Maintenance Policy Research Articles

Assessment Methodology for Conservation Planning of Concrete Buildings: Ocean Swimming Pool (1960–2021) by Álvaro Siza in Portugal

ABSTRACT The paper intends to provide a building assessment methodology for the conservation planning of concrete buildings, presenting the demonstrative case-study of the Ocean Swimming Pool (design 1960–1966; conservation 2018–2021) conceived by the Pritzker Prize Álvaro Siza, in Portugal. The building is a landmark of 20th century architecture in the national and international context, being listed as National Monument and included in the World Heritage Tentative List. The article will focus particularly on the building assessment methodology, supported by the cross-analysis of the ‘documentary evidence’ and ‘physical evidence’, allowing for an integrated approach for conservation planning. The paper includes documentation on the building chronology and construction, as well as on the inspection and diagnosis using both non-destructive and minor destructive testing. Moreover, the article presents a pilot demonstration of concrete repair supported on the building knowledge and diagnosis. This real case study shows the advantages of integrating conservation criteria and methods used for traditional buildings to modern heritage in concrete (going beyond the common visible patch repair), in which there are still few case-studies illustrating good practices. This holistic approach has also contributed to the development of detailed policies for risk assessment, concrete conservation and maintenance in the framework of the Conservation Plan.

A Low-Cost Deep Learning System to Characterize Asphalt Surface Deterioration

Every day millions of people travel on highways for work- or leisure-related purposes. Ensuring road safety is thus of paramount importance, and maintaining good-quality road pavements is essential, requiring an effective maintenance policy. The automation of some road pavement maintenance tasks can reduce the time and effort required from experts. This paper proposes a simple system to help speed up road pavement surface inspection and its analysis towards making maintenance decisions. A low-cost video camera mounted on a vehicle was used to capture pavement imagery, which was fed to an automatic crack detection and classification system based on deep neural networks. The system provided two types of output: (i) a cracking percentage per road segment, providing an alert to areas that require attention from the experts; (ii) a segmentation map highlighting which areas of the road pavement surface are affected by cracking. With this data, it became possible to select which maintenance or rehabilitation processes the road pavement required. The system achieved promising results in the analysis of highway pavements, and being automated and having a low processing time, the system is expected to be an effective aid for experts dealing with road pavement maintenance.

Quasi-periodic Inspection and Preventive Maintenance Policy Optimisation for a system with Wiener Process degradation

Periodic inspection policy is performed for some degradation systems to check their degradation states, whereas it is usually difficult to implement on time due to impact of some random factors. Inspections and some maintenance actions are implemented in an inspection window with random, and thus how to optimize the inspection windows and the degradation threshold of the system to perform preventive maintenance (PM) are beneficial in practice. To this end, an optimisation of quasiperiodic inspection and PM policy with inspection window is proposed for a degradation system whose degradation followed Wiener process with a linear drift. Assume that PM can change the degradation rate and inspections are randomly performed in each inspection window. After optimisation, the optimal interval of the inspection window, the degradation threshold of PM and PM policy are determined by minimising the long-term running cost rate of the system. Finally, modeling and optimisation are illustrated using the degradation process of an axial piston pump, and the sensitivity analysis of some key parameters is conducted.

Evaluation of Maintenance Policies Using a Two-Stage Pythagorean-Based Group Decision-Making Approach

Evaluation of Maintenance Policies Using a Two-Stage Pythagorean-Based Group Decision-Making Approach

Maintenance policies considering degradation and cost processes for a multi-component system

PurposeThis paper models the deterioration process of a multi-component system. Each deterioration process is modelled by the Wiener process. The purposes of this paper are to address these issues and consider the cost process based on the multi-component system.Design/methodology/approachCondition-based Maintenance is a method for reducing the probability of system failures as well as the operating cost. Nowadays, a system is composed of multiple components. If the deteriorating process of each component can be monitored and then modelled by a stochastic process, the deteriorating process of the system is a stochastic process. The cost of repairing failures of the components in the system forms a stochastic process as well and is known as a cost process.FindingsWhen a linear combination of the processes, which can be the deterioration processes and the cost processes, exceeds a pre-specified threshold, a replacement policy will be carried out to preventively maintain the system.Originality/valueUnder this setting, this paper investigates maintenance policies based on the deterioration process and the cost process. Numerical examples are given to illustrate the optimisation process.

Optimal condition‐based maintenance policy for systems with mutually dependent competing failure

AbstractIn this paper, a novel condition‐based maintenance (CBM) policy for systems with mutually dependent competing failure processes (DCFPs) is studied. The cumulative random shocks contribute to the increased rate of degradation, meanwhile, the hard failure threshold is reduced when the amount of degradation reaches a threshold. The degradation process is simulated by a non‐stationary Gamma process and random shocks are modeled as a homogeneous Poisson process. Preventive replacement (PR) and corrective replacement (CR) are accounted for in the developed maintenance strategy. The optimal periodic inspection interval, PR threshold, and the number of cumulative random shocks can be derived by minimizing the average long‐term maintenance cost rate. In the end, a numerical case is applied to demonstrate the availability of the developed models.

