Proposed Maintenance Policy Research Articles

Developing data-driven O&M policy through sequential pattern mining: A case study

This study focuses on utilizing data mining techniques to extract valuable insights from discrete industrial data, crucial for Operations and Maintenance (O&M) decision-making. We present an innovative methodology integrating data mining with the Plan-Do-Check-Act (PDCA) cycle and Knowledge Discovery in Databases (KDD) principles. Through a manufacturing facility case study, we develop a hybrid opportunistic O&M policy, blending Condition-based Maintenance (CBM) and Time-based Maintenance (TBM) to replace the traditional Failure-based Maintenance (FBM) strategy. Our proposed maintenance policy incorporates delay-time modeling in a machining center’s critical sub-system, the lubrication equipment, proving its cost-effectiveness even amid parameter fluctuations. We assess the financial impact of adopting the Total Productive Maintenance (TPM) approach through Sensitivity Analysis, and compare it with established O&M policies, demonstrating the superior cost-effectiveness of our model.

Optimal maintenance policy design considering learning effects under successive performance-based contracts

Successive short-term performance-based contracts (PBCs) would not only drive the original equipment manufacturer (OEM) to improve maintenance service quality, but also decrease the financial burdens of the equipment owner. This paper presents the optimal maintenance policy incorporating both preventive maintenance (PM) and upgrade actions for continuously monitored degrading equipment under successive short-term PBCs. The improvement factor model is adopted to characterize the impact of the PM and upgrade actions. We also consider that the OEM’s learning effects will affect the cost and duration of the PM, as well as the upgrade cost. The learning effect increases as the period of the contract accumulates. To characterize the varied equipment production and degradation rates across different PBC periods, a Wiener process with covariates is developed. Meanwhile, we assume that the reward and penalty parameters of the performance-based revenue function varied each contract period, thus motivating the OEM to enhance the equipment availability. Based on that, the optimal maintenance plan for each contract period is determined to maximize the OEM’s expected net profit. Numerical examples show the superiority and examine the sensitivity of the proposed successive short-term PBCs. Managerial insights are also given to guide the implementation of the proposed maintenance policy.

A predictive maintenance policy for manufacturing systems considering degradation of health monitoring device

Health monitoring device (HMD) providing the current condition for its monitored manufacturing system is often subjected to a gradual degradation process. The degradation of the HMD may lead to inaccurate system condition monitoring data, conducting to non-appropriate maintenance decision-making. This paper presents a novel predictive maintenance policy for manufacturing systems considering degradation of its HMD. To address this issue, a method based on Kalman filter is developed for estimating the system state. However, due to the data uncertainty given by statistic noise and HMD degradation impacts, the error between estimated value and true state is unavoidable. To overcome this issue, an adaptive predictive maintenance, in which both the estimated state of system and the current state of HMD are used for maintenance decision-making, is then proposed for the system and its HMD. A cost model is developed to find the optimal maintenance policy. A case study of injection molding machine is conducted to show the applications and the benefits of the proposed maintenance policy.

Optimal task-driven time-dependent covariate-based maintenance policy

In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark.

A novel modeling framework for a degrading system subject to hierarchical inspection and maintenance policy

Most engineering systems exhibit degrading performance in their designated service times as influenced by their working conditions. Thus, inspection and maintenance actions are crucial for maintaining their performance. Among these, critical engineering systems, such as compressors and safety valves, are typically subject to a multi-level inspection scheme consisting of partial and full inspections. In this study, inspired by this inspection scheme, we propose a hierarchical maintenance model to incorporate collected system performance information from these inspection interventions. Partial inspections verify whether the system is functional or not, and then partial preventive maintenance is performed; a more accurate system state is known at the instant of full inspections, which serve as windows for the conduction of condition-based maintenance. The proposed maintenance policy is then applied to a system undergoing monotone stochastic degradation. Subsequently, the average cost per unit time in the operational phase and the sensitivity analysis with respect to variables, such as the preventive maintenance threshold, inspection intervals, and the maintenance effect factors, are investigated. Theoretical results and numerical examples are obtained to examine the applicability of the generalized model.

