Showing papers on "Preventive maintenance published in 2010"

Reliability and maintenance modeling for systems subjected to multiple dependent competing failure precesses

Abstract: For complex systems that experience Multiple Dependent Competing Failure Processes (MDCFP), the dependency among the failure processes presents challenging issues in reliability modeling. This article, develops reliability models and preventive maintenance policies for systems subject to MDCFP. Specifically, two dependent/correlated failure processes are considered: soft failures caused jointly by continuous smooth degradation and additional abrupt degradation damage due to a shock process and catastrophic failures caused by an abrupt and sudden stress from the same shock process. A general reliability model is developed based on degradation and random shock modeling (i.e., extreme and cumulative shock models), which is then extended to a specific model for a linear degradation path and normally distributed shock load sizes and damage sizes. A preventive maintenance policy using periodic inspection is also developed by minimizing the average long-run maintenance cost rate. The developed reliability and ma...

Optimal Selective Maintenance Strategy for Multi-State Systems Under Imperfect Maintenance

Abstract: Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. In such a case, one of the most widely used maintenance policies is a selective maintenance in which a subset of feasible maintenance actions is chosen to be performed with the aim at achieving the subsequent mission success under limited maintenance resources. Traditional selective maintenance optimization reported in the literature only focuses on binary state systems. Most systems in industrial applications, however, have more than two states in the deterioration process. In this work, a selective maintenance policy for multi-state systems (MSS) consisting of binary state elements is investigated. Taking the imperfect maintenance quality into consideration, the Kijima model is reviewed, and a cost-maintenance quality relationship which considers the age reduction factor as a function in terms of maintenance cost is established. Moreover, with the assistance of the universal generating function (UGF) method, the probability of the repaired MSS successfully completing the subsequent mission is formulated. In place of enumerative methods, a genetic algorithm (GA) is employed to solve the complicated optimization problem where both multi-state systems, and imperfect maintenance models are taken into account. The effectiveness of the proposed method is demonstrated via a case study of a power station coal transportation system. Finally, a comparative analysis between the strategies with and without considering imperfect maintenance is conducted, and it is concluded that incorporating imperfect maintenance quality into selective maintenance achieves better outcomes.

Optimal Maintenance Strategies for Wind Turbine Systems Under Stochastic Weather Conditions

Abstract: We examine optimal repair strategies for wind turbines operated under stochastic weather conditions. In-situ sensors installed at wind turbines produce useful information about the physical conditions of the system, allowing wind farm operators to make informed decisions. Based on the information from sensors, our research objective is to derive an optimal preventive maintenance policy that minimizes the expected average cost over an infinite horizon. Specifically, we formulate the problem as a partially observed Markov decision process. Several critical factors, such as weather conditions, lengthy lead times, and production losses, which are unique to wind farm operations, are considered. We derive a set of closed-form expressions for the optimal policy, and show that it belongs to the class of monotonic four-region policies. Under special conditions, the optimal policy also belongs to the class of monotonic three-region policies. The structural results of the optimal policy reflect the practical implications of the turbine deterioration process.

Linear and Nonlinear Preventive Maintenance Models

Abstract: Preventive maintenance (PM) is a maintenance program with activities initiated at predetermined intervals, or according to prescribed criteria, and intended to reduce the probability of failure, or the degradation of the functioning of an item. In the literature, a number of PM models have been introduced to depict the effectiveness of PM. Based on these models, approaches to scheduling PM policies have been considerably studied. This paper attempts to review existing PM models, and investigate their inter-relationships. We then categorize these models into three classes: linear, nonlinear, and a hybrid of both. These three PM model classes depict the relationships of the hazard functions before, and after a PM. Possible extensions to these three PM models are discussed. The statistical properties for models are derived, and approaches to optimizing the PM policy are given.