A joint age-based system replacement and component reallocation maintenance policy: Optimization, analysis and resilience

The component reallocation (CR) policy is an emerging and effective preventive maintenance policy for improving the system performance, which has attracted much attention in reliability engineering and is independent from the research of age-based system replacement (ASR). This paper is to explore advantages and characteristics of a joint ASR and CR (JASRCR) preventive maintenance policy for a system, for which it is hard or uneconomical to execute repair or replacement at the component level but easy to reallocate the functionally-interchangeable and unevenly degraded components. In the JASRCR policy, the CR is implemented before the system failure and preventive ASR, and the corrective maintenance is to replace the system whenever it fails. The CR is of capability of balancing the degradation processes of components and thus has potential on improving the system resilience in resisting the component failures. An integrated mixed binary nonlinear optimization model is proposed to determine the optimal assignment of CR and optimal times for ASR and CR with the objective of minimizing the expected total maintenance cost per unit time. Numerical experiments demonstrate efficiency of the JASRCR policy on improving system resilience and reducing system maintenance cost.

Deep reinforce learning for joint optimization of condition-based maintenance and spare ordering

Condition-based maintenance (CBM) policy can avoid premature or late maintenance and reduce system failures and maintenance costs. Most existing CBM studies cannot solve the dimensional disaster problem in multi-component complex systems. Only some studies consider the constraint of maintenance resources when searching for the optimal maintenance policy, which is hard to apply to practical maintenance. This paper studies the joint optimization of the CBM policy and spare components inventory for the multi-component system in large state and action spaces. We use Markov Decision Process to model it and propose an improved deep reinforcement learning algorithm based on the stochastic policy and actor-critic framework. In this algorithm, factorization decomposes the system action into the linear combination of each component's action. The experimental results show that the algorithm proposed in this paper has better time performance and lower system cost compared with other benchmark algorithms. The training time of the former is only 28.5% and 9.12% of that of PPO and DQN algorithms, and the corresponding system cost is decreased by 17.39% and 27.95%, respectively. At the same time, our algorithm has good scalability and is suitable for solving Markov decision-making problems in large-scale state and action space.

Condition-based maintenance with reinforcement learning for refrigeration systems with selected monitored features

Worldwide, buildings are responsible for almost 30% of energy consumption, and those buildings that intensively use refrigeration systems, such as supermarkets and grocery stores, are also among the most energy-intensive consumers. Refrigeration devices, either commercial or residential, are responsible for a significant part of net emissions. Based on careful measurements, it is possible to reduce energy consumption in these devices by up to 15% only by improving the fault detection and diagnosis techniques. Thus, improving maintenance programs has become a crucial area in energy management in recent years. Nowadays, the market has experienced a hike after smart systems and new network interfaces applied to smart buildings that have allowed previously isolated devices to become smart devices, interacting with control algorithms smartly and, to some extent, autonomously. Here, we propose a reinforcement learning framework to develop a maintenance policy for mechanical compression refrigeration devices. Firstly, a test bench is built in which each component is assigned to be individually repairable and individually degradable in parallel and interconnected processes. Then, the degradation of the components is combined to formulate the system degradation, and the optimal maintenance policy is modeled via Markov decision processes and solved by a reinforcement learning algorithm. The agent-proposed maintenance program if compared to corrective maintenance, managed to reduce energy use and emissions by around 6% while avoiding shortfalls, as well as about the preventive program, where the agent managed to accomplish the same level of energy efficiency while reducing the maintenance costs by 31% and the time under maintenance in 10%. It was found that the reinforcement learning frameworks applied to maintenance have a series of challenges but are innovative and can show promising results compared to traditional maintenance techniques, such as preventive and corrective ones.

Optimal preventive maintenance policy for leased equipment for free with minimum quantity of consumables purchasing commitment by the lessee

