Group Maintenance Policy Research Articles

Group machinery intelligent maintenance: Adaptive health prediction and global dynamic maintenance decision-making

Intelligent preventive maintenance powered by health data analytics is essential to ensure operation safety and performance of diverse industrial equipment. Despite the rapid advancement of preventive maintenance methodologies in recent several decades, their implementation to global dynamic maintenance of complex systems across infinite time horizon, in particular leveraging real-time prognosis information to realize adaptive group partition and ordering, has been largely an under-explored domain. To this end, this study devises a generic global-dynamic group maintenance policy for multi-component degrading systems. As opposed to existing models, the policy automatically renews the entire system health information instantly upon the completion of each group maintenance task; therefore, it realizes the dynamic union of (a) predictive group maintenance and (b) unplanned opportunistic maintenance over an infinite time horizon for the first time, such that to promote decision precision and efficiency. A dynamic grouping algorithm is designed to explore the structure of optimal maintenance solutions. The applicability is exemplified by comparative experiments that show substantial advantages over heuristic policies.

Group Maintenance for Numerical Control Machine Tools: A Case Study

The high-reliability requirement of numerical control (NC) machine tools emphasizes the effectiveness of preventive maintenance (PM). However, most existing PM decisions are made by treating them as single-component systems, resulting in the problems of “excessive maintenance” or “insufficient maintenance.” Moreover, real failure data has rarely been utilized for maintenance decision-making, which leads to idealized policies far from practice. This article presents a framework to derive the optimal group maintenance (GM) policy from real failure histories for an NC machine tool comprised of heterogeneous components. Criticality analysis is first conducted to identify high-risk and low-risk components. The lifetime of each high-risk component is characterized by a two-parameter Weibull model or by a Weibull competing-risk model according to whether its failures are independent of the other components. High-risk components with strictly increasing or bathtub-shaped failure rates are selected for PM decision-making. This article introduces a multilevel PM decision-making approach: 1) a repair-replacement model is developed at the component level to derive the optimal PM interval and the maximum number of PM actions; and 2) at the system level, a GM procedure is presented to obtain the optimal PM interval of the system. The failure histories of an NC machine tool with 13 components are used to illustrate the effectiveness of the proposed approach. A maintenance plan is finally made based on optimization results to guide maintenance implementation.

Optimal group maintenance decision for a wind farm based on condition‐based maintenance

AbstractThis study investigated the group maintenance (GM) strategy based on condition‐based maintenance (CBM) of a parallel wind farm with multiple identical wind turbines. In terms of this strategy, the state of deterioration of the system is determined by periodic inspection, with the GM activities at each inspection point performed according to the number of failed turbines, which are determined by the corresponding state of deterioration of the system. Under this strategy, the probabilities that influence GM are derived by the stationary probability density function, which is based on the stationary law of the state of the wind farm system. Furthermore, employing numerical experiments and a case study, we verified the correctness and effectiveness of the stationary probability density function under different deterioration‐related parameters and obtained the optimal number of failed turbines when GM is performed, as well as the optimal interval between two consecutive inspection points. Finally, through comparison of strategies and sensitivity analysis, we demonstrated the economic advantages and the effectiveness of the proposed strategy. For a parallel system with high maintenance set‐up cost owing to severe environmental conditions, our proposed GM policy based on CBM outperformed other existing strategies in terms of cost benefit and ease of implementation.Highlights Proposing group maintenance (GM) schedules for cost‐effectiveness of the whole wind farm. Determining optimal inspection interval and number of the failed wind turbines for GM. A stationary probability density function is developed to address maintenance relevance. Investigating effects of various parameters on the optimal policy performance. Saving huge maintenance cost over the other two maintenance strategies.

