Periodic Preventive Maintenance Research Articles

Deep Reinforcement Learning for Distributed Flow Shop Scheduling with Flexible Maintenance

A common situation arising in flow shops is that the job processing order must be the same on each machine; this is referred to as a permutation flow shop scheduling problem (PFSSP). Although many algorithms have been designed to solve PFSSPs, machine availability is typically ignored. Healthy machine conditions are essential for the production process, which can ensure productivity and quality; thus, machine deteriorating effects and periodic preventive maintenance (PM) activities are considered in this paper. Moreover, distributed production networks, which can manufacture products quickly, are of increasing interest to factories. To this end, this paper investigates an integrated optimization of the distributed PFSSP with flexible PM. With the introduction of machine maintenance constraints in multi-factory production scheduling, the complexity and computation time of solving the problem increases substantially in large-scale arithmetic cases. In order to solve it, a deep Q network-based solution framework is designed with a diminishing greedy rate in this paper. The proposed solution framework is compared to the DQN with fixed greedy rate, in addition to two well-known metaheuristic algorithms, including the genetic algorithm and the iterated greedy algorithm. Numerical studies show that the application of the proposed approach in the studied production-maintenance joint scheduling problem exhibits strong solution performance and generalization abilities. Moreover, a suitable maintenance interval is also obtained, in addition to some managerial insights.

Optimal Periods of Conducting Preventive Maintenance to Reduce Expected Downtime and Its Impact on Improving Reliability.

The present research mainly aims to use a mathematical formula to determine the optimal intervals for conducting preventive maintenance operations for machines to reduce the expected failure time when the malfunction data follow the Weibull distribution. The reliability function, failure rate, and the average time between machine failures were derived after performing preventive maintenance operations and before conducting preventive maintenance operations to state the amelioration that happens to machines. These rely on real data of performing preventive maintenance operations and the downtime required to repair machine or device faults that occur between preventive maintenance periods and the downtime necessary to perform preventive maintenance operations on the machine or device. Thus, the study concluded that preventive maintenance operations are working to increase the reliability of the machine and improve it, as well as to increase the average period of time for the machine to operate between faults.

Nanotechnology Applications towards Sustainable Road Surface Maintenance and Effective Asset Protection, Generating Rapid Employment Opportunities in a Post COVID-19 Era

The developing world has been faced with high rates of unemployment, exacerbated by extended enforced lockdowns due to the pandemic. Pressure is mounting for drastic intervention to accelerate economic growth and to provide employment opportunities. Most of these countries are faced with inadequate road transport facilities in support of economic growth. The construction of high-order roads in support of economic growth requires high degrees of compliance with limited opportunities for increased labour content. However, many of the existing surfaced roads are notoriously lacking periodic preventative maintenance operations needed to preserve the integrity of road surfaces to protect pavement structures against water ingress and resultant rapid deterioration. This article demonstrates the ability of available, proven nanotechnologies to restore the water-resistant properties of already compromised road surfacings. It is shown that traditionally used road products can substantially be improved (in terms of strength properties and resistance to environmental factors) through the addition of applicable nanotechnology modifiers. These modified products can be applied at ambient temperatures, ideally suited for labour intensive applications as demonstrated, showing several examples of actual applications. A combination of modified existing technologies is recommended to partially restore severely compromised road surfacings, especially applicable to secondary and tertiary urban road networks. The implementation of the recommended restoration programmes can go a long way towards road asset preservation, while simultaneously addressing the urgent need for rapid employment generation.

Preventive Maintenance Strategy of Environmental Test Chamber Based on Particle Swarm Optimization Algorithm

With the development of various physical industries, people pay more attention to reliability tests and test equipment. To solve the problem of making maintenance strategy of an environmental test chamber for reliability test, a periodic preventive maintenance strategy based on RCM(Reliability Centre Maintenance) is proposed. Firstly, a multi-objective optimization model of reliability and maintenance cost is established by combining reliability theory and life distribution theory, and two objectives of equipment reliability and maintenance cost are considered. Secondly, the actual environmental test chamber fault maintenance data is analyzed, and it is found the fault distribution meets the dual parameter Weibull. Finally, the particle swarm optimization algorithm is used to solve the multi-objective model optimization, and a series of Pareto optimal solutions are obtained, that is, the number of maintenance times and the corresponding time interval in the operation cycle of the environmental test chamber, and these solutions might be good references for maintenance management personnel.