PurposeThis paper deals with a particular type of leasing contracts according to which an equipment is leased for free with the condition for the lessee to purchase a predetermined minimum quantity of consumables during each leasing period. Maintenance actions are performed by the lessor and borne by him. Imperfect preventive maintenance is carried out every t time units throughout the leasing period. Minimal repairs are performed following equipment failures. At the end of the leasing period, an overhaul which restores the equipment to “as good as new” state is performed. The equipment is leased many times during its life cycle. The purpose of this paper is to determine the values of the decision variables for the lessor, which are the preventive maintenance (PM) period and the minimum quantity of consumables to be sold to ensure profit.Design/methodology/approachA mathematical model is developed to express the expected maintenance cost per time unit incurred by the lessor as well as his expected profit over the equipment life cycle. The optimal PM period minimizing the maintenance cost is determined first. Then, given the corresponding minimum maintenance cost, the minimum quantity of consumables (the lessor's break-even point) to be purchased by the lessee is computed. A numerical example and a sensitivity study are presented, and the obtained results are discussed.FindingsThe outcome of this work is supposed to help the lessors determining two key values to be included in each leasing contract, namely: (1) the periodicity according to which they will commit to perform preventive maintenance actions such that their average total cost of maintenance is minimized, (2) the minimum quantity of consumables that the lessee must commit to purchasing during the leasing period. This quantity must be between the break-even point and the maximum quantity associated with the capacity of the equipment.Practical implicationsPractically, the objective of this work is first to determine the optimal strategy to be adopted by the lessor in terms of effort relating to PM and second to determine the minimum quantity of consumables that the lessee must purchase during the leasing period such as profit is insured for the lessor.Originality/valueThis type of leasing (for free) has not been addressed in the literature particularly when considering maintenance strategies.

Maintenance policies and models: A bibliometric and literature review of strategies for reuse and remanufacturing

• Maintenance models and policies with reuse or remanufacture strategies are studied. • Models, business and sustainability strategies of reviewed papers are discussed. • Reuse and remanufacture have been implemented mainly to address economic benefits. • Future work should address environmental and social dimensions of sustainability. • New possibilities for reuse or remanufacture in maintenance contexts are provided. Reuse and remanufacturing have been widely studied in the maintenance related literature due to the imperative of preserving resources and protecting the environment. To provide researchers with a holistic view of the existing research, there is a need to check how relevant methods have been developed. This paper undertakesa bibliometric analysis and a comprehensive literature review of maintenance policies and models that deal with reuse or remanufacturing as sustainable strategies. A total of 581 papers collected from Web of Science, Scopus, and IEEE Xplore databases were analysed. 53 of them were chosen for further investigation after four selection criteria were applied. The bibliometric analysis provided a general understanding of the publication trends while the literature review built a taxonomy that organises the related literature under the dimensions of models, businesses, and sustainable strategies, which creates novelty in the maintenance-sustainability related literature. The main contribution is the classification of related papers and identification of the main knowledge gaps, including the neglect of the environmental and social aspects of sustainability vis-à-vis the prioritisation of an economic perspective.

Brand Lokal Produk Pariwisata Maluku Utara: Upaya Konservasi Bahasa Daerah

Post-pandemic economic recovery focuses on developing the tourism sector through revitalizing tourist objects and developing tourism-based MSMEs. This can be seen by the proliferation of MSMEs by sharing superior products. On the one hand, tourism development has a positive impact by increasing regional income, especially through foreign tourist visits. But on the other hand this policy can have a negative impact on the preservation of regional languages. Therefore tourism development policies must be balanced with regional language maintenance policies. This problem can be overcome with policies to use regional languages or local terms as tourism product brands. The use of local brands for tourism products can be a means of maintaining local languages. This study aims to analyze various languages or local terms used by tourism business actors in various tourism products as a form of language conservation. The use of qualitative descriptive methods with observation, interviews and documentation techniques to identify various tourism products that use local terms as brands, brands or labels in their marketing. In addition, the background and meaning of the various local terms are also traced to obtain comprehensive data. From several districts and cities in North Maluku, many business actors and tourism managers choose to use local terms as brands or product brands because of the philosophy or story telling in these local terms.

Designing optimal proactive replacement strategies for degraded systems subject to two types of external shocks

This article mainly investigates a proactive replacement policy for a stochastically deteriorating system concurrently subject to two types of shocks. First, the closed-form representation of system reliability function suffering from both a degradation process and environmental shocks is derived based on the degradation-threshold-failure (DTS) modeling framework. An age- and state-dependent competing risks model with mutual dependence between the two failure processes is embedded into system reliability modeling, where two types of shocks are taken into consideration upon arrival of an external shock including a minor one and a major one. Based on which, a bivariate maintenance policy is put forward for the deteriorating system, where the system is proactively replaced before failure at a planned time, or at an appropriate number of minimal repairs, whichever takes place first. The expected long-run cost rate (ELRCR) is formulated, and optimal solutions are evaluated analytically for two special cases. Finally, an illustrative example is redesigned to validate the theoretical results, exploring the significance of two types of shocks and mutual dependence in system reliability modeling, and illustrating the potential applications in maintenance decisions in various manufacturing systems.