Predictive group maintenance for multi-system multi-component networks

Predictive maintenance has become highly popular in recent years due to the emergence of novel condition monitoring and data analysis techniques. However, the application of predictive maintenance at the network-level has not seen much attention in the literature. This paper presents a model for predictive group maintenance for multi-system multi-components networks (MSMCN). These networks are composed of multiple systems that are, in turn, composed of multiple components. In particular, the hierarchical structure of the MSMCN enables different representations of dependences at the network and system levels. The key novelty in the paper is that the designed approach combines analytical and numerical techniques to optimize the predictive group maintenance policy for MSMCNs. Moreover, we introduce a genetic algorithm with agglomerative mutation (GA-A) that enables a more effective evolution of the predictive group maintenance policy. Application of this model on a case study of a two-bridge network made of 23 different components shows a potential 11.27% reduction in maintenance cost, highlighting the model's practical significance.

Group maintenance scheduling for two-component systems with failure interaction

• A novel failure interaction framework is constructed considering hazard rate increment. • Environmental uncertainties are incorporated into dependent failure processes. • A new group maintenance strategy is developed to prevent dependent failures. • The applicability is validated by a case study on an electrical distribution system. Failure dependence is a common phenomenon in complex and large-scale industrial plants, which may cause tremendous economic losses and unacceptable safety risks. This paper investigates an advanced group and opportunistic maintenance policy for a two-component system with failure interaction. The interaction between soft and hard failures is characterized by random hazard rate increments, with fully consideration of environmental uncertainties. To sufficiently utilize the economic dependence between components, Scheduled maintenance windows are equally spaced for group maintenance of the whole system. Whenever a component is replaced at a maintenance window, the other one is also opportunistically replaced. Furthermore, failure of component 2 is removed by minimal repair, whereas replacement is left to the subsequent window. The objective is to jointly optimize the maintenance interval and the number of maintenance windows via the artificial bee colony algorithm. A case study on an electrical distribution system is provided to validate the applicability of the adopted policy.

Group maintenance policies for an R-out-of-N system with phase-type distribution

This paper presents an extension of our earlier paper on the 1-out-of-N repairable cold standby system (i.e., Barron IIE Trans 47:1139–1151, 2015). Specifically, we consider an R-out-of-N repairable system where the lifetimes of the units follow phase-type distribution. The system is functioning if at least R out of its N components work. Each working component is subject to failure. There are fixed, unit repair, and replacement costs associated with the maintenance facility, which is carried out after a fixed lead time $$\tau $$ . A penalty cost is incurred when the number of good components decreases to $$R-1$$ . We assume that the repair takes no time and repaired units are as good as new. By applying renewal theory and matrix-geometric methods, we derive the expected discounted costs under three classes of group maintenance policies: m-failure, T-age, and ( $$m,T,\tau $$ ), which is a refinement of the classical (m, T) policy. Illustrative examples, a comparative study and insights are provided.

A proactive group maintenance policy for continuously monitored deteriorating systems: Application to offshore wind turbines

This article presents an optimum proactive group maintenance policy for continuously monitored systems affected by stochastic deterioration (degradation). A system is composed of multiple nonidentical subsystems, each exposed to a gradual degradation phenomenon. When the length (or size) of degradation in a subsystem reaches a predetermined fault threshold, it fails and leads to failure of the whole system. In order to avoid system failures and to improve availability levels, a proactive group maintenance task is conducted once the degradation level of a subsystem exceeds an “alert” threshold (smaller than the fault threshold). In this maintenance task, the critical subsystem undergoes a state-dependent repair action, and a preventive maintenance is performed on the other subsystems. Furthermore, the whole system is preventively replaced because of safety requirements when its operational age attains a fixed value. We formulate a multivariate nonlinear maintenance optimization model to simultaneously determine the optimal alert thresholds for subsystems and the replacement time for system. The performance of the proposed maintenance policy, regarding the objective of minimum system’s average long-run maintenance cost per unit time, is compared to five conventional cases of maintenance policies: the reactive response, individual age-based, individual condition-based, bivariate age- and condition-based, and age-based group maintenance. A numerical example, using real-life data collected from an offshore wind dataset, is presented to illustrate the applicability of the proposed model to the maintenance of a group of wind turbine bearings.