Joint preventive maintenance and extended warranty strategy for leased unreliable equipment submitted to imperfect repair at failure

This paper examines the maintenance policy and the length of the extended warranty for leased equipment from the lessor's perspective. When the equipment fails within the base and extended warranty periods, imperfect repairs are carried out with a given efficiency following a decreasing quasi-renewal process. To reduce the number of failures, the lessor performs imperfect periodic preventive maintenance (PM) actions restoring the equipment to the state ‘as good as new’ with probability p and keeping it at state ‘as bad as old’ with probability q. A mathematical model and a numerical algorithm are developed to jointly determine the optimal PM period T* and the optimal extended warranty period we* maximizing the lessor's expected total profit over a finite time horizon. A numerical example is presented to illustrate the use of the developed model.

Determination of optimal post-warranty variable maintenance period for repairable second-hand product based on mean residual life

Due to the increased transactions of second-hand products in the market, the optimization of maintenance strategy for the second-hand product has become very important issue to attract a great attention from many researchers of late. This paper proposes a new post-warranty strategy with a variable self-maintenance period for the second-hand product, assuming that the product is replaced by another one on the first failure following a fixed length of post-warranty self-maintenance period. During the non-renewing warranty period, the product is subject to preventive maintenance periodically at a prorated cost while only minimal repair is implemented at each failure by the dealer. The main goal of this study is to determine an optimal length of post-warranty self-maintenance period which minimizes the expected cost rate per unit time during the product’s life cycle from the user’s perspective. This approach considers not only the periodic preventive maintenance during the warranty period, but also the remaining life distribution of the product after the warranty expires, which is the significant difference of this work from many existing maintenance policies. For this purpose, we formulate the expected length of life cycle and evaluate the expected total cost incurred during the life cycle of the second-had product which is purchased at the age of [Formula: see text] The existence of the optimal self-maintenance period is proved analytically under mild conditions and the proposed maintenance model is compared with an existing model with regard to the expected cost rate. Finally, assuming that the life distribution of the product follows a Weibull distribution, the effect of relevant parameters on the optimal self-maintenance period is analyzed numerically.

Hybrid dynamic risk modelling for safety critical equipment on offshore installations

Safety-critical equipment (SCE) are important safety barriers installed on offshore installations to prevent the occurrence as well as mitigate the consequence of major accidents. Risk assessment is significant for SCE in consideration that the risk of major accidents is increased due to the harsh operational environment and the hazards introduced by periodical preventive maintenance (PM). However, it is difficult to access risk precisely or robustly as the dynamic characteristics of SCE degradation and the influence of human error can increase the uncertainties. In view of this, this study proposes a new hybrid dynamic risk modelling methodology for SCE on offshore installations to capture the causality and dynamic dependencies precisely, where dynamic Bayesian network (DBN) technique and support vector regression (SVR) algorithm are combined. The impact of human error is evaluated through a complex structure modelling of risk influence factors (RIFs) in DBN. The hybrid SVR-DBN methodology is applied in the case study to assess the dynamic risk profile of a typical SCE (pressure safety valve, PSV) in the context of operation and maintenance respectively. The effectiveness of the hybrid SVR-DBN methodology is verified and the synergy from the integration of SVR and DBN overcomes the limitations of traditional DBN for modelling actual dynamic dependency between dynamic nodes in jacent time-slices. Besides, the modelling of RIFs introduced in DBN facilitates the assessment of human error. The hybrid SVR-DBN methodology is a systematic tool for the dynamic risk assessment of SCE on offshore installations in consideration of both the dynamic characteristics of SCE degradation and the influence of human error.

