Imperfect Maintenance Research Articles

Optimal Policies on Education Training for Employees Using Maintenance Modeling Aspects

Employee training is essential for corporate activities to improve their production efficiency and product quality. The most representative two types of employee training are known, i.e., on-the-job training and off-the-job training. Off-the-job training can be classified into two types, i.e., compulsory training and non-compulsory training. Safety training and compliance are known as compulsory training, and they are needed to ensure that daily work continues without serious problems. Compulsory education is undertaken in a classroom every year by all employees of a department or section. Daily e-learning is effective for complementing and enhancing mandatory education and it helps employees to remember what they have learned during their annual education. In this paper, we discuss optimal employee safety education models that are complemented and enhanced by e-learning. The expected cost rate of education is expressed using the imperfect maintenance model, and the optimal policies that minimize it are discussed.

A multi-phase Wiener process-based degradation model with imperfect maintenance activities

In order to improve the performance and prolong the life of the equipment, it is usually necessary to maintain the equipment in practice. Remaining useful life prediction with imperfect maintenance activities is of great importance to prognostics and health management. In this paper, a multi-phase Wiener process-based degradation model is constructed to characterize the degradation process subjected to imperfect maintenance activities. The beta distribution is introduced to describe the residual degradation coefficient caused by the imperfect maintenance activity. The hyper-parameters of residual degradation coefficient are estimated through the maximum likelihood estimation and Newton iteration method. To reflect the unit heterogeneity, the drift coefficient and diffusion coefficient of each phase are synchronously defined as random variables. Furthermore, the analytical forms of remaining useful life are obtained by the convolution operator. The approximate expression of probability density function is derived by the Monte Carlo simulation approach. In the end, a numerical study and a practical study of gyroscopes are provided to demonstrate the practicality and effectiveness of the proposed method.

Maintenance optimization considering the mutual dependence of the environment and system with decreasing effects of imperfect maintenance

A system state and its working environment can be mutually dependent, such as when the surrounding area is relatively confined, and there is a strong interaction between them. Maintenance strategies without considering this relationship can be ineffective. In this study, a novel maintenance strategy is developed for multi-state systems operating exposed to changing environment states, and the system and environment can influence each other. Also, the operation and maintenance costs, and the maintenance effects are influenced by the current system and environment. In this study, three actions are selectable at decision-making times, including do nothing, imperfect maintenance and replacement. Imperfect maintenance can improve the system state to some extent, but not fully. Then subsequently, the environment state also improves, which occurs with a time delay after the system improvement. In addition, the effect of imperfect maintenance decreases as the number of completed imperfective maintenance activities increases. Whenever the system is at its worst state, replacement is required. A Markov decision process is formulated to model the problem. By using a policy iteration algorithm, the maintenance strategy can then be optimized. Numerical examples are given to demonstrate the proposed method and its effectiveness, and sensitivity analyses of parameters are also performed.

Optimal bivariate spare ordering policy for a system with imperfect maintenance

In this paper, we investigate an ordering-replacement modelling framework for a system deteriorating with two failure types: a repairable failure (Type-I failure) and an unrepairable failure (Type-II failure). Repairable failures are followed by minimal repair, whereas unrepairable failures are removed by a corrective replacement. A spare unit ordered regularly is associated with the system's operation time T and the number N of successive working times for preventive replacement, or ordered in emergency at any unrepairable failure for corrective replacement, whichever is first to happen. Three kinds of objectives – system availability, expected cost rate, and cost-effectiveness – are optimised to seek for the optimal spare unit ordering schedule (T*, N*), respectively. The existence and uniqueness of each optimal ordering policy are derived analytically and computed numerically.

Integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints

Integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints

Multi-component system maintenance optimization of a rail transit train based on opportunistic correlations

Abstract In order to deal with the problems of insufficient or excessive maintenance in the current maintenance activities of China transit trains, this paper develops a novel multi-component system maintenance optimization approach based on an opportunistic correlation model. Based on the minimal reliability and failure rate change rule of each train component, the novel proposed maintenance optimization benefits from an improved opportunistic maintenance model with system structure correlation, fault correlation and reliability correlation under imperfect maintenance. Then, different maintenance modes can be determined by a proposed maintenance factor under the different conditions of components. Specifically, the reliability threshold of each component is also considered to optimize the maintenance cost by the system reliability and operational availability of the train. Furthermore, as the mentioned problem belongs to the NP-Hard optimization problems, a modified particle swarm optimization (PSO) with the improvement of inertia weight is proposed to cope with the optimization problem. Based on a specific case under the practical recorded failure data, the analysis shows that the proposed model and approach can effectively cut the maintenance cost.

Selective Maintenance Strategy for a Finite Planning Horizon Considering Imperfect Maintenance

Most selective maintenance research strategies ignore the comprehensive evaluation of numerous following missions in favor of focusing just on the reliability of the next mission. In the same circumstance, however, assessing simply impending missions differs from considering the overall system’s maintenance planning outcomes. In this study, a decision‐making model for selective maintenance is developed by evaluating the total system reliability over a finite planning horizon. The purpose is to calculate the maintenance activities for each system component during each break and the best number of maintenance interruptions for the planning horizon to achieve maximum system reliability. Consequently, the selective maintenance problem is formulated as a max–min optimization model. Also, a hybrid imperfect maintenance model is used to formulate the component improvement after maintenance. Finally, simple case illustrations of maintaining the production system in coal transportation are given based on the assumed data.

Remaining Useful Lifetime Prognostic Approach for Stochastic Degradation Equipment Considering Imperfect Maintenance Activities

Remaining Useful Lifetime Prognostic Approach for Stochastic Degradation Equipment Considering Imperfect Maintenance Activities

Imperfect Maintenance Model for Optimizing Air Compressor Availability

Imperfect Maintenance Model for Optimizing Air Compressor Availability

Simulation-based Reliability Evaluation of Maintenance the Efficiency of A Repairable System

The aim of this paper is to study the asymptotic behavior of the Arithmetic Reduction of Intensity (ARI) and Arithmetic Reduction of Age (ARA) models as two imperfect maintenance models. These models have been proposed by Doyen & Gaudoin (2011), the failure process with bathtub failure intensity. The maintenance effect is characterized by the change induced by the failure intensity before and after a failure during the degradation period. To simplify the study, the asymptotic properties of the failure process are derived. Then, the asymptotic normality of several maintenance efficiency estimators can be proved in the case where the failure process without maintenance is known. Practically, the coverage rate of the asymptotic confidence intervals issued from those estimators is studied.

Improved maintenance strategy for the wind turbine system under operating and climatic conditions

This paper studies and proposes a novel joint policy of production, imperfect and priority maintenance for a wind turbine system connected to battery storage and supply grid. The failure rate of wind farm is closely related to production rate and working time. The objective of this paper is to establish an economical energy production and imperfect maintenance plans minimizing the various costs incurred, taking into account the electricity demand variation, uncertainty of wind velocity, and the service level. The proposed maintenance strategy based on the priority and selective maintenance actions aims to choose the priority components for maintenance, while minimizing the total maintenance cost and ensuring a minimum reliability level for the wind turbine system. To achieve the latter goal, we formulate the reliability model of the wind turbine components by considering the influence of operating and environmental conditions. Numerical examples and sensitivity analyzes are presented to illustrate the significance and the effectiveness of the proposed methodology.

Joint Optimization of Maintenance and Spare Parts Inventory for Multi-Unit Systems With a Generalized Structure

Abstract Joint optimization of maintenance operations and spare parts inventory is promising for ensuring the reliable and efficient operation of industrial systems. Extensive studies have been conducted on joint optimization emphasizing various aspects. However, those studies still suffer two limitations. (1) The considered system structures are restricted to particular cases, and thus are unable to fully address the joint optimization needs of actual industrial systems possessing various structures, and (2) imperfect maintenance, as has been commonly adopted in industries, is not addressed in existing works, which compromises the economic efficiency of the joint policy. Targeted at these limitations, this paper investigates the joint optimization for multi-unit systems with a generalized structure, i.e., the systems consist of multiple identical units, and each unit possesses a complex subsystem structure. First, a total cost rate model is formulated, in which maintenance actions include both perfect replacement and imperfect maintenance, and the spare parts inventory policy consists of an (s, S) policy and the option to place emergency orders. Based on the formulated model, a simulation optimization method is proposed. The simulation module evaluates the total cost rate iteratively, and the optimization module utilizes a variable neighborhood search-based algorithm to obtain a high-quality joint policy with tractable computation times. Two numerical examples are provided for demonstration, and sensitivity analyses are conducted to study the effects of parameters.

