Machine Preventive Maintenance Research Articles

Terje Aven Availability Model Application to the N-1 System with One Unit of Backup Towing Motor Components for Kiln Machine Preventive Maintenance

The kiln machine is one of the pieces of machinery used to produce granite tiles. One of its many crucial components is the towing motor. The production process requires a high-performance and easily accessible kiln unit because it lasts for 24 hours. As a result, there will be less downtime since the extra towing motor can take the place of the damaged one. The Terje Aven availability model calculates the number of towing motor reserves. The availability of kiln equipment has not yet achieved its full potential because the company currently has six backup towing motors.

Research on joint scheduling method of order grading and machine maintenance

AbstractIn the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

Multiobjective Flexible Job-Shop Rescheduling With New Job Insertion and Machine Preventive Maintenance.

In the actual production, the insertion of new job and machine preventive maintenance (PM) are very common phenomena. Under these situations, a flexible job-shop rescheduling problem (FJRP) with both new job insertion and machine PM is investigated. First, an imperfect PM (IPM) model is established to determine the optimal maintenance plan for each machine, and the optimality is proven. Second, in order to jointly optimize the production scheduling and maintenance planning, a multiobjective optimization model is developed. Third, to deal with this model, an improved nondominated sorting genetic algorithm III with adaptive reference vector (NSGA-III/ARV) is proposed, in which a hybrid initialization method is designed to obtain a high-quality initial population and a critical-path-based local search (LS) mechanism is constructed to accelerate the convergence speed of the algorithm. In the numerical simulation, the effect of parameter setting on the NSGA-III/ARV is investigated by the Taguchi experimental design. After that, the superiority of the improved operators and the overall performance of the proposed algorithm are demonstrated. Next, the comparison of two IPM models is carried out, which verifies the effectiveness of the designed IPM model. Last but not least, we have analyzed the impact of different maintenance effects on both the optimal maintenance decisions and integrated maintenance-production scheduling schemes.

Straightening machine preventive maintenance intervention plan based on AHP: a case study in a steel company in Brazil

Equipment failure and production shortfalls can cause significant impacts on companies. Preventive maintenance can potentially increase equipment reliability and availability, helping companies achieve their corporate goals while reducing operation and maintenance costs. However, determining the optimal interval between maintenance interventions involves balancing various factors such as maintenance costs, equipment availability, risk of failures, and the need for corrective maintenance, as well as potential operational and financial impacts. Under this context, this paper aims to define the interval between preventive maintenance interventions for a key production equipment in a steel company located in Southeast region of Brazil. Upon documental analysis and interviews with experts, an AHP was applied to hierarchize the maintenance periodicity, based on the following criteria: part replacement based on service life, downtime hours for preventive maintenance, equipment reliability, and cost of materials and labour. A biweekly preventive maintenance periodicity was identified as the most adequate for the analysed device. An implementation plan was approved and preliminary results indicate no availability deterioration, while maintenance costs are below historical average.

IoT-based Running Time Monitoring System for Machine Preventive Maintenance Scheduling

Machines are valuable assets that need to be protected from damage and failure through proper maintenance measures. This paper proposes a system that automatically monitors the running time of machines and sends notifications regarding their preventive maintenance (PM) schedules. The system core consists of a programmable logic controller (PLC) and a human machine interface (HMI). The HMI is connected to an online platform via internet connection provided by a router, so that the monitoring result can be accessed via Android smartphone or laptop/PC. This IoT-based running time monitoring system (IRTMS) will be particularly helpful in implementation at an production site that consists of multiple various machines. The PM items of a machine may vary from cleaning, changing single component, to an overhaul, each with different time interval. By using the IRTMS, the user will have an overview of the PM schedules anytime and anywhere. The preparation of material, components, or tools can be known ahead of time. For simulation purpose, a prototype is constructed by using components as used in industrial real-life condition. Four output connections are provided to simulate the simultaneous monitoring of four machines. The IRTMS prototype is tested and completely successful on doing the running time monitoring, the running time reset, the PM notifications, and the remote access for monitoring and control.

Mathematical model for maintenance planning of machine tools

Preventive maintenance planning of machine tools may be a complex task for tools with multiple components. For manufacturing processes with high setup and downtime costs, components replacement should be combined to avoid too many production stoppages and therefore reduce costs. The combination that minimizes costs should take into account the lifetime distribution and the age of each component at the replacement time. Replacing too soon may imply a high number of replacements for a given component, while replacing too late may imply a high number of failures that lead to shutdowns, increasing costs. In this paper, a tool is seen as a series system, which means that whenever a component fails, a corrective action is needed and at least the failed component has to be replaced. In the literature, some of the models and heuristics for maintenance planning of series systems consider that a minimal repair is made when a component fails, while other models propose static approaches, i.e., the same combination and the same interval is used over time regardless of the ages of the components involved. This paper aims to propose a dynamic approach and presents a mathematical model to determine both the next time to perform a preventive maintenance task and the components that should be replaced in order to minimize the total cost. The model also intends to determine the components to be replaced preventively when unexpected events occur (such as the failure of a tool component or the machine, the shortage of raw material, etc.) or during planned stoppages (such as the end of a production order, machine preventive maintenance task, etc.).