Reliability and Maintenance of Complex Systems

Abstract: Stochastic Models of Reliability and Maintenance: An Overview.- Fatigue Crack Growth.- Predictive Modeling for Fatigue Crack Propagation via Linearizing Time Transformations.- The Case for Probabilistic Physics of Failure.- Dynamic Modelling of Discrete Time Reliability Systems.- Reliability Analysis via Corrections.- Towards Rational Age-Based Failure Modelling.- Maintenance Policies for Multicomponent Systems: An Overview.- Complex Systems in Random Environments.- Optimal Replacement of Complex Devices.- A Framework for Single-Parameter Maintenance Activities and Its Use in Optimisation, Priority Setting and Combining.- Economics Oriented Maintenance Analysis and the Marginal Cost Approach.- Availability Analysis of Monotone Systems.- Optimal Replacement of Monotone Repairable Systems.- How to Determine Maintenance Frequencies for Multi-Component Systems? A General Approach.- A Probabilistic Model for Heterogeneous Populations and Related Burn-in Design Problems.- An Overview of Software Reliability Engineering.- The Operational Profile.- Assessing the Reliability of Software: An Overview.- The Role of Decision Analysis in Software Engineering.- Analysis of Software Failure Data.- Simulation: Runlength Selection and Variance Reduction Techniques.- Simulation: Sensitivity Analysis and Optimization Through Regression Analysis and Experimental Design.- Markov Dependability Models of Complex Systems: Analysis Techniques.- Bounded Relative Error in Estimating Transient Measures of Highly Dependable Non-Markovian Systems.- Maintenance Management System: Structure, Interfaces and Implementation.- PROMPT, A Decision Support System for Opportunity-Based Preventive Maintenance.- Maintenance Optimisation with the Delay Time Model.- List of Contributors.

An Integrated Model for Production and Preventive Maintenance Planning in Multi-State Systems

Abstract: This paper integrates preventive maintenance with tactical production planning in multi-state systems. The proposed model coordinates the production with the maintenance decisions, so that the total expected cost is minimized. We are given a set of products that must be produced in lots on a multi-state production system during a specified finite planning horizon. Planned preventive maintenance, and unplanned corrective maintenance can be performed on each component of the multi-state system. The maintenance policy suggests cyclical preventive replacements of components, and a minimal repair on failed components. The objective is to determine an integrated lot-sizing and preventive maintenance strategy of the system that will minimize the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs, and production costs, while satisfying the demand for all products over the entire horizon. We model the production system as a multi-state system with binary-states, and s-independent components. A method is proposed to evaluate the times and the costs of preventive maintenance and minimal repair, and the average production system capacity in each period. We show how the formulated problem can be solved by comparing the results of several multi-product capacitated lot-sizing problems. For large-size problems, a genetic algorithm is developed to deal with the preventive maintenance selection task in the integrated planning model.

Single-machine-based production scheduling model integrated preventive maintenance planning

Abstract: Manufacturing and production plants operate physical machine that will deteriorate with increased usage and time. Maintenance planning which can keep machines in good operation is thus required for smooth production. However, in previous research, production scheduling and maintenance planning are usually performed individually and not studied as an integrated model. In order to balance the trade-offs between them, this study proposes an integrated scheduling model by incorporating both production scheduling and preventive maintenance planning for a single-machine problem with the objective of minimizing the maximum weighted tardiness. In this model, a variable maintenance time subjected to machine degradation is considered. Finally, a numerical example using this improved production scheduling model is shown. The computational results prove its efficiency.

Preventive maintenance scheduling for repairable system with deterioration

Abstract: Maintenance as an important part in manufacturing system can keep equipment in good condition. Many maintenance policies help to decrease the unexpected failures and reduce high operational cost such as conventional preventive maintenance. But these conventional preventive maintenance policies have the same time interval T that may easily neglect system's reliability, because the system deteriorates with increased usage and age. Hence, this study has developed a reliability-centred sequential preventive maintenance model for monitored repairable deteriorating system. It is supposed that system's reliability could be monitored continuously and perfectly, whenever it reaches the threshold R, the imperfect repair must be performed to restore the system. In this model, system's failure rate function and operational cost are both considered by the effect of system's corresponding condition, which helps to decide the optimal reliability threshold R and preventive maintenance cycle number. Finally, through case study, the simulation results show that the improved sequential preventive maintenance policy is more practical and efficient.