Tabu search for a parallel-machine scheduling problem with periodic maintenance, job rejection and weighted sum of completion times

We consider in this work a bicriteria scheduling problem on two different parallel machines with a periodic preventive maintenance policy. The two objectives considered involve minimization of job rejection costs and weighted sum of completion times. They are handled through a lexicographic approach, due to a natural hierarchy among the two objectives in the applications considered. The main contributions of this paper are first to present a new problem relevant to practice, second, to develop a mixed-integer-linear-program model for the problem, and third, to introduce two generalizable tabu-search metaheuristics relying on different neighborhood structures and solution spaces. Computational results for 120 instances (generated from a real case) are reported to empirically demonstrate the effectiveness of the proposed metaheuristics.

Study on Adaptive Cycle Life Extension Method of Li-Ion Battery Based on Differential Thermal Voltammetry Parameter Decoupling

Battery aging leads to reduction in a battery’s cycle life, which restricts the development of energy storage technology. At present, the state of health (SOH) assessment technology, which is used to indicate the battery cycle life, has been widely studied. This paper tries to find a way to adjust the battery management system adaptively in order to prolong the battery cycle life with the change of SOH. In this paper, an improved Galvanostatic Intermittent Titration Technique (GITT) method is proposed to decouple the terminal voltage into overpotential (induced by total internal resistance) and stoichiometric drift (caused by battery aging, indicated by OCV). Based on improved GITT, the open circuit voltage-temperature change (OCV-dT/dV) characteristics of SOH are described more accurately. With such an accurate description of SOH change, the adaptive method to change the discharge and charge cut-off voltage is obtained, whose application can prolong battery cycle life. Experiments verify that, in the middle of a battery’s life-cycle, the adaptive method to change the discharge and charge cut-off voltage can effectively improve the cycle life of the battery. This method can be applied during the period of preventive maintenance in battery storage systems.

Nonintrusive Ventilation System Diagnostics

Ventilation systems depend on periodic preventive maintenance. Unfortunately, mission critical systems, exposed to varying levels of particulates and other contaminants, may require unpredictable and highly variable maintenance intervals. Direct sensing for pressure drops or other condition metrics can provide prognostic indicators, but at the expense of the installation and maintenance burden of the sensors themselves. Rotor slot harmonics from motors have been observed for decades to track motor speed. This paper presents hardware instrumentation and a signal processing algorithm that can nonintrusively track the rotor slot harmonics of multiple fan motors in an aggregate electrical stream. Combined with a physics-based model of the motors under observation, this nonintrusive data can be used to track the maintenance condition of multiple fan motors on a single electrical service, minimizing the costs of installation and data analysis and reducing the total number of sensors necessary to track fan health. Results are demonstrated with experiments on-board a US Coast Guard Cutter.

Preventive maintenance period decision for elevator parts based on multi-objective optimization method

Elevator maintenance plays an important role in the safe and reliable operation of elevators. However, elevator maintenance is carried under some relevant norms and standards at present, and little research has been conducted to explore how to analytically make maintenance period decision for elevator parts. In addition, the unreasonable selection of the elevator maintenance period will cause excessive cost and low equipment operation efficiency. A decision-making method for elevator parts' maintenance is provided in this paper, which aims to solve the problem of seeking a reasonable maintenance cycle for elevator parts. First of all, the historical fault data of elevator parts is used to analyze their fault distribution law. In order to accurately reflect the changes of parts after maintenance and repair, the age reduction factor and the failure rate increasing factor are introduced to establish a mixed failure rate model. Accordingly, the cost rate and utilization rate model are established under the comprehensive consideration of cost and time factors in the maintenance process. Furthermore, a multi-objective optimization model is built with maintenance frequency as the decision variable. Then, Monte Carlo simulation method is used to solve the optimization model, and the best maintenance period of the parts within the service time is obtained. Finally, the proposed method is applied to the elevator parts to find out their optimum maintenance frequency, and the results show the effectiveness of the method. A reasonable maintenance period can not only reduce the risk of equipment failure, but also save costs so as to avoid excessive waste of resources.

