Imperfect Maintenance Actions Research Articles

Effect of test-caused degradation on the unavailability of standby safety components

This paper proposes a safety-critical standby component unavailability model that contains aging effects caused by the elapsed time from installation, component degradation due to surveillance tests, and imperfect maintenance actions. An application of the model to a Motor-Operated Valve and a Motor-Driven Pump involved in the HPIS of a VVER/1000-V446 nuclear power plant is demonstrated and compared with other existing models at component and system levels. In addition, the effects of different unavailability models are reflected in the NPP's risk criterion, i.e., core damage frequency, over five maintenance periods. The results show that, compared with other models that do not simultaneously consider the full effects of degradation and maintenance impacts, the proposed model realistically evaluates the unavailabilities of the safety-related components and the involved systems as a plant age function. Therefore, it can effectively reflect the age-dependent CDF impact of a given testing and maintenance policy in a specified time horizon.

Dynamic inspection and maintenance scheduling for multi-state systems under time-varying demand: Proximal policy optimization

Inspection and maintenance activities are effective ways to reveal and restore the health conditions of many industrial systems, respectively. Most extant works on inspection and maintenance optimization problems have assumed that systems operate under a time-invariant demand. Such a simplified assumption is oftentimes violated by a changeable market environment, seasonal factors, and even unexpected emergencies. In this article, with the aim of minimizing the expected total cost associated with inspections, maintenance, and unsupplied demand, a dynamic inspection and maintenance scheduling model is proposed for Multi-State Systems (MSSs) under a time-varying demand. Non-periodic inspections are performed on the components of an MSS and imperfect maintenance actions are dynamically scheduled based on the inspection results. By introducing the concept of decision epochs, the resulting inspection and maintenance scheduling problem is formulated as a Markov Decision Process (MDP). The Deep Reinforcement Learning (DRL) method with a Proximal Policy Optimization (PPO) algorithm is customized to cope with the “curse of dimensionality” of the resulting sequential decision problem. As an extra input feature for the agent, the category of decision epochs is formulated to improve the effectiveness of the customized DRL method. A six-component MSS, along with a multi-state coal transportation system, is given to demonstrate the effectiveness of the proposed method.

Statistical inference for a Wiener‐based degradation model with imperfect maintenance actions under different observation schemes

AbstractThis paper studies the statistical inference in a degradation model with imperfect maintenance. Technological or industrial devices subject to degradation undergo maintenance actions that reduce their degradation level. The underlying degradation process is a Wiener process with drift. Maintenance effects are assumed to be imperfect, described by an Arithmetic Reduction of Degradation () model. The system is regularly inspected and the degradation levels are measured. Four different observation schemes are considered so that degradation levels can be observed between maintenance actions as well as just before or just after maintenance times. The paper studies the estimation of the model parameters under the four observation schemes. Maximum likelihood estimators are derived for each scheme. The quality of the estimations is assessed and the observation schemes are compared through an extensive simulation and performance study.

Implementation of automation system-based model checking for managing imperfect maintenance actions in chemical plant projects

PurposeThe chemical plant (CP) maintenance industry has been under increasing pressure by process designers to demonstrate its evaluation and information management of model checking (MC) on the durability’s performance and design of plant control instrument. This main problem has been termed as imperfect maintenance actions (IMAs) level. Although IMAs have been explored in interdisciplinary maintenance environments, less is known about what imperfect maintenance problems currently exist and what their causes are, such as the recent explosion in the Beirut city (4 August 2020, about 181 fatalities). The aim of this paper is to identify how CP maintenance environments could integrate MC within their processes.Design/methodology/approachTo achieve this aim, a comprehensive literature review of the existing conceptualisation of MC practices is reviewed and the main features of information and communication technology tools and techniques currently being employed on such IMA projects are carried out and synthesised into a conceptual framework for integrating MC in the automation system process.FindingsThe literature reveals that various CP designers conceptualise MC in different ways. MC is commonly shaped by long-term compliance to fulfil the requirement for maintaining a comfortable durability risk on imperfect maintenance schemes of CP projects. Also, there is a lack of common approaches for integrating the delivery process of MC. The conceptual framework demonstrates the importance of early integration of MC in the design phase to identify alternative methods to cogenerate, monitor and optimise MC.Originality/valueThus far, this study advances the knowledge about how CP maintenance environments can ensure MC delivery. This paper highlights the need for further research to integrate MC in CP maintenance environments. A future study could validate the framework across the design phase with different CP project designers.