An optimal age-based group maintenance policy for multi-unit degrading systems

In order to share maintenance set-up costs and reduce system breakdown, the group maintenance policies are widely used for complex multi-unit systems. In this paper, an optimal age-based group maintenance policy is proposed for a multi-unit series system whose components are subject to different gradual degradation phenomena. When the degradation level of a component reaches a given critical size, it is replaced by a new one and the other components undergo a preventive maintenance (PM) action; otherwise, a planned group PM is performed for the whole system at operational age T>0. The problem is to determine an optimal group maintenance time T⁎ such that the system’s average long-run maintenance cost per unit time is minimized. The explicit expression of the objective function is derived and sufficient conditions for existence and uniqueness of the optimal solution are obtained. Finally, the proposed maintenance policy is applied to a group of wind turbine bearings and the results are compared with the case without planned maintenance (i.e., reactive response) and with an individual age-based maintenance policy.

Maintenance Policies of Single and Multi-Unit Systems in the Past and Present

This paper surveys the literature related to maintenance policies for single and multi-unit systems. The emphasis is on work appearing in different periods. The literature is divided into two main categories single and multi unit system. The single unit system is divided into six sub-categories like: Agedependent, Periodic, Failure limit policy, Sequential, Repair limit, Repair number counting and reference time policy. Similarly policies of multi-unit are divided into two subcategories; viz Group and Opportunistic maintenance policies; Each kind of policy has different characteristics, advantages and disadvantages with lot of contributions from Research scientist and Technologists. Around 150 authors and their research works are presented in the reference section. It will help the reader to select the exact policy for their organization and the reference section will be helpful for further study and further knowledge about different existing maintenance practices.

Integrating production, inventory and maintenance planning for a parallel system with dependent components

This paper deals with the problem of integrating preventive maintenance and tactical production planning, for a production system composed of a set of parallel components, in the presence of economic dependence and common cause failures. Economic dependence means that performing maintenance on several components jointly costs less money and time than on each component separately. Common cause failures correspond to events that lead to simultaneous failure of multiple components due to a common cause. We use the β-factor model to represent common cause failures. This means that we assume two possible causes for system failure: the independent failure of single components, and the simultaneous common cause failure of all components. The suggested preventive maintenance is a T-age group maintenance policy in which components are cyclically renewed all together. Furthermore, between the periodic group replacements, minimal repairs are performed on failed components. We are given a set of products that must be produced by this parallel system in lots during a specified finite planning horizon. 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. Numerical examples are used to illustrate the proposed approach.

Three m-failure group maintenance models for M/M/N unreliable queuing service systems

This paper considers group maintenance problems for an unreliable service system with N independent operating servers and a Markovian queue. A specific class of group maintenance policies is developed where the repair is started as soon as the number of failed servers reaches a predetermined threshold. This is actually a Quasi Birth-and-Death Process with two dimensions, the level for the arrival/service process and the phase for the failure/repair process. Two models with positive repair time and another with instantaneous repair are considered. The matrix geometric approach is applied to calculate the steady state distribution and the expected average cost for all three models. For the theoretical analysis, this paper proves that there exists an optimal group maintenance parameter m*, which can find the minimal average cost for all three models. Additionally, some mathematical properties and sensitivity analyses are numerically demonstrated based on various parameters. Finally, the comparisons of these three proposed models in many aspects are also discussed.

Survey of maintenance policies for the Last 50 Years

In the past several decades, maintenance and replacement problems have been extensively studied in the literature. Thousands of maintenance and replacement models have been created. However, all these models can fall into some categories of maintenance policies: age replacement policy, block replacement policy, periodic preventive maintenance policy, failure limit policy, sequential preventive maintenance policy, repair cost limit policy, repair time limit policy, repair number counting policy, reference time policy, mixed age policy, group maintenance policy, opportunistic maintenance policy, etc. Each kind of policy has different characteristics, advantages and disadvantages with lot of contributions from Research scientist, Technologists... This survey summarizes, classifies, and compares various existing maintenance policies Around 170 Authors and their research works are presented in the Reference section. It will help to look into the different policies which is appropriate to the organization and for further study the reference section will be helpful for the researchers for further knowledge

Machine reliability and preventive maintenance planning for cellular manufacturing systems