Assessing the Effect of Reliability-Based Maintenance Approach in Job Shop Scheduling with Setup Time and Energy Consideration Using Simulation; A Simulation Study

ABSTRACT Maintenance in a stochastic job shop scheduling environment significantly impacts real-time scheduling problems with the environmental aspect. This simulation research assesses the effect of a reliability-based preventive maintenance approach to system performance for considering job shop scheduling problems with sequence-dependent setup time (SDST). Two types of reliability-based maintenance approaches are considered, i.e., reliability-centered preventive maintenance (RCPM) and a reliability-centered periodic preventive maintenance approach (RCPPM). The shop comprises 10 different machines and six job types. Six scheduling and two energy-oriented performance measures (Pms) are considered for evaluating the system’s performance. Results reveal that lower levels of reliability, namely, 0.74, 0.78, and 0.82 recommended for mean flow time, makespan, average operation energy consumption, average idle energy consumption, and total setups Pms. A 0.74 level of reliability is recommended for mean tardiness and the number of tardy jobs Pms for the RCPM approach. RCPPM approach provides the best system Pms by mean flow time, makespan, mean tardiness, average operation energy consumption, average idle energy consumption, and the number of tardy jobs when maintenance time is 5% of operation time. In contrast, mean setup, and total setups Pms are independent of maintenance time. Both approaches get compared, and its statistical analysis shows that if maintenance time is 15% or more than 15% of the operation time, the RCPM method is recommended. If maintenance time is 10% or less than 10% of the operation time, maintenance planning is recommended using the RCPPM approach. Considering real-time scheduling work with the environmental aspect represents the novelty of the present study.

발전설비 대형펌프의 신뢰성 향상을 위한 설비 상태지수 연구

Power generation facilities carry out periodic preventive maintenance to ensure smooth generation of electricity. However, frequent maintenance results in considerable losses such as suspension of operation and unnecessary replacement of parts. In order to overcome these issues, it has been proposed to develop an asset health index to reduce the maintenance and economic costs. To improve the reliability of the asset soundness index assessment, this study considers pump efficiency and conducts a state assessment study. Based on the results of the improved asset soundness index assessment, important information is expected to be provided for developing an efficient maintenance plan.

Application of Hierarchical Colored Petri Nets for Technological Facilities’ Maintenance Process Evaluation

The high reliability of modern engineering systems is achieved by performing predictive maintenance. Mathematical models based on stochastic timed colored Petri nets are an effective tool for developing complex production processes for Industry 4.0. This article discusses the maintainability evaluation used in hierarchical Petri net models. The hierarchical simulation model was built using timed colored Petri nets, and was constructed with four levels of repair and maintenance modules. New module structures are proposed for simulating the schedule of production tasks and interaction with technological units. The emphasis is on the processes of predicting maintenance and repair, moving units to service, replacing units, and forming a reserve. The design of the simulation modules allows the setting of probabilistic parameters for the distributions of equipment failures, requests for unit maintenance, repair time, and recovery time after repair. The article proposes to use the hierarchical Petri model in conjunction with solving the problem of minimizing the cost of service. The iterative procedure consists of obtaining an approximate unit distribution by tasks, subsequent simulation of the technological process, and adjusting the optimization problem constraints. For example, the hierarchical Petri net is considered to assess the maintainability of autonomous agricultural vehicles. The results of the simulation experiments are presented. A simulation of the agrotechnical production process was performed, during which vehicles were maneuvered, taken out for repair or maintenance, and returned to the reserve fund. The interdependencies of preventive maintenance periods, service operations, failure rates, and predictive maintenance requests were obtained in order to comply with the task scheduling. The proposed model is a generalization, but it is especially effective in studying mobile equipment servicing.

Optimal maintenance and warranty strategy for second-hand product with periodic preventive maintenance action

Optimal maintenance and warranty strategy for second-hand product with periodic preventive maintenance action

Single-Machine Scheduling with Fixed Periodic Preventive Maintenance to Minimise the Total Weighted Completion Times

The single-machine scheduling problem with fixed periodic preventive maintenance, in which preventive maintenance is implemented periodically to maintain good machine operational status and decrease the cost caused by sudden machine failure, is studied in this paper. The adopted objective function is to minimise the total weighted completion time, which is representative of the minimisation of the global holding/inventory cost in the system. This problem is proven to be NP-hard; a position-based mixed integer programming model and an efficient heuristic algorithm with local improvement strategy are developed for the total weighted completion time problem. To evaluate the performances of the proposed heuristic algorithms, two new lower bounds are further developed. Computational experiments show that the proposed heuristic can rapidly achieve optimal results for small-sized problems and obtain near-optimal solutions with tight average relative percentage deviation for large-sized problems.