The Impact of Imperfect Maintenance Scheduling on the Physical Degradation of Painted Renderings

Theoretical studies suggest that maintenance improves the state of conservation of buildings and components, increasing their durability and service life. However, the existing studies point to a qualitative improvement somewhere between “as-good-as-new” and “as-bad-as-old”. This study proposes a methodology to estimate the impact of imperfect maintenance actions on the degradation condition of painted renderings in external walls, based on an extensive fieldwork survey evaluating the timing and the effect of different maintenance actions on the life cycle of these claddings. Façades are analyzed at different instants (before and after the execution of maintenance services). The results reveal that the maintenance actions usually improve the condition of the painted renderings, although they are performed at inadequate moments. Cleaning actions reduce approximately 13% of the overall degradation condition of the claddings (12.2% for renderings and 13.2% for painted surfaces). The partial repair of renderings leads to a reduction of approximately 70% of the façade’s overall degradation condition. The façade only returns to the condition “as-good-as-new” when a total replacement of the rendering is performed. This study contributes to a better understanding of the phenomenon and to a more accurate projection of the real effect of the maintenance actions on the durability of façades.

Optimum opportunistic maintenance schedule over variable horizons considering multi-stage degradation and dynamic strategy

A suitable maintenance schedule is crucial for large-scale, complex multi-component systems undertaking a long-term operation to reduce failure risks and improve availability and profitability. Opportunistic maintenance is a popular solution when cost consideration is paramount. However, the extensively adopted single-level preventive maintenance action and single-stage degradation process limit the development and application of opportunistic maintenance. Multi-level preventive maintenance including both perfect and imperfect maintenance actions on multi-stage degradation is considered within an opportunistic model to address this challenge. A reliability evaluation model is first formulated to describe a multi-stage degradation process involving the effect of imperfect maintenance. An opportunistic model is then proposed to arrange reliability proportion thresholds for components in systems. The cost rate is considered as the objective function to accommodate the opportunistic model to variable scheduling horizons. A flexible dynamic strategy is developed within the opportunistic model to coordinate the schedules on the system and components by updating component schedules once a system maintenance action is executed. An improved hybrid genetic algorithm combining differential evolution is adopted to optimize the complex problem. The case studies on a locomotive system provide a better understanding of the proposed models and demonstration of its effectiveness, generality, and robustness.

Opportunistic preventive maintenance scheduling for multi-unit reusable rocket engine system based on the variable maintenance task window method

The reusable rocket engine (RRE), recognized as future propulsion system for space transportation, is composed of multiple components of which each one plays an essential role for the whole system. In rocket launches, these components are impaired by severe service conditions while the maintenance actions can only be executed in mission intervals. To program a reasonable maintenance scheme for the RRE system, an opportunistic preventive maintenance (PM) scheduling strategy is proposed: at the component-level, we evaluate the PM intervals dynamically by minimizing the maintenance cost rate over a life cycle under the reliability constraint where two imperfect maintenance actions: maintenance repair and replacement, are taken into account; at the system-level, a variable maintenance task window (VMTW) method is applied to dynamically rearrange maintenance actions when the maintenance opportunities arise. The novel strategy can effectively reduce the PM cost and avoid unnecessary depot maintenance. Its efficiency is validated by a comparison with traditional optimal preventive maintenance schedule derived from the genetic algorithm.

Maintenance Decision Optimization of Multi-State System Considering Imperfect Maintenance

In the maintenance decision-making problem of a multi-state system, it is more in line with the actual situation to consider imperfect maintenance. However, under the influence of imperfect maintenance, the failure mechanism and maintenance process of the multi-state system are more complicated. Further discussion is needed about how to deal with the relationship between the states of various components and the overall state of the system, and how to make the optimal maintenance strategy. To address this issue, this paper proposes a multi-state system maintenance decision optimization method considering imperfect maintenance. Firstly, an imperfect maintenance model is established based on quasi-renewal theory. Secondly, the mapping relationship between the state of the components and the system is decoupled combining universal generating function with the non-homogeneous continuous-time Markov model, the reliability evaluation of the multi-state system is realized. Finally, the maintenance decision optimizing model considering imperfect maintenance is constructed, the optimal maintenance strategy of the multi-state system is obtained by optimizing the number of imperfect maintenance actions to maximize the expected benefit of the whole life cycle.

