Preventive Maintenance Strategy Research Articles

Análise de confiabilidade para formulação de estratégia de manutenção de equipamentos em uma empresa da indústria alimentícia

A função manutenção tem um importante papel na busca por vantagens competitivas pelas empresas. Nesse sentido, a análise de confiabilidade de equipamentos é parte fundamental para a definição e seleção de estratégias de manutenção mais adequadas. O objetivo deste trabalho é avaliar, por meio de análise de confiabilidade, a coerência da atual estratégia de manutenção de equipamentos de uma empresa da indústria alimentícia localizada no interior do Estado de São Paulo. O método de pesquisa utilizado foi a modelagem estatística aplicada ao tempo de reparo (TTR) e tempo entre falhas (TBF) de um conjunto de 6 máquinas de embalagem de uma das linhas de produção. Estes tempos foram modelados por distribuições de probabilidade Log-normal e Weibull. Os resultados apontam que a atual estratégia de manutenção preventiva adotada para os equipamentos não está adequada, pois os equipamentos se encontram na fase de mortalidade infantil do ciclo de sua vida útil, o que sugere o emprego da estratégia de manutenção corretiva com o objetivo de se identificar e eliminar as falhas de origem.

Cumulative Hazard Intensity Based on Sequential Preventive Maintenance Strategy for Ensuring Minimum Reliability

Cumulative Hazard Intensity Based on Sequential Preventive Maintenance Strategy for Ensuring Minimum Reliability

Preventive maintenance scheduling for serial multi-station manufacturing systems with interaction between station reliability and product quality

Station reliability and product quality usually interact with each other stochastically in the multi-station manufacturing systems, which brings much trouble for the preventive maintenance (PM) scheduling of the system. In this paper, a station reliability evaluation method is developed firstly based on the introduced bidirectional interaction mechanism between station reliability and product quality, in which the focus is on the derivation of the failure rate of the stations. Then, a dynamic opportunistic PM (OM) policy is presented for the series multi-station systems with such quality integrated station reliability. The OM model is built up based on an extended cost saving method, and the optimal PM scheme of the system is obtained by maximizing the short-term cost savings which not only come from the stations conducting PM but also come from the downstream stations of those PM stations. Finally, a numerical example is given to illustrate how this PM scheduling approach works, and a numerical comparison on different OM policies is also given to show the effectiveness of the proposed OM policy under such interaction between station reliability and product quality.

An Automatic Road Distress Visual Inspection System Using an Onboard In-Car Camera

Speaking of road maintenance, the preventive maintenance strategy is preferable for most governments. Many governments possess special vehicles that can accurately detect and classify many types of road distresses. By running these vehicles frequently, small road distresses will be detected before growing into the big ones. However, because running these huge and expensive vehicles is not easy, in practical, it usually ends up with infrequent road inspection regardless of having automatic road inspection vehicles. In this paper, we focus on investigating and developing an automatic and nondestructive visual inspection system whose setup and usage are designed by considering the context of drivers, driving styles, and road conditions in Bangkok, the capital city of Thailand. Our proposal includes a workflow diagram of a vision-based road inspection system that is capable of detecting, classifying, tracking, measuring, and pricing road distresses. As for the proof-of-concept, our current system focuses on detecting one specific type of road distresses called pothole, using only one onboard in-car camera. Experimental results reveal that the context of Bangkok introduces many nontrivial challenges for vision-based analysis systems where maintaining both accuracy and ease of use altogether may not be easy.

Bayesian approach with subjective opinion fusions for wind turbine maintenance

An optimal Bayesian update strategy that implements the subjective opinions of several experts are introduced for preventive maintenance of wind turbines while single expert opinion has been introduced by the author in the previous studies. This work is introducing the opinion of the wind farm manager or technician via subjective opinions based on a Bayesian adaptive update strategies for optimal preventive maintenance. Subjective opinion will be implemented to Bayesian cycles while experts can impact the distribution parameters with no knowledge of statistics but just by presenting their opinion as belief, disbelief or uncertainty. Statistical parameters such as minimal time of maintenance and cost of new strategy will be impacted by the interaction of wind farm manager and technician that interact with quantitative data with their opinions. The approach employs and complements the quantitative data from turbine Supervisory control and data acquisition (SCADA).