Joint optimization of two-dimensional preventive maintenance parameters, technology level and product price under continuous usage rate and agent warranty services

ABSTRACT An efficient two-dimensional warranty model with the possibility of extending after-sale services to the end of the product life is developed in which services in the extended warranty period are assigned to an agent as a transaction side. To control product failures, two factors are taken into account in the proposed model: (1) the manufacturing technology level which results in a different reliability outcome for the product; and (2) a two-dimensional maintenance policy with respect to customers’ usage rate. In addition, in order to adapt the model to reality, we depart from the literature by considering the continuous nature of the usage rate variable. Finally, in order to confirm the effectiveness of the model, some comparative studies are carried out. The obtained results reveal that preventive maintenance (PM) activities lead to a considerable rise in cost savings. Besides, in comparison with one-dimensional PM policies based on product age and usage, using a two-dimensional PM policy improves the economic criteria. Furthermore, the presence of the agent has a great impact on reducing the customer’s cost as well as the failure rate after the basic warranty period.

Optimal maintenance policy for a k-out-of-n system with replacement first and last

Optimal maintenance policy for a k-out-of-n system with replacement first and last

Quantifications and predictions of sectoral pollutants emissions in Nigeria from 1980 to 2050.

The fast-economic development and population growth in Nigeria have resulted in huge quantities of air pollutants emission which have implications on the environment. Detailed sectoral emission inventory to serve as the basis for policy formation to mitigate the condition is still lacking. This study builds detailed sectoral emission inventory using the emission factor approach to estimates various pollutant emissions from different sources. Five major sources of pollutant emissions were identified which include transportation, energy, municipal solid waste, wood fuel, and agricultural sectors. An increasing trend in emissions from 1980 to 2020 was observed for total emission of CO, NOx, PM2.5, PM10, SO2, NH3 and NMVOC in Nigeria that increased from 1 736-6 210; 143-338; 126-551; 171-717; 19-60; 4-28; and 471-1 587 Gg, respectively. Wood fuel, transportation, and municipal waste sectors are the major sources that contributed to 63%, 16%, and 15% of the total CO emission. Three mitigation scenarios for emission reduction for the future were analyzed. CO emission reductions of 38%, 24%, and 38% will be obtained from the liquefied petroleum gas (LPG) intervention, waste to energy (WTE) technology, and vehicle inspection and maintenance (VIM) policy scenarios, respectively, through to the year 2050.

Code for "Dynamic loading and condition-based maintenance policies for multi-state systems with periodic inspection"

Code for "Dynamic loading and condition-based maintenance policies for multi-state systems with periodic inspection"

Cost-Effective Maintenance Policy for Sliding Surfaces of Bridge Bearings Using a Gamma Stochastic Process for Forecasting

To determine the optimal alert threshold for sliding surface replacement of bridge bearings, a cost-effective maintenance policy is proposed in this paper using a gamma stochastic process. First, the sliding surface-triggered run-to-failure process of bridge bearings is discussed based on existing field inspection and maintenance records. Then, the wear thickness of sliding materials is estimated step-by-step based on the cumulative travel distance and wear rate. The gamma stochastic process is used to model degradation of sliding surfaces by using the indicator of wear thickness to depict uncertainties during the degrading process. Next, the optimal alert threshold for replacement of sliding surfaces is determined based on the cost-effective maintenance policy by minimizing the long-term expected maintenance cost rate. Finally, bearings of a long-span suspension bridge are employed to demonstrate the potential effectiveness of the proposed methodology. As a result, the wear thickness of sliding materials approximately follows a linear degrading law. Based on the gamma degrading model, the objective function subject to the long-term expected cost rate is formed. After optimization, the optimal alert threshold for replacement of sliding surfaces is 2.016 mm to achieve a minimum long-term expected maintenance cost rate of US$9,427 per day. In addition, the estimated service life subject to the alert threshold obeys a Gaussian distribution with a mean of 1278 days based on the one-year monitored displacement data.

Code for "Dynamic loading and condition-based maintenance policies for multi-state systems with periodic inspection"

Code for "Dynamic loading and condition-based maintenance policies for multi-state systems with periodic inspection"

Joint optimisation of the maintenance and buffer stock policies considering back orders

Inventory control and maintenance systems are two main processes in production systems that play a vital role in the efficiency of production resources. This study aims to propose a joint optimisation procedure for maintenance and inventory policies in a single-machine production system considering back orders. The failure rate is random and the minimum level of accessibility required for preventive maintenance is considered. To close the problem at hand to the real-world condition, demand is considered as uncertain and shortage is inevitable. In addition, the unsatisfied demand is considered as back order. The problem is described and formulated via a mathematical model and all the possible scenarios corresponding to the different possible cases of the problem are described. Then a numerical iterative procedure is introduced to solve the problem in a reasonable time. The proposed procedure does not explore all solution space, but it behaves like a numerical optimisation procedure and provides a near optimal solution. The performance of the proposed method is evaluated using a numerical instance based on a real case study. In addition, some sensitivity analyses are performed to identify the most important effective parameters.