Coordination of the decisions associated with maintenance, quality control and production in imperfect deteriorating production systems

In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. In this study, a single machine produces a product with a known and constant production rate per time unit and the production process has two operational states, i.e. in-control state and out-of-control state, and the probability of the state transition follows a general distribution. To monitor the process, sampling inspection is conducted during a production cycle and a proper control chart is applied. In the developed model, there is no restriction on the type of the control chart. Therefore, different control charts can be applied in practice for quality control. The lot size produced in each production cycle is determined with respect to the production rate of the machine and the proportion of conforming and non-conforming items produced in each cycle. In this study, preventive maintenance and corrective maintenance as perfect maintenance actions and minimal maintenance as imperfect maintenance action are applied to maintain the process in a proper condition. The objective of the integrated model is to plan the maintenance actions, determine the optimal values of the control chart parameters and optimize the production level to minimize the expected total cost of the process per time unit. To evaluate the performance of this model, a numerical study is solved and a sensitivity analysis is conducted on the critical parameters and the obtained results are analyzed.

Condition-based opportunistic maintenance policy for a series–parallel hybrid system with economic dependence

PurposeThe purpose of this paper is to propose a condition-based opportunistic maintenance policy considering economic dependence for a series–parallel hybrid system with a K-out-of-N redundant structure, where a single component in series is denoted as subsystem1, and K-out-of-N redundant structure is denoted as subsystem2.Design/methodology/approachBased on the theory of Residual Useful Life (RUL), inspection points are determined, and then different maintenance actions are adopted in the purpose of minimizing the cost rate. Both perfect and imperfect maintenance actions are carried out for subsystem1. More significantly, regarding economic dependence, condition-based opportunistic maintenance is designed for the series–parallel hybrid system: preemptive maintenance for subsystem1, and both preemptive and postponed maintenance for subsystem2.FindingsThe sensitivity analysis indicates that the proposed policy outperforms two classical maintenance policies, incurring the lowest total cost rate under the context of both heterogeneous and quasi-homogeneous K-out-of-N subsystems.Practical implicationsThis model can be applied in series–parallel systems with redundant structures that are widely used in power transmission systems in electric power plants, manufacturing systems in textile factories and sewerage systems. Considering inconvenience and high cost incurred in the inspection of hybrid systems, this model helps production managers better maintain these systems.Originality/valueIn maintenance literature, much attention has been received in repairing strategies on hybrid systems with economic dependence considering preemptive maintenance. Limited work has considered postponed maintenance. However, this paper uses both condition-based preemptive and postponed maintenance on the issue of economic dependence bringing opportunities for grouping maintenance activities for a series–parallel hybrid system.

Optimal preventive replacement last policy for a successive random works system with random lead time

Most systems deteriorate with age and use, and eventually, fail from either or both causes in random environment. It is reasonable in commercial industries that only one spare item is available for replacement. Selecting a cost efficient and effective replacement strategy to improve system reliability and inventory of spare parts is crucial work. In this paper, we consider an operating system which works at successive random times and models an imperfect maintenance action under two types of age-dependent failures, repairable minor failures (R) and unrepairable catastrophic failures (U). R failures are followed by a minimal repair and U failures are removed by a corrective replacement. A notation of preventive replacement last policy is considered in which the system is replaced before any U failures at age T or at number N of working times, whichever occurs last. A spare unit for replacement can be delivered upon order and is available only when the random lead time is finished. The main objective is to determine an optimal schedule (T, N) of that minimizes the mean cost rate function in a finite time horizon. The existence and uniqueness of optimal PRL policy are derived analytically and computed numerically.

An improved imperfect maintenance strategy for multiperiod randomly failing equipment with stochastic repair times