Mathematical Simulation Methods to Evaluate the Effects of Actions on Conditional Preventive Maintenance of Complex Systems

In this work, we propose two mathematical simulation methods to evaluate the results of conditional preventive maintenance strategies based on controls and inspections with optimal performance. In order to have an optimal availability of the equipment in question, the evaluation of the maintenance strategies associated with the controls will be treated by the method of the fault trees based on the decision binary diagram with the calculation engine ALBIZIA. The maintenance performance associated with inspections will be determined by Petri networks coupled with the Monte Carlo method.

RELIABILITY CENTERED MAINTENANCE FRAMEWORK FOR MANUFACTURING AND SERVICE COMPANY: FUNCTIONAL ORIENTED

Maintenance methodologies are vary due to complexity of the system. There are many ways to approach final goal in any philosophy by using processes, paths, methodologies and procedure. Reliability centered maintenance (RCM) is a method that used to ascertain the maintain requirement of any elemental assets in its operating context. RCM mainly identifies function of a systems, physical assets and equipment which could be critically fail in operations and RCM uses to optimize their maintenance strategies. This paper focus to present RCM II methodologies and framework to introduce for manufacturing and service company in functional oriented context. Through the proposal, company motivated to increase profit over the production and availability of system continuously. RCM II enables maintenance actions on the basis of evidence and it reduces failure rate of equipment and unnecessary involvement of maintenance staff. The company doesn’t use any maintenance strategies. This paper proposes RCM concept for use in general industry specific in manufacturing and service. Introduced preventive maintenance strategy supposed to collect data by manual format, the collected data should be used to evaluate machine health, condition and other relevant information. Collected data forwarded to decision making upon the maintenance strategy. This Paper demonstrate how successfully maintained program carried out with customized methodology in manufacturing industry arena. Successfulness of program supported to deliver company common goal.

Critical Study of the Different Types of Maintenance Used in Industry

The aim of this study is to discuss and clarify the important features and characteristics of effective approaches in order to perform maintenance management. For this purpose, the relevant literature is examined, classified and analyzed to carry out a comparative study between many methods of the maintenance used in the industry. Deficient maintenance management can severely affect competitiveness of an organization by reducing throughput, increasing inventory and leading to poor performance, Maintenance management of industrial equipment is an important but still relatively neglected by business function In order to achieve world-class performance, more and more companies are replacing their reactive, strategies for maintenance with proactive strategies like preventive and predictive maintenance and progressive strategies like total productive maintenance. While these new maintenance strategies require increased commitments to training, resources and integration. They also tend to improve performance. This article discusses a research project to identify the strong points, weak points between the techniques of maintenance and the relationship between the approaches of maintenance and production methods. A survey was conducted to determine the relationship between the two approaches.

Feasibility in assessing the dipped rail joint defects through dynamic response of heavy haul locomotive

The feasibility of monitoring the dipped rail joint defects has been theoretically investigated by simulating a locomotive-mounted acceleration system negotiating several types of dipped rail defects. Initially, a comprehensive locomotive-track model was developed using the multi-body dynamics approach. In this model, the locomotive car-body, bogie frames, wheelsets and driving motors are considered as rigid bodies; track modelling was also taken into account. A quantitative relationship between the characteristics (peak–peak values) of the axle box accelerations and the rail defects was determined through simulations. Therefore, the proposed approach, which combines defect analysis and comparisons with theoretical results, will enhance the ability for long-term monitoring and assessment of track systems and provides more informed preventative track maintenance strategies.

Multi-level maintenance strategy of deteriorating systems subject to two-stage inspection

Abstract Preventive maintenance, including time-based and condition-based maintenance, plays an important role in reducing system economic losses caused by unexpected failures. This paper studies a multi-level preventive maintenance strategy for a three-state industrial system subject to two competing failure processes. The first is a continuous degradation process characterized by the general path model, and the second is a shock process arriving according to a non-homogenous Poisson process. The degradation/hazard rate of the system undergoes an abrupt increase upon the initialization of the defective state, and the magnitude of the damage caused by a shock load is correlated to two factors: (a) operational age; (b) degradation speed. The system is preventively replaced at a pre-determined operational age, before which a finite number of inspections are executed according to a two-stage interval partition. The objective of this paper is to minimize the expected cost per unit time via the optimization of the replacement age, the control limit and two inspection intervals. A case study on a peristaltic pump is provided to illustrate the application of the maintenance model.