Grouping Maintenance Policy for Improving Reliability of Wind Turbine Systems Considering Variable Cost

Wind farms have gained wide attention due to unlimited resources and clean energy. Considering that wind turbine systems are always in harsh conditions, subsystem failures could reduce the reliability of wind turbine systems. At present, the maintenance behaviors for wind turbine systems are various (e.g., corrective maintenance, preventive maintenance) when reliability is reduced below the threshold. Considering the maintenance cost and downtime, it is impossible to repair each component in a timely manner. One of the key problems is dividing components into maintenance groups to improve maintenance efficiency. In this paper, a grouping maintenance policy considering the variable cost (GMP-VC) is proposed to improve direct-drive permanent magnet (DPM) turbine systems. Grouping modes are proposed to fully consider the stated transition probability of turbine components and the variable cost of turbine systems. A maintenance model is formulated to select components as members of the group based on a RIM-VC index. An instance is given to verify the proposed GMP-VC method. The result indicates that the proposed maintenance policy may save maintenance costs over baseline plans.

Optimization of condition-based maintenance for multi-state deterioration systems under random shock

This paper proposes a new condition-based maintenance policy for systems subject to multi-state deterioration and random shock simultaneously. Both Semi-Markov process and compound Poisson process are employed to explicitly model the exposed failure processes and dependent relationships between them. A condition-based inspection policy is designed to timely capture the states where maintenance actions are needed. The influence factors of preventive maintenance are specified as random shock and ageing process respectively. The Monte Carlo simulation technique is employed to obtain the optimal values about the proposed maintenance policy. An illustrate example about a power generator is taken into consideration to show how the newly developed maintenance policy works in real practice. The comparison analysis demonstrates that (1) the proposed condition-based inspection can effectively reveal the maintenance time points within limited inspection cost; (2) the new maintenance policy can achieve better objective values of availability and maintenance cost.

Transformer Population Failure Rate State Distribution, Maintenance Cost and Preventive Frequency Study Based on Markov Model

This work investigates the state distributions of failure rate, performance curve, maintenance cost and preventive frequency of the transformer population through the Markov Model (MM). The condition parameters data of the oil samples known as Oil Quality Analysis (OQA), Dissolved Gas Analysis (DGA), Furanic Compounds Analysis (FCA) and age were analyzed from 370 distribution transformers. This work utilized the computed failure rate prediction model of the transformer population based on MM using the nonlinear minimization technique. First, the transition probabilities for each state were adjusted based on pre-determined maintenance repair rates of 10%, 20%, and 30%. Next, the failure rate state distributions and performance curves at various states were analyzed. Finally, the maintenance costs and preventive maintenance frequency were estimated utilizing the proposed maintenance policy models and the failure rate state probabilities. The result reveals that the transition from state 2 to state 1 with a 30% pre-determined maintenance repair rate can provide an average reduction of failure rate up to 11%. Based on the failure rate state probability, an average increment of maintenance cost from RM 18.32 million to RM 251.87 million will be incurred over 30 years. In total, 85% of the transformer population must undergo maintenance every nine months to avoid reaching very poor conditions.

Optimal N-policy for the maintenance of k-out-of-n systems with dynamic minor repairs considering second-hand component income

ABSTRACT This paper proposes a maintenance policy for a k-out-of-n: F system operating to fulfil several jobs without interruptions. The system is replaced when the Nth job completes or at the kth failure, whichever occurs first. So, there are some non-failed components when a replacement is done. These components can be sold as second-hand products to continue working in other systems for a while. The price of them is also considered in the proposed maintenance policy. An optimal maintenance policy is studied to minimise long-run average cost under the constraint of relative mean operating time. Comprehensive numerical studies are done to assess the effect of the model parameters on the optimal solutions. Also, to demonstrate the applicability of the proposed plan, a data set related to wind turbine generator failures is considered as a case study.

Condition-based opportunistic maintenance policies with two-phase inspections for continuous-state systems

This paper proposes two condition-based opportunistic maintenance policies with two-phase inspections for continuously degraded systems with real-time monitoring and periodic inspections, respectively. The degradation of the components and the system is modeled by a Gamma process. Existing literature finds the optimal maintenance strategies by monitoring the performance of all components in a multi-component system. However, it maybe unrealistic and costly to perform inspections on all components for certain systems. Hence, this study considers a two-phase inspection method, that is, the system-level inspection is conducted first, and then the decision for component-level inspection is made based on the system performance. Maintenance is performed to the component when its degradation reaches the preventive maintenance (PM) threshold or the corrective maintenance (CM) threshold at a component-level inspection. Whenever a maintenance action is implemented, the other components whose degradation exceeds an opportunistic maintenance (OM) threshold will be repaired. Then the long-run expected cost for two-component systems and multi-component systems are derived based on the semi-regenerative properties and simulation method. The combination of the system-level inspection interval, OM, PM and CM thresholds is determined to minimize the long-run expected cost. Finally, this study applies the proposed maintenance policies to the battery pack systems in electrical vehicles.