PurposeIn this paper, a maintenance strategy based on improved imperfect maintenance actions with stochastic repair times for multiperiod randomly failing equipment is developed. The main objective is to minimize the total maintenance cost by jointly finding the optimal preventive maintenance (PM) cycle and planning horizon.Design/methodology/approachA model based on the mathematical theory of reliability is developed to minimize the total maintenance cost by jointly finding the optimal couple: PM cycle T* and planning horizon H*. The proposed model aims to characterize the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures. The conventional threshold accepting (TA) algorithm is implemented to solve the proposed model. A numerical example for a given set of input parameters is presented in order to show the usefulness of the proposed model. A sensitivity analysis of some of the key parameters is performed to demonstrate the coherence of the developed maintenance policy.FindingsThe obtained results showed a sensitive trade-off between PM frequency and the total maintenance cost. Performing PM actions more frequently helps significantly to reduce the expected number of corrective maintenance actions and the corresponding total cost. It has also been found that improving the efficiency of the PM actions allows for maintaining the equipment less frequently by increasing the time between successive PM actions.Research limitations/implicationsGiven the complexity of the objective function to be minimized and the stochastic nature of the model's parameters, the authors limited this study to equally cyclic production periods over the planning horizon.Practical implicationsThe present model aims to provide an integrated maintenance/production comprehensive framework to assist planners in establishing maintenance schedules considering multiperiod randomly failing production systems and the evolutionary impact of imperfect PM actions on the equipment failure rate.Originality/valueContrary to the majority of existing works in the literature dealing with maintenance strategies, the authors consider that repair times are stochastic to provide a more realistic framework. In addition, the developed model considers the impact of imperfect maintenance on the equipment's mean time to failure. Thus, the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures is characterized. Simultaneously, the production planning horizon along with the length of each PM cycle is optimized in order to minimize the total maintenance cost over the planning horizon.

Continuous improvement of imperfect maintenance actions in PAS and PAR models

In this paper, an unavailability model is proposed to define the test and maintenance policies of systems that are subject to hidden failure and degradation, assuming practices of continuous improvement (CI) under imperfect maintenance actions with Proportional Age Setback (PAS) and Proportional Age Reduction (PAR). For this, two CI equations are analyzed to estimate the progress of maintenance effectiveness over time, and a maximum unavailability constraint is incorporated into the model for evaluation of safety point of view. A numerical application was performed, and the results showed that by adopting CI practices over maintenance actions, the unavailability of the system is reduced in greater proportion over time, besides contributing positively to safety.

A data-driven recurrent event model for system degradation with imperfect maintenance actions

Although a large number of degradation models for industrial systems have been proposed by researchers over the past few decades, the modeling of impacts of maintenance actions has been mostly limited to single-component systems. Among multi-component models, past work either ignores the general impact of maintenance, or is limited to studying failure interactions. In this article, we propose a multivariate imperfect maintenance model that models impacts of maintenance actions across sub-systems while considering continual operation of the unit. Another feature of the proposed model is that the maintenance actions can have any degree of impact on the sub-systems. In other words, we propose a multivariate recurrent event model with stochastic dependence, and for this model we present a two-stage approach which makes estimation scalable, thus practical for large-scale industrial applications. We also derive expressions for the Fisher information so as to conduct asymptotic statistical tests for the maintenance impact parameters. We demonstrate the scalability through numerical studies, and derive insights by applying the model on real-world maintenance records obtained from oil rigs. In the online supplemental material, we provide the following: (i) sketch of proof for likelihood, (ii) convergence analysis, (iii) contamination analysis, and (iv) a set of R codes to implement the current method.

Clustering condition-based maintenance for manufacturing systems with both perfect and imperfect maintenance actions

This paper integrates maintenance and product quality for a manufacturing system with three failure modes. The system is monitored and can be alerted by a prediction signal before any failure mode. Due to the high setup cost of maintenance activities, their clustering can be beneficial. To reduce the frequency of failures and their impacts on the quality of output products, a clustering model with perfect and imperfect maintenance actions is applied. The objective is to investigate the impacts of clustering maintenance actions on non conforming production costs and to compare cost saving in perfect and imperfect maintenances. Our analysis and results yield valuable insights into when and how to cluster maintenance operations so that the cost for maintenance and non conforming units be minimized. Clustering perfect maintenance can be more optimal than the imperfect one for several reasons, e.g., because perfect maintenance actions restore the system completely to the as-good-as new state and the time between two maintenance is larger, and thus the frequency of maintenance decreases.

Condition-based maintenance optimization for continuously monitored degrading systems under imperfect maintenance actions

Condition-based maintenance (CBM) is receiving increasing attention in various engineering systems because of its effectiveness. This paper formulates a new CBM optimization problem for continuously monitored degrading systems considering imperfect maintenance actions. In terms of maintenance actions, in practice, they scarcely restore the system to an as-good-as new state due to residual damage. According to up-to-data researches, imperfect maintenance actions are likely to speed up the degradation process. Regarding the developed CBM optimization strategy, it can balance the maintenance cost and the availability by the searching the optimal preventive maintenance threshold. The maximum number of maintenance is also considered, which is regarded as an availability constraint in the CBM optimization problem. A numerical example is introduced, and experimental results can demonstrate the novelty, feasibility and flexibility of the proposed CBM optimization strategy.

