Periodic Preventive Maintenance Model Research Articles

Cost-oriented predictive maintenance based on mission reliability state for cyber manufacturing systems

With the advent of Industry 4.0, maintenance strategy faces new demands to avoid the hysteresis of the conventional passive maintenance mode and the non-feasibility of the periodic preventive maintenance model. In view of the inherent polymorphism of manufacturing systems and with the objective of maximizing benefits, a novel cost-oriented predictive maintenance based on mission reliability state for manufacturing systems is proposed. First, the cyber-physical system is adopted to organize and analyze big data in the operational process of manufacturing systems in terms of predictive analytics in cyber manufacturing environment. Second, a new connotation of mission reliability is defined based on the big operational data to comprehensively characterize the dynamic state of the equipment health states and the qualified degree of the production task. Third, the predictive maintenance mode based on mission reliability state is quantified by the comprehensive cost, and the relationship between mission reliability and cost is established. Thereafter, cost-oriented dynamic predictive maintenance strategy is proposed. Finally, a case study on the maintenance decision-making problem of a cylinder head manufacturing system is presented. The final result shows that the comprehensive cost can be further reduced by the proposed method relative to the traditional periodic preventive maintenance strategy.

Sustainable Maintenance: a Periodic Preventive Maintenance Model with Sustainable Spare Parts Management

Industrial companies are among the largest responsible of materials and energy flows in an environment where natural resources are running out and impacts of human activities are still rising. Consequently, in the last few years, research is focusing on the new paradigm of sustainable manufacturing, which aims to develop sustainable production processes, innovative technologies, and new tools for evaluating economic, environmental, and social impacts of industrial assets. In this context, maintenance process, necessary to ensure availability, reliability, and safety of industrial assets, could become one of the main pillars for sustainable manufacturing. The purpose of the paper is to provide a periodic preventive maintenance model that establishes the optimal maintenance period for each system component, which minimizes conventional, environmental, and social costs generated by maintenance interventions. The model, moreover, integrates the concept of Circular Economy, choosing the most suitable spare parts to use in maintenance activities from a sustainable perspective. The model is applied on a part of a pasta production plant obtaining a maintenance plan, which evidences economic, environmental, and social benefits and confirms the necessity to introduce sustainability considerations into conventional maintenance procedures.

Optimal maintenance strategy under renewable warranty with repair time threshold

In this paper, we consider a periodic preventive maintenance model, from the manufacturer's perspective, which can be implemented to reduce the maintenance cost of a repairable product during a given warranty period. The product is assumed to deteriorate with age and the warranty policy we adopt in this paper takes into account the two factors of failure time and repair time of the product when the product failure occurs. Under the proposed two-factor warranty, a repair time threshold is pre-determined and if the repair takes more time than that of the threshold, the failed product is replaced with a renewed warranty policy. Otherwise, the product is only minimally repaired to return to the operating state. During such a renewable warranty period, preventive maintenance is conducted to reduce the rate of degradation periodically while the product is in operation. By assuming certain cost structures, we formulate the expected warranty cost during the warranty period from the manufacturer's perspective when a periodic preventive maintenance strategy is adapted. Although more frequent preventive maintenance increases the warranty cost, the chance of product failures would be reduced. The main aim of this paper is to accomplish the optimal trade-off between the warranty cost and the preventive maintenance period by determining the optimal preventive maintenance period that minimizes the total expected warranty cost during the warranty period. Assuming the power law process for the product failures, we illustrate our proposed maintenance model numerically and study the impact of relevant parameters on the optimal preventive maintenance policy.

A Failure-rate-reduction Periodic Preventive Maintenance Model with Delayed Initial Time in a Finite Time Period

HeA new machine will not fail easily in the early stage of its useful life. This phenomenon is consistent with the fact that the optimal sequential preventive maintenance (PM) policy which has longer PM intervals in its earlier stage of lifetime. In this paper, we propose a failure-rate-reduction periodic PM model with delayed initial time in a finite time span. Then, the optimal periodic PM policy is developed by minimizing the expected total maintenance cost, which can have smaller expected total maintenance cost than the optimal policy of the original failure-rate-reduction periodic PM model. The algorithm of finding the optimal PM policy for the proposed PM model is developed. Finally, examples are illustrated to verify the optimal policies of the proposed new PM model.