The repair alert models with fixed and random effects

ABSTRACTThis article extends a random preventive maintenance scheme, called repair alert model, when there exist environmental variables that effect on system lifetimes. It can be used for implementing age-dependent maintenance policies on engineering devices. In other words, consider a device that works for a job and is subject to failure at a random time X, and the maintenance crew can avoid the failure by a possible replacement at some random time Z. The new model is flexible to including covariates with both fixed and random effects. The problem of estimating parameters is also investigated in details. Here, the observations are in the form of random signs censoring data (RSCD) with covariates. Therefore, this article generalizes derived statistical inferences on the basis of RSCD albeit without covariates in past literature. To do this, it is assumed that the system lifetime distribution belongs to the log-location-scale family of distributions. A real dataset is also analyzed on basis of the results obtained.

Money-back guarantee warranty policy with preventive maintenance strategy for sensor-embedded remanufactured products

In today’s global environment, technology is constantly evolving. Being able to stay up-to-date with the very latest technological advances can be extremely hard to accomplish. As a result of these changes and developments in technology, which often come unexpectedly, consumers are frequently tempted to update their devices to the very latest model. The result is that the life cycle of a product is becoming shorter and shorter than before. Manufacturers attempt to respond to consumers’ concerns involving environmental issues as well as the more governmentally stringent environmental legislations by establishing facilities which include the minimization of the totality of waste relocated to landfills by recovering materials and components from returned, or End-Of-Life products and reuse them to build a remanufactured product, and/or novel components. With the rapid growth of interest in remanufactured products’ market, offering warranty for remanufactured products and components is becoming a necessity for remanufacturer in order to meet customers’ requirement and as a marketing mechanism. During that process, maintenance policies are of great importance in order to reduce the warranty cost on the remanufacturer. In this paper, an optimization simulation model for remanufactured items sold with one-dimensional non-renewing money-back guarantee (MBG) warranty policy is proposed from the view of remanufacturer, in which, an End-Of-Life product is subjected to upgrade action at the end of its past life and during the warranty period, preventive maintenance actions are carried out when the remaining life of the product reaches a pre-specified value so that the remanufacturer’s expected profit can be maximized. Finally, a numerical example and design of experiment analysis are provided to demonstrate the proposed approach.

Critical component identification and autonomous maintenance activities determination using fuzzy analytical hierarchy process method

Autonomous maintenance (AM) is a preventive maintenance (PM) strategy that focuses on the 'man-machine' relationship towards effectively performing the following activities: cleaning, lubricating and tightening. The previous literature found that most of the applications and implementations of AM strategy were only discussed as a subset of total productive maintenance (TPM) and from a managerial perspective. This paper highlights the technical issue of AM strategy implementation by proposing an AM decision model using the fuzzy analytical hierarchy process (FAHP) method to identify the critical components and to determine the right AM activities. A case study that used a lathe machine was used to validate the model. The model assists the maintenance engineering team to systematically implement an AM strategy with their production assets. The benefit of this model is that it minimises the maintenance costs and time by identifying and determining the right component to be focused on for maintenance as well as the right maintenance activities to be carried out.

Research on Preventive Maintenance Strategy of Elevator Equipment

The real estate industry is booming with the accelerating process of urbanization. The elevator is widely used as an important part of the building and its sales volume and usage have continued to grow. The frequent occurrence of elevator accidents aroused people’s attention in real life. Therefore, it is very important to strengthen the elevator maintenance process. This paper studies the optimal maintenance strategy of elevators within finite life from the perspective of preventive maintenance to reduce the failure rate of elevators. The number of elevator preventative maintenance and the time interval were found by establishing a target programming optimization model. Finally, the validity of the model is verified by numerical example.

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.