The paper proposes a preventive maintenance (PM) planning model for the performance improvement of cellular manufacturing systems (CMS) in terms of machine reliability, and resource utilization. In a CMS, parts are processed by a group of interdependent machines, where machine reliability plays an important role in the performance improvement of the cell. Assuming that machine failure times follow a Weibull distribution, the proposed model determines a PM interval and a schedule for performing PM actions on each machine in the cell by minimizing the total maintenance cost and the overall probability of machine failures. The model uses a combined cost and reliability based approach, and optimizes maintenance costs by administering a group maintenance policy subject to a desirable machine reliability threshold. The study also proposes a CMS design model that integrates the above PM concepts into the design process. Illustrative examples are presented to demonstrate the applicability of the proposed approach.

A survey of maintenance policies of deteriorating systems

In the past several decades, maintenance and replacement problems of deteriorating systems have been extensively studied in the literature. Thousands of maintenance and replacement models have been created. However, all these models can fall into some categories of maintenance policies: age replacement policy, random age replacement policy, block replacement policy, periodic preventive maintenance policy, failure limit policy, sequential preventive maintenance policy, repair cost limit policy, repair time limit policy, repair number counting policy, reference time policy, mixed age policy, preparedness maintenance policy, group maintenance policy, opportunistic maintenance policy, etc. Each kind of policy has different characteristics, advantages and disadvantages. This survey summarizes, classifies, and compares various existing maintenance policies for both single-unit and multi-unit systems. The emphasis is on single-unit systems. Relationships among different maintenance policies are also addressed.

A generalized group maintenance policy

This paper proposes a two-phase maintenance policy for a group of identical repairable units. We define the time-interval (0, T] as the first phase, and the time interval ( T, T + W ] as the second phase. As individual units fail individual units have two types of failures. Type I failures (minor failures) are removed by minimal repairs (in both phases), whereas Type II failures (catastrophic failures) are removed by replacements (in the first phase) or are left idle (in the second phase). A group maintenance is conducted at time T + W or upon the kth idle, whichever comes first. The optimal policy is to select T ∗, W ∗ and k ∗ to minimize the expected cost per unit time for an infinite time span. Various special cases are considered. Numerical examples are given to illustrate the method.

A Note on Variance Reducing Group Maintenance Policies

Group replacement policies for machines operating in parallel are generally evaluated by computing the associated expected cost per unit time. However, as with all stochastically evolving systems, variance should play an important managerial role. No matter which policy is used by the maintenance manager, knowledge of the associated variance provides important information about the variability of the costs that will be incurred by using this policy. Additionally, there will be cases where a decision maker is willing to incur increased expected cost per unit time in order to reduce the variability of the costs among cycles. This paper demonstrates that it is feasible and practicable to calculate the variance of the cost per unit time associated with group maintenance policies.

Two Simple Control Policies for a Multicomponent Maintenance System

Optimal group maintenance policies for a set of M identical machines subject to stochastic failures are considered. The control of the system is not based on the complete age configuration of all components, nor on the number of failed components only. We compromise between these two extreme cases by introducing four possible states for each component: good, doubtful, preventive maintenance is due, and failed. Two types of control policies are considered, both based on the number of doubtful components at component failure epochs. Starting from a general model with general (but identical) lifetime distributions for the individual components, we introduce an approximate model in which the four possible states are identified with certain age intervals for each individual component. The sojourn times in the good and the doubtful state are supposed to be exponentially distributed. For this resulting approximate model, explicit expressions are derived for various performance measures, like the time to system replacement and the average costs per unit time. By making use of the results obtained for the approximate model several approximations for the performance measures of the original model are presented. Validation of these approximations is performed by simulation.

Optimal Group Maintenance Policies with Continuous and Periodic Inspections

Optimum group maintenance policies for a set of N machines subjected to stochastic failures under continuous and periodic inspections are considered. Under very general conditions it is shown that a control limit policy minimizes the expected cost per unit time over an infinite horizon, when costs are incurred due to loss of production and repair only. It is also shown how to explicitly compute this control limit. When additional costs are incurred due to inspection, a characterization of an optimal periodic inspection/repair policy that minimizes the expected cost per unit time over an infinite horizon is given.