A BRKGA-DE algorithm for parallel-batching scheduling with deterioration and learning effects on parallel machines under preventive maintenance consideration

This paper introduces a parallel-batching scheduling problem with deterioration and learning effects on parallel machines, where the actual processing time of a job is subject to the phenomena of deterioration and learning. All jobs are first divided into different parallel batches, and the processing time of the batches is equal to the largest processing time of their belonged jobs. Then, the generated batches are assigned to parallel machines to be processed. Motivated by the characteristics of machine maintenance activities in a semiconductor manufacturing process, we take the machine preventive maintenance into account, i.e., the machine should be maintained after a fixed number of batches have been completed. In order to solve the problem, we analyze several structural properties with respect to the batch formation and sequencing. Based on these properties, a hybrid BRKGA-DE algorithm combining biased random-key genetic algorithm (BRKGA) and Differential Evolution (DE) is proposed to solve the parallel-batching scheduling problem. A series of computational experiments is conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.

Filling machine preventive maintenance using age replacement method in PT Lucas Djaja

PT Lucas Djaja is a company engaged in the pharmaceutical industry which produce sterile drugs and non-sterile. Filling machine has a high failure rate and expensive corrective maintenance cost. PT Lucas Djaja has a policy to perform engine maintenance by way of corrective maintenance. The study focused on the critical components, namely bearing R2, bearing 625 and bearing 626. When the replacement of the failure done by the company is currently using the formula mean time to failure with the result of bearing R2 at point 165 days, bearing 625 at a point 205 days, and bearing 626 at a point 182 days. Solutions generated by using age replacement method with minimization of total maintenance cost given on the bearing R2 at a point 60 days, bearing 625 at the point of 80 days and bearing 626 at a point 40 days.

Preventive Maintenance Interval Prediction: a Spare Parts Inventory Cost and Lost Earning Based Model

In this paper, some preventive maintenance parameters in manufacturing firms were identified and used to develop cost based functions in terms of machine preventive maintenance. The proposed cost based model considers system’s reliability, cost of keeping spare parts inventory and lost earnings in deriving optimal maintenance interval. A case of a manufacturing firm in Nigeria was observed and the data was used to evaluate the model.

Occupational hazards control of hazardous substances in clean room of semiconductor manufacturing plant using CFD analysis

The manufacturing processes in chip industries are complex, and many kinds of raw materials and solvents of different nature are used, most of which are highly toxic and dangerous. During the machine preventive maintenance period, these toxic and harmful substances will escape from the sealed reaction chamber to the clean workshop environment and endanger the health of the workers on-site, resulting in occupational diseases. From the perspective of prevention, the spread and prediction of hydrochloric acid (HCl) that escaped from the metal-etching chamber during maintenance were studied in this article. The computational fluid dynamics technology was used for a three-dimensional numerical simulation of the indoor air velocity field and the HCl concentration field, and the simulation results were then compared with the on-site monitoring data to verify the correctness and feasibility. The occupational hazards and control measures were analyzed based on the numerical simulation, and the optimal control measure was obtained. In this article, using the method of ambient air to analyze the occupational exposure can provide a new idea to the field of occupational health research in the integrated circuit industry and had theoretical and practical significance.

Modeling and Simulation of Re-Entrant Semiconductor Wafer Fabrication Lines with PM Using GSPN

Semiconductor wafer fabrication factories are characterized by re-entrant flows, dynamic and uncertain environments, strict production control requirements and so on. Re-entrant flow means a product routing that consists of multiple visits to a workstation during the manufacturing processes. In this paper, a modeling approach based on generalized stochastic Petri nets is presented. Specifically, a Petri net model representing a re-entrant flow line with three centers and six machines is modeled. Work release policies (WRP) and queuing disciplines (QD) are also added to the model, which take into account the machine preventive maintenance (PM) requirement. Finally, a simulation study is used to analyze the model, which provides support for scheduling decision in shorter time.

Scheduling machine-dependent jobs to minimize lateness on machines with identical speed under availability constraints

In this paper we consider the problem of scheduling n preemptive jobs on m machines with identical speed under machine availability and eligibility constraints when minimizing maximum lateness ( L max ). The lateness of a job is defined to be its completion time minus its due date, and L max is the maximum value of lateness among all jobs. We assume that each machine is not continuously available at all time throughout the planning horizon and each job is only allowed to be processed on specific machines. Network flow technique is used to formulate this scheduling problem into a series of maximum flow problems. We propose a polynomial time two-phase binary search algorithm to verify the feasibility of the problem and to solve the scheduling problem optimally if a feasible schedule exists. Finally, we show that the time complexity of the algorithm is O ( ( n + ( 2 n + 2 x ) ) 3 log ( UB - LB ) ) . Most literature in parallel machine scheduling assume that all machines are continuously available for processing and all jobs can be processed at any available machine throughout the planning horizon. But both assumptions might not be true in some practical environment, such as machine preventive maintenance and machines that have different capabilities to process jobs. This type of scheduling problem is seldom studied in the literature. The purpose of this paper is to examine the scheduling problem with machines with identical speed under machine availability and eligibility constraints. The objective is to minimize maximum lateness. We formulate this scheduling problem into a series of maximum flow problems with different values of L max . A polynomial time two-phase binary search algorithm is proposed to verify the feasibility of the problem and to determine the optimal L max .