Application of reliability centred maintenance with availability assessment: a case study

Restoration of initial operational availability of the system in a cost effective way is the main objective of reliability centred maintenance. It is used to optimise preventive maintenance strategies. In this paper, reliability centred maintenance is applied to a real-time case and suggested cost effective maintenance tasks for the most critical component of the system, which is identified by using Ishikawa diagram and failure mode effect and criticality analysis (FMECA). The decision tree is used to suggest cost effective maintenance tasks for the critical component. The inherent and operational availability of the system is estimated by determining two important reliability parameters, mean time between failure (MTBF) and mean time to repair (MTTR). These parameters are determined by using Monte Carlo simulation and continuous Markov chain respectively.

An optimisation research on two-dimensional age-replacement interval of two-dimensional products

For the failures of two-dimensional products affected by the calendar time and usage time, traditional one-dimensional preventive maintenance cannot satisfy the actual maintenance demand of two-dimensional products. Therefore, this paper proposes two-dimensional preventive maintenance strategy for two-dimensional products and makes decision for two-dimensional products through calendar time and usage time. Firstly, we establish failure rate function expression of two-dimensional products and analyse the process of two-dimensional preventive maintenance in detail, after proposing a cost model and availability model from the view of economy and mission in finite span. According to the cost and availability model, through examples, we derive the optimal two-dimensional age-replacement interval by numerical algorithm and particle swarm algorithm, and verify the model's applicability and validity through contrast and analysis. Lastly, we give a brief discussion of future research area about two-dimensional maintenance.

Application of reliability centred maintenance with availability assessment: a case study

Restoration of initial operational availability of the system in a cost effective way is the main objective of reliability centred maintenance. It is used to optimise preventive maintenance strategies. In this paper, reliability centred maintenance is applied to a real-time case and suggested cost effective maintenance tasks for the most critical component of the system, which is identified by using Ishikawa diagram and failure mode effect and criticality analysis (FMECA). The decision tree is used to suggest cost effective maintenance tasks for the critical component. The inherent and operational availability of the system is estimated by determining two important reliability parameters, mean time between failure (MTBF) and mean time to repair (MTTR). These parameters are determined by using Monte Carlo simulation and continuous Markov chain respectively.

A Novel Short-Term Maintenance Strategy for Power Transmission and Transformation Equipment Based on Risk-Cost-Analysis

Current studies on preventive condition-based maintenance of power transmission and transformation equipment mainly focus on mid-term or long-term maintenance, and cannot meet the requirements of short-term especially temporary maintenance. In order to solve the defects of the present preventive maintenance strategies, according to the engineering application and based on risk-cost analysis, a short-term maintenance strategy is proposed in this manuscript. For the equipment working in bad health condition, its active maintenance costs and operation risk costs are evaluated, respectively. Then the latest maintenance time is calculated in accordance with the principle that its operation risk costs are no higher than active maintenance costs. Utilizing the latest maintenance time, the best maintenance time is calculated by setting the maximum relative earnings of postponing maintenance as the target, which provides the operation staffs with comprehensive maintenance-decision support. In the end, different cases on the IEEE 24-bus system are simulated. The effectiveness and advantages of the proposed strategy are demonstrated by the simulation results.

Optimization of periodic preventive maintenance policy following the expiration of two-dimensional warranty

This paper considers an optimal periodic preventive maintenance policy after the expiration of two-dimensional warranty. During the two-dimensional warranty period, both renewal warranty and nonrenewal warranty are considered and a repair time threshold is pre-specified so that the failed system is either minimally repaired or is replaced depending on whether the length of repair time exceeds the repair time threshold or not. After the warranty expires, the system undergoes the preventive maintenance periodically a fixed number of times by the user to prolong the life of the system and then the system is replaced preventively by a new one. In this study, we develop an optimal post-warranty periodic preventive maintenance strategy by minimizing the expected cost rate incurred during the life cycle of the system. From the user's perspective, the system is maintained free of charge or with prorated charge on the failed system during the warranty period. Once the warranty expires, the maintenance cost of the system is entirely charged to the user. Subject to such a cost structure, we derive the formula to evaluate the expected cost rate, which is used as an objective function for the optimality. The main goal of this paper is to determine an optimal preventive maintenance cycle after the warranty expires and thus to propose an optimal post-warranty strategy for the user. The effect of several parameters on the optimal strategy is also investigated numerically in this study. For the purpose of illustration of our proposed model, we present and discuss some numerical examples.