Integrated production-maintenance strategy considering quality constraints in dry machining

ABSTRACT The selection of the suitable production parameters for dry machining remains a challenging task which impacts the productivity, the system degradation and the quality of the output product. This study proposes an integrated production-maintenance policy allowing the simultaneous consideration of the production parameters, mainly the cutting speed, the production time and cost, the preventive maintenance period and the quality levels and the selling prices of the output product. A special feature of this strategy, defined on a finite time span, is to allow a change of the cutting speed at a certain instant to be determined in an optimal way concurrently with the preventive maintenance period, such as to maximise the total expected profit per time unit. A mathematical model is developed to express the average profit per time unit as a function of the decision variables. A solving procedure is also proposed. A numerical example and a sensitivity analysis are presented in order to show the impact of some major input parameters on the optimal policy.

A new approach to select the reliable suppliers for one-shot devices

A one-shot device is referred to as a unit that performs its function only once, hence a one-shot device cannot be used for testing more than once. Missiles, airbags of automobiles, magnetorheological fluids, and thermal batteries are some examples of one-shot devices. Usually, one-shot devices are kept in storage or in the stand-by condition and taken into use when required. As a matter of fact, proper operation of such devices at designated times becomes an important issue. Thus, choosing appropriate and reliable suppliers of one-shot devices in preventive maintenance periods can be regarded as a critical measure. This paper presents a new approach to provide resilience in supplier selection for one-shot devices and its subsystems in response to risks. In this research: (1) an expert team is set up having experience and expertise in one-shot systems, (2) risks of suppliers are estimated using satisfying and simple additive weighting (SAW) methods, (3) risk related to each equipment are determined by a cause and effect matrix, (4) after presenting a novel integrated approach, the best combination for one-shot equipment suppliers is selected by using fault tree analysis, (5) finally, to evaluate the performance of the proposed approach, a real case study is also used. This approach can therefore be used for determining faults in the entire system evaluated along with allocating suppliers to each equipment by considering suppliers and equipment risks as well as making and selecting scenarios.

Integrated Scheduling of Tasks and Preventive Maintenance Periods in a Parallel Machine Environment With Single Robot Server

In this study, the objective of minimizing makespan has been considered for a scheduling problem of identical parallel machines with a single server and unavailability constraints. The unavailability constraints correspond to preventive maintenance periods. In this study, the jobs and the maintenance periods are scheduled simultaneously. This scheduling problem has a wide range of potential application areas in the manufacturing environment. In addition, the studied problem is a challenging one from theoretical point of view, due to its NP-Hardness. To conduct the study, a lower bound ( LB ) for the problem, and three metaheuristics namely Simulated Annealing ( SA ), Tabu Search ( TS ), and Genetic Algorithm ( GA ) have been proposed. The best parameters settings of the proposed algorithms were conducted using pilot runs with a Taguchi design. The algorithms performance has been assessed by using a set of test problems generated randomly. These test problems are based on a literature benchmark. The size of the instances, or number of jobs, were up to 500. Along with the performance analysis of the proposed algorithms, the effect of varying processing times and unavailability periods on the performance of the proposed algorithms is studied. The present work provides strong evidence of the efficiency and the performance of the proposed algorithms.

Optimal periodic preventive maintenance policies for systems subject to shocks

External shock process, known as the shot-noise process, will lead to the failure rate increasing of systems operating under random environment. In this paper, assuming that each shock may cause a random increment of the system failure rate, the optimal periodic preventive maintenance (PM) policy for the system is investigated. The system is preventively maintained at periodic time points nT,n=1,2,…,N−1, and is preventively replaced at time point NT. The PM is not perfect, however, it can remove the accumulated failure rate increments caused by external shocks occurred during the last PM interval. When the system fails during the PM intervals, a minimal repair is performed to restore the function of the system. The expression of the average cost rate of the system is derived explicitly. The existence and uniqueness of the optimal bivariate PM policy (N,T) that minimizes the average cost rate are proved theoretically. Numerical examples are presented to illustrate the maintenance model and show the effectiveness of the proposed method.