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.

Hybrid Discrete Differential Evolution and Deep Q-Network for Multimission Selective Maintenance

The multimission selective maintenance problem (MSMP) for repairable systems has received increasing attention in recent years. The problem amounts to selecting a subset of feasible maintenance actions, in view of the resource limitations. For considering the realistic case of the imperfect maintenance, this article introduces a hybrid imperfect maintenance model, which is more realistic to evaluate the system reliability. The challenge of solving such kind of problems lies not only in the reliability estimation, but also in the solution method of the maintenance selection. Such decision-making problem can be effectively formulated using the Markov decision process, but it is difficult to apply current methods for solving the engineering systems with large action decision spaces. In order to solve this issue, this work puts forth a novel hybrid algorithm for the MSMP in a large multicomponent system. In the proposed method, a discrete differential evolution algorithm is developed for searching the optimal maintenance action in large-scale discrete action spaces and the deep Q-network method is utilized to approximate the effectiveness of maintenance actions and facilitate the agent training. The experiments, based on a large-scale coal transportation system, verify the effectiveness of the proposed method compared with LSDQN and differential evolution.

Replacement and Repair Optimization for Production Systems Under Random Production Waits

This study considers a repairable production system operated under an age-based preventive replacement policy that is subject to independent random production waits and failures. In addition to the age-based replacement policy, we propose a maintenance model that uses production waits to schedule preventive replacement. That is, a decision maker can preventively replace the system either during a production wait or at the age threshold, whereas if a failure occurs during production, the decision maker must decide whether to perform a minimal repair or a corrective replacement to restore the system. Under the above setting, we develop a semi-Markov decision process (SMDP) to obtain the optimal maintenance policy that minimizes the long-run average maintenance cost rate. We establish the existence of the optimal maintenance policy and provide an algorithm to numerically obtain the optimal action for each state. We further generalize the model to incorporate imperfect maintenance and the nonhomogeneous arrival of production waits. In the latter case, it is computationally intractable to optimize the SMDP using a value iteration algorithm due to the curse of dimensionality. To address this challenge, we further develop an approximate dynamic programming framework to generate high-quality solutions. An attractive feature of our model is its generality, such that the model includes many existing maintenance models as special cases. A real-world example from a steel factory is used to demonstrate the proposed model.

General Independent Competing Risks for Maintenance Analysis

Abstract Repairable systems are submitted to corrective maintenance and condition-based preventive maintenance actions. Condition-based preventive maintenance occurs at times which are determined according to the results of inspections and degradation or operation controls. The generalization of the models suggested makes it possible to integrate the dependence between corrective and preventive maintenances. In order to take into account this dependency and the possibility of imperfect maintenances, generalized competing risks models have been presented in Doyen and Gaudoin (2006). In this study, we revise the general case in which the potential times to next corrective and preventive maintenance are independent conditionally to the past of the maintenance process. We address the identifiability issue and we find a result similar to that of Zhou, Lu, Shi and Cheng (2018) for usual competing risks. We propose realistic models with exponential risks and derive their likelihood functions.

Optimizing inspection plan for corroded pipeline with considering imperfect maintenance

Metal-loss corrosion may result in pipeline’s failure and expensive downtime loss. Thus, to keep the pipeline’s normal operation, it is crucial to draw up scientific inspection and maintenance plans. This study is to optimize the inspection plan for corroded pipeline. Limit state functions are established for the corrosion leakage and burst respectively, and the pipeline’s failure probability is obtained with Monte Carlo simulation. The pipeline’s failure probability is further evaluated by the copula function and with considering the correlation of different failure modes. Moreover, a hybrid failure rate model is developed to update the pipeline’s failure probability, where the age reduction factor and failure rate increase factor are adopted, and imperfect maintenance is taken into account. On this basis, an optimization model is established with the objective to minimize the total maintenance cost, and genetic algorithm is applied to optimize the inspection plans. A case study is conducted, and the optimal results are analyzed according to acceptable maximum failure probability characteristics for the areas with different risk levels. The results show that periodic inspection and perfect maintenance are suitable for high-risk areas, and the proposed inspection plan is more suitable for corroded pipeline in low or medium-risk areas.