Novel Approach for Multicriteria Optimization of Life-Cycle Preventive and Essential Maintenance of Deteriorating Structures

Abstract: In this paper, the performance of structures is modeled using lifetime functions. Specifically, the unavailability and redundancy are used as performance indicators, based on which optimum maintenance strategies are sought. Models that reflect the separate or combined effects of essential and preventive maintenance on the unavailability are presented. A novel optimization approach is proposed in which the problem is formulated to provide optimum maintenance strategies with either or both essential and preventive maintenance actions. Genetic algorithms are used to solve this problem. In this paper, multiple essential maintenance types and multiple preventive maintenance types are considered, and regular or irregular preventive maintenance time-intervals are considered. Furthermore, essential maintenance is treated as performance-based, i.e., essential maintenance is only applied when a performance threshold is reached, and an algorithm is proposed for conducting the optimization under uncertainty. Although applicable to any type of structure, the proposed approach is illustrated on a highway bridge example.

Joint Production and Spare Part Inventory Control Strategy Driven by Condition Based Maintenance

Abstract: Throughput of a manufacturing process depends on the effectiveness of equipment maintenance, and the availability of spare(service) parts. This paper addresses a joint production and spare part inventory control strategy driven by condition based maintenance(CBM) for a piece of manufacturing equipment. Specifically, a critical unit is continuously monitored for performance degradation during operation. The amount of degradation is utilized to initiate replacement actions in conjunction with spare part inventory control under both production lot size, and due date constraints. A degradation limit maintenance policy is combined with a base stock spare part inventory control policy to manage the manufacturing process. The objectives are to minimize the spare part inventory, and the expected total operating cost. Constrained least squares approximation, and simulation-based optimization are utilized, in a heuristic two-step approach, to determine the optimal base-stock level of spare parts, along with the preventive maintenance threshold. The resulting joint decision ascertains the allowed stockout probability for spare parts, while incurring the minimal operating cost for the required production within a fixed production duration. A case study of an automotive engine manufacturing process is provided to demonstrate the proposed decision-making methodology in practical use.

Cost-Effective Updated Sequential Predictive Maintenance Policy for Continuously Monitored Degrading Systems

Abstract: The importance of maintenance optimization has been recognized over the past decades and is highly emphasized by today's competitive economy. In this paper, an updated sequential predictive maintenance (USPM) policy is proposed to decide a real-time preventive maintenance (PM) schedule for a continuously monitored degrading system that will minimize maintenance cost rate (MCR) in the long term, by considering the effect of imperfect PM. The USPM model is continuously updated based on the change in the system state to decide an optimal PM schedule. Mathematical analysis of the proposed USPM model demonstrates the existence and uniqueness of an optimal PM schedule under practical conditions. The results validate that: 1) the proposed USPM model yields PM schedules that are consistent with the change in the system states and 2) the USPM model is able to quickly react to drastic degradation of the system and provide an optimal PM schedule in real time. The proposed maintenance policy can provide significant benefits for real-time maintenance decision making.