Improved particle swarm optimization to minimize periodic preventive maintenance cost for series-parallel systems

This study minimizes the periodic preventive maintenance cost for a series-parallel system using an improved particle swam optimization (IPSO). The optimal maintenance periods for all components in the system are determined efficiently. Though having advantages such as simple understanding and easy operation, a typical particle swam optimization (PSO) is easily trapped in local solutions when optimizing complex problems and yields inferior solutions. The proposed IPSO considers maintainable properties of a series-parallel system. The importance measure of components is utilized to evaluate the effects of components on system reliability when maintaining a component. Accordingly, the important components form superior initial particles. Furthermore, an adjustment mechanism is developed to deal with the problem in which particles move into an infeasible area. A replacement mechanism is implemented that replaces the first n particles ranked in descending order of total maintenance cost with randomly generated particles in the feasible area. The purpose of doing so is overcome the weakness in that a typical PSO is easily trapped in local solutions when optimizing a complex problem. An elitist strategy is also applied within the IPSO. Additionally, this study employs response surface methodology (RSM) via systematic parameters experiments to determine the optimal settings of IPSO parameters. Finally, a case demonstrates the effectiveness of the proposed IPSO in optimizing the periodic preventive maintenance model for series-parallel systems.

A Generalized Periodic Preventive Maintenance Model With Virtual Age for a System Subjected to Shocks

This article presents a periodic preventive maintenance (PM) model for a system subjected to random shocks. A system is subject to shocks that arrive according to a non homogeneous Poisson process. As shocks occur, the system experiences one of two types of failures: Type-I failure (minor) and Type-II failure (catastrophic). Type-I failures are rectified by a minimal repair. In a PM period, the system is preventively maintained following the occurrence of a Type-II failure or at age T, whichever takes place first. At the Nth PM, the system is replaced. An approach that generalizes the existing studies on the periodic PM policy is proposed. Taking the shock number-dependent failure type and the virtual age into consideration, the object consists of determining the optimal PM and replacement schedules that minimize the expected cost per unit of time, over an infinite time horizon.

A hybrid genetic algorithm to minimize the periodic preventive maintenance cost in a series-parallel system

This study presents a hybrid genetic algorithm (GA) to optimize the periodic preventive maintenance model in a series-parallel system. The intrinsic properties of a repairable system, including the structure of reliability block diagrams, maintenance priority of components, and their maintenance periods, are considered in developing the proposed hybrid GA. The importance measure of components is employed to account for these properties, identify important components, and determine their maintenance priorities. The optimal maintenance periods of these important components are then determined to minimize total maintenance cost given the allowable worst reliability of a repairable system using the GA search mechanism. An elitist conservation strategy is applied to retain superior chromosomes in the iterative breeding process to accelerate the approach toward the global optimum. Furthermore, the response surface methodology is utilized to systematically determine crossover probability and mutation probability in the GA instead of using the conventional trial-and-error process. A case study demonstrates the effectiveness and practicality of the proposed hybrid GA in optimizing the periodic preventive maintenance model in a series-parallel system.

THE PERIODIC MAINTENANCE POLICY FOR A WEIBULL LIFE-TIME SYSTEM WITH DEGRADATION RATE REDUCTION UNDER RELIABILITY LIMIT

From the literature, it is known that preventive maintenance (PM) can reduce the deterioration of system or equipment to a younger level. Researchers usually develop optimal PM policies based on the assumption that the PM can reduce system's age or failure rate. However, the PM actions, such as cleaning, adjustment, alignment, and lubrication work, may not always reduce system's age or failure rate. Instead, it may only reduce the degradation rate of the system to a certain level. In addition, most of the existing optimal PM policies are developed by minimizing the expected cost rate only. Yet, as demonstrated in this paper, the system will have very low reliability at the time of preventive replacement if the reliability limit is not considered. Hence, this paper is to develop an optimal periodic PM model by minimizing the expected cost rate per unit time with the consideration of reliability limit for repairable systems with degradation rate reduction after each PM. The improvement factor method is used to measure the reduction effect of the degradation rate. The algorithm for searching the optimal solutions for this PM model is developed. Examples are also presented with discussions of parameter sensitivity and special cases.