Joint Optimization Strategy of Condition-Based Maintenance and Spare Parts Ordering for Nonlinear Degraded Equipment under Imperfect Maintenance

When existing methods consider the impact of the imperfect maintenance activities on random degraded equipment, they usually assume that the degraded equipment is linear, and set the number of imperfect maintenance activities in advance. However, in engineering practice, most of the degraded equipment is nonlinear, and the number of imperfect maintenance activities has an impact on the development of maintenance strategies. Therefore, this paper proposes a new joint maintenance strategy considering the uncertainty of the number of imperfect maintenance activities for nonlinear degraded equipment. First, nonlinear degradation data are linearized based on Box-Cox transformation (BCT), and the degradation model under the influence of imperfect activities is constructed by the random coefficient regression model. Accordingly, the remaining useful life (RUL) probability distribution can be derived. Secondly, the number of maintenances is calculated by imperfect maintenance level and success probability. Then, the detection cycle, preventive maintenance threshold and maintenance times are taken as decision variables. The optimization objective is to minimize the expected average cost. However, this is restricted by availability and probability of success for imperfect maintenance activities. A multi-objective joint optimization model of condition-based maintenance and spare parts ordering is constructed. Finally, the analysis results based on numerical examples verify the feasibility of the proposed joint optimization strategy.

Imperfect maintenance policies for warranted products under stochastic performance degradation

Both customers and manufacturers benefit from warranties that effectively guarantee products’ performance with reasonable price and cost. Reliability-oriented degradation analysis has boosted the accuracy of lifetime prediction in the past decades and prompted additional maintenance and warranty options to manufacturers. In this paper, we present a systematic framework that aims to optimize imperfect maintenance policies for warranted products subject to stochastic performance degradation. We propose a novel objective-oriented imperfect repair scheme, and consider two scenarios corresponding to different types of products/systems. In the first one, warranties are claimed by customers in a random manner driven by performance degradation, whereas in the second one, the manufacturer offers periodic inspection services to customers and implements imperfect repairs if necessary. In both scenarios, we derive critical closed-form results to facilitate computation and optimization, from which managerial insights are gleaned. We find that (i) the randomness in customers’ claiming behaviors leads to remarkably more conservative warranty policies in which repairs are suggested to be more substantial; and (ii) the variable objective repair level in the imperfect repair exercise can effectively reduce the total maintenance cost over a finite warranty period. Overall, this work will equip practitioners with quantitative tools to prescribe optimal maintenance policies for warranted degrading products in various practical scenarios.

Optimal block replacement policy for two-dimensional products considering imperfect maintenance with improved Salp swarm algorithm

Abstract Human society is entering Industry 4.0. Engineering systems are becoming more complex, which increases the difficulties in maintenance support work. Maintenance plays a very important role in the entire life cycle of a system, and in reality, maintenance is not always perfect, and its maintenance degree is between good as new and bad as old, which should be considered in the maintenance strategy. Under the framework of two-dimensional warranty, this work proposes an optimal two-dimensional block replacement strategy based on the minimum expected warranty cost. Two-dimensional block replacement maintenance is imperfect maintenance. The failure rate reduction method is used to describe the maintenance effect of two-dimensional block replacement. In case analysis, the grid search algorithm, genetic algorithm, particle swarm optimization algorithm, and improved salp swarm algorithm (SSA) are used to find the optimal warranty scheme for the laser module. The improved SSA can converge faster and find a warranty scheme that makes the warranty cost lower. Therefore, managers can use these results to reduce costs and get a win-win extended warranty cost. Rationalization suggestions are put forward for managers to make maintenance decisions through comparative analysis and sensitivity analysis.