Statistical Process Control and Condition‐Based Maintenance: A Meaningful Relationship through Data Sharing

Abstract: This paper focuses on the close relationship between statistical process control and preventive maintenance (PM) of manufacturing equipment. The context is very general: a production process that is characterized by multiple distinct operational states and a failure state. The operational states differ in terms of operational/quality costs and/or the proneness to complete failure. The times of shift from the normal operational state to an inferior one and the times to failure are random variables, not necessarily exponentially distributed. The process is monitored with a control chart with the purpose of quickly detecting shifts to an inferior operational state due to the occurrence of some unobservable assignable cause. At the same time, the information collected from the process may be used to re-schedule the planned PM, if there is evidence that a failure is imminent. The two mechanisms are obviously related, especially if they are based on measurements of the same critical process characteristic. Yet, they are typically treated independently. We develop a fairly general mathematical model for the joint optimization of the control chart parameters and the maintenance times. Numerical investigation using this model shows that ignoring the close relationship between process control and maintenance results in inefficiencies that may be substantial. It also provides practical insights about the effects of some key problem characteristics on the optimal joint design of process control and maintenance.

A decision-making framework for process plant maintenance

Abstract: An optimal maintenance approach is a key support to industrial production in the contemporary process industry. This paper presents a Multiple-Criteria Decision Making (MCDM) methodology for selecting the optimal mix of maintenance approaches – Corrective Maintenance (CM), Time-Based Preventive Maintenance (TBPM) and Condition-Based Predictive Maintenance (CBPM) – for different equipment in a typical process plant. First, the criticality of each equipment from the point of view of maintenance is achieved by risk ranking them (based on the worst-case failure mode), thus prioritising them for maintenance interventions. Next, the MCDM methodology with a fuzzy adaptation of the Analytic Hierarchy Process (AHP) technique is applied to individual equipment. The criteria used as part of the MCDM model are safety, maintenance investment, business interruption loss and maintenance technique feasibility. Furthermore, this technique is embedded into a Goal Programming (GP) model to optimise multiple objectives such as risk reduction and cost minimisation that are subject to resource constraints. The present approach is an improvement over the contemporary approaches to decision making for maintenance management in that it integrates risk assessment with the GP-fuzzy AHP technique and is sufficiently generalised. The approach can aid in the formulation of a cost-effective maintenance approach for a plant. [Received 11 August 2008; Revised 31 December 2008; Revised 20 February 2009; Accepted 25 February 2009]

Optimal reliability, production lotsize and safety stock: an economic manufacturing quantity model

Abstract: Reliability based maintenance provides sound guidance for managers who wish to attain high standards of maintenance at their operating systems Basically, the amount and the type of maintenance app

Maintenance management in Italian manufacturing firms: Matters of size and matters of strategy

Abstract: Purpose – This paper aims to give a picture of maintenance management in Italian manufacturing firms supported by empirical evidence. The purpose is also to highlight how far maintenance performance and strategies are influenced by context and which measures and goals are within reach of small‐sized firms.Design/methodology/approach – Frameworks for describing maintenance management and strategies derived from literature were reviewed and used to develop a questionnaire. A survey‐based empirical research involving a sample of 100 manufacturing firms was performed. Non‐parametric statistics are applied to highlight correlations and dependencies between contextual variables, maintenance strategies and performance.Findings – Maintenance performance hardly seems a matter of size, while many elements of strategy certainly are. Some elements of strategy, in particular planning and control elements, seem to have little impact on performance. By contrast, an enhanced use of preventive maintenance and, above all, ...

A Decision Support System for Integrating Corrective Maintenance, Preventive Maintenance, and Condition-Based Maintenance

Abstract: This paper presents a framework of decision support systems for facilities maintenance management (FMM) with the objective of integrating facilities maintenance management, real-time project management, condition monitoring systems and building information models. Multi-faceted views of maintainable assets are designed to meet the requirements of any potential functional extensions or systems integration. Basic processes for asset management, Corrective Maintenance (CM), Preventive Maintenance (PM), and Condition-based Maintenance (CBM) are implemented in a Web-based FMM prototype system. The generic aspects of the system lay in the fact that: (1) all sources of maintenance work, ranging from manually entered CM orders and system generated PM orders to individual maintenance projects, are normalized and manipulated as projects and tasks; (2) the allocation of various kinds of resources, including equipment, materials, trades, contractors, and tools, is optimized using the proposed algorithms.