Selective maintenance modeling and analysis of a complex system with dependent failure modes

In this paper, we propose a statistical and optimization framework for selective maintenance of a complex system with components that have multiple failure modes. Specifically, we study the case where each component has multiple instantaneous hard failure modes and a single soft failure mode. An imperfect maintenance action reduces the effective age and degradation level of a component while increasing the mean degradation parameter and the failure rates of hard failures. A numerical example illustrates the application of the proposed framework and the effectiveness of the Genetic Algorithm and Differential Evolution heuristics in solving such maintenance optimization problems.

Multi-objective imperfect maintenance optimization for production system with an intermediate buffer

In several production systems, buffer stocks are built between consecutive machines to ensure the continuity of supply during interruptions of service caused by breakdowns or planned maintenance actions. However, in previous research, maintenance planning is performed individually without considering buffer stocks. In order to balance the trade-offs between them, in this study, an integrated model of buffer stocks and imperfective preventive maintenance for a production system is proposed. This paper considers a repairable machine subject to random failure for a production system by considering buffer stocks. First, the random failure rate of a machine becomes larger with the increase of the number of random failures. Thus, the renewal process is used to describe the number of random failures. Then, by considering the imperfect maintenance action reduced the age of the machine partially, a mathematical model is developed in order to determine the optimal values of the two decision variables which characterize the proposed maintenance strategy and which are: the size of the buffer stock and the maintenance interval. The optimal values are those which minimize the average total cost per time unit including maintenance cost, inventory holding cost and shortage cost, and satisfy the availability constraint. Finally, a heuristic procedure is used to solve the proposed model, and one experiment is used to evaluate the performance of the proposed methods for joint optimization between buffer stocks and maintenance policy. The results show that the proposed methods have a better performance for the joint optimization problem and can be able to obtain a relatively good solution in a short computation time.

The impact of imperfect maintenance actions on the degradation of buildings’ envelope components

Usually, maintenance models consider maintenance actions as perfect, i.e. these interventions can restore the components to the “as good as new” state. However, in many situations, this assumption is too simplistic, and moreover it is not true. In this study, imperfect maintenance actions are considered. For that purpose, a new methodology is developed to quantify the impact of maintenance actions on buildings' envelope components. This methodology is based on a probabilistic approach, implemented through a stochastic maintenance model based on Petri Nets (PN). The quantification of the impact of maintenance actions on building components is estimated by assessing the impact of these actions on the overall degradation condition of the component, which is expressed by a numerical index called severity of degradation index, Sw. This is a conceptual study, in which the maintenance plans are composed of condition-based maintenance actions, and the schedule of the actions is carried out based on the condition of the components. In the definition of the maintenance plans, four levels of interventions were considered: inspections; cleaning operations; minor interventions; and total replacement. The results reveal that the adoption of minor interventions or cleaning operations increases the operational costs but, when performed with an adequate periodicity, allows maintaining the components’ performance at high standards for most of their service life, thus promoting their durability.

Joint maintenance and spare parts inventory optimization for multi-unit systems considering imperfect maintenance actions

Joint maintenance and spare parts inventory optimization has attracted increasing attention in recent years because of its capability in addressing the maintenance planning and the spare parts provisioning of industrial systems simultaneously. However, imperfect maintenance (IM) actions are either neglected or over-simplified as constant improvements in existing studies, which reduces their practicality in industrial applications. To tackle this limitation, this paper investigates the joint maintenance and spare parts inventory optimization for multi-unit systems with the consideration of IM actions as random improvement factors. First, a two-step approximate derivation method is proposed, which overcomes the derivation difficulties of replacement numbers due to the introduction of random improvement factors and enables the construction of the inventory level transition relationship. Then based on the inventory level transition relationship, an expected total cost model is formulated via the finite horizon stochastic dynamic programming (FHSDP). The decision variables are optimized by the joint use of enumeration and the FHSDP. Finally, a numerical simulation of a wind farm is carried out for illustration. Sensitivity analyses are further conducted to study the influences of critical parameters.