water networks under disaster is proposed. First, a reliability model is established with the flow rate of nodes in the water network as the index. Second, the user's demand is considered, as well as the impact of water pressure on water use. Therefore, a node failure model considering node water pressure and flow rate is established. The performance degradation of the urban water network is analyzed by analyzing the cascading failure process of the network. Third, the recovery process of the urban water network is analyzed, and the changes in the reliability of the urban water network before and after the disaster are analyzed to assess the ability of the urban water network to resist the disaster. Finally, an urban water network consisting of 28 nodes, 42 edges and 4 reservoirs is used to verify the effectiveness of the proposed method

Opportunistic Maintenance Strategy of a Heave Compensation System for Expected Performance Degradation

In the marine industry, heave compensation systems are applied to marine equipment to compensate for the adverse effects of waves and the hydraulic system is usually used as the power system of heave compensation systems. This article introduces importance theory to the opportunistic maintenance (OM) strategy to provide guidance for the maintenance of heave compensation systems. The working principle of a semi-active heave compensation system and the specific working states of its hydraulic components are also first explained. Opportunistic maintenance is applied to the semi-active heave compensation system. Moreover, the joint integrated importance measure (JIIM) between different components at different moments is analyzed and used as the basis for the selection of components on which to perform PM, with the ultimate goal of delaying the degradation of the expected performance of the system. Finally, compared with conditional marginal reliability importance (CMRI)-based OM, the effectiveness of JIIM-based OM is verified by the Monte Carlo method

FedABC: Targeting Fair Competition in Personalized Federated Learning

Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poor performance. Recently, personalized federated learning (PFL) has achieved great success in handling Non-IID data by enforcing regularization in local optimization or improving the model aggregation scheme on the server. However, most of the PFL approaches do not take into account the unfair competition issue caused by the imbalanced data distribution and lack of positive samples for some classes in each client. To address this issue, we propose a novel and generic PFL framework termed Federated Averaging via Binary Classification, dubbed FedABC. In particular, we adopt the ``one-vs-all'' training strategy in each client to alleviate the unfair competition between classes by constructing a personalized binary classification problem for each class. This may aggravate the class imbalance challenge and thus a novel personalized binary classification loss that incorporates both the under-sampling and hard sample mining strategies is designed. Extensive experiments are conducted on two popular datasets under different settings, and the results demonstrate that our FedABC can significantly outperform the existing counterparts.Comment: 9 pages,5 figure

Linking component importance to optimisation of preventive maintenance policy

In reliability engineering, time on performing preventive maintenance (PM) on a component in a system may affect system availability if system operation needs stopping for PM. To avoid such an availability reduction, one may adopt the following method: if a component fails, PM is carried out on a number of the other components while the failed component is being repaired. This ensures PM does not take system’s operating time. However, this raises a question: Which components should be selected for PM? This paper introduces an importance measure, called Component Maintenance Priority (CMP), which is used to select components for PM. The paper then compares the CMP with other importance measures and studies the properties of the CMP. Numerical examples are given to show the validity of the CMP

Dual Fire Retardant Action: The Combined Gas and Condensed Phase Effects of Azo-Modified NiZnAl Layered Double Hydroxide on Intumescent Polypropylene

Ternary nickel-substituted layered double hydroxide (C-LDH) was synthesized. It was intercalated with azobenzene-4,4'-dicarboxylic acid, using an ion exchange method to obtain organically modified NiZnAl-LDH (O-LDH). Both LDHs were melt-blended into polypropylene (PP) with intumescent fire retardant (IFR). The structure, morphology, thermal stability and combustible properties of intercalated LDH and its hybrid composite have been comprehensively characterized. SEM and EDX mapping show O-LDH exhibits better dispersion than ZnNiAl-CO3 LDH (C-LDH). Cone calorimetry shows the addition of IFR and LDH significantly reduced smoke and heat release rate. The composite with 1 wt% O-LDH, which showed dual gas phase and condensed phase fire retardant action, exhibited the lowest flammability with an LOI value of 29.3 % and achieved a UL-94 V-0 rating. In addition, incorporation of LDH improved the mechanical properties compared to PP/IFR composites. UV absorption showed that O-LDH could significantly improve the ultraviolet stability of PP composites

Modelling the Effects of Climatic Factors on the Biomass and Rodent Distribution in a Tibetan Grassland Region in China

To identify the main climatic factors from 2007 to 2009 that influence biomass and rodent distribution, 576 fixed sample plots within 81 million km2 of different climatic grassland in Tibet were monitored. The aboveground biomass, the total burrows, the active burrows, the burrow index, and the rodent density in the plots were measured yearly in October. The monthly precipitation and the average temperatures from April to November were obtained for four successive years (2006-2009). Correlative and modelling analyses between the aboveground biomass, the rodent density, and the climatic factors were performed. The results showed that biomass and rodent density were significantly correlated with the climatic factors. Using ridge regression analyses, models of the biomass and rodent density with respect to the monthly precipitations and average temperatures of the previous year were developed. The raw testing data demonstrated that the models can be used approximately to predict biomass and rodent density