Preventive maintenance for heterogeneous parallel systems with two failure modes

The parallel structure in the reliability engineering is widely used in many industrial systems such as power generation systems, pump systems, production systems and computing systems. Designing the optimal maintenance policies for these parallel systems can minimize the operation cost while achieving a satisfying system availability. Previous studies have studied various maintenance optimization for parallel systems. However, little research has considered the multiple failure modes that widely exist in many real industrial systems. This research considers a parallel system that consists of heterogeneous components subject to two types of competing failures: a one-stage catastrophic failure and a two-stage delay-time failure. Inspections are conducted periodically to identify the state of each component. Three maintenance policies are proposed in this research. To find the optimal inspection interval, we analyze the renewal process of the system to minimize the long-term expected cost per unit time. A simulation procedure is proposed to evaluate the long-term expected cost per unit time. Numerical experiments are carried out to illustrate the applications of the proposed maintenance policies. The discussion provides useful insights on choosing the best policy under different system performance requirements.

Proposed Maintenance Policy Design on Paving Molding Machine Using Reliability and Risk Centered Maintenance (RRCM) Method at PT XYZ

PT. XYZ is a company engaged in construction and manufacturing. The company focuses on paving production. Where there is a problem with paving printing machines in carrying out production, there are many productions that do not reach the target or the production process does not run optimally. The purpose of this study is to find out the performance of paving molding machines, especially in the components that most often experience damage to paving molding machines. Based on historical data on machine damage, a paving molding machine is a machine that has had the highest frequency of damage for 5 years, because this machine operates continuously to meet production targets and paving demand from customers. Reliability and Risk Centered Maintenance (RRCM) methods to be used to analyze a historical engine malfunction. The results obtained in the form of 7 proposed maintenance tasks are 2 scheduled on-condition tasks, 3 scheduled restoration tasks, 2 scheduled waste tasks. In addition, the average maintenance time interval for pillow blocks is 7 months, iron molding is 11 months, and chain is 4 months. The total cost of proposed maintenance is lower than IDR 52,938,750 or 41.7% compared to the existing maintenance cost of PT. XYZ. The development of this research is to increase the application of RCM as a basis for maintaining the balance between preventive maintenance and corrective maintenance while optimizing maintenance costs to improve engine reliability by considering risk factors as an analysis.

A maintenance strategy based on system reliability considering imperfect corrective maintenance and shocks

In this study, an innovative maintenance strategy is proposed for systems subject to normal degradation and shock process. The shock process is a non-homogeneous Poisson process, and a changing factor is considered for the dependence between the normal degradation and the occurrence intensity of shocks. The system reliability is divided into three stages, based on which maintenance activities are implemented. In Stage 1 and Stage 2, when a fatal shock occurs, corrective maintenance is performed if the number of completed corrective maintenance activities does not reach the allowed limit; Otherwise, the system is replaced. Replacement is always performed once the system reliability decreases to Stage 3. Corrective maintenance is imperfect, with a perfect or minimal effect at certain probabilities. Two control variables, the allowed numbers of corrective maintenance in Stage 1 and Stage 2, are determined to minimize the expected system cost rate, by using Particle Swarm Optimization algorithm. The proposed model and method are illustrated by an example of one sliding spool. The optimal result is verified by checking the results of feasible variables. Furthermore, the proposed maintenance policy is compared with two contrastive maintenance policies. The comparison results show the necessity and effectiveness of the proposed method.

A finite-time maintenance policy of deteriorating systems with multiple dependent performance characteristics

Condition monitoring and health management of deteriorating systems have attracted significant attentions in reducing failure rate and ensuring normal operation of systems. However, related existing methodologies usually confront difficulty when applied to systems with multivariate performance characteristics (PCs) in the presence of model uncertainty. In this work, a periodic inspection and maintenance policy based on bivariate degradation process is proposed, where each PC is modeled by a gamma process with time-scale transformation. Further, copula functions are used to model the dependency between degradation increments. The uncertainty of model parameters is estimated by the Bayesian Markov chain Monte Carlo (MCMC) algorithm. Subsequently, the maintenance policy is optimized by minimizing the expected cost under parameter uncertainty. The expectation and variance of maintenance cost are computed over a finite-time horizon. Finally, a real example of heavy machine tools is applied to illustrate the proposed maintenance model. Numerical results show that the proposed maintenance policy provides a more robust and accurate solution in maintenance decision-making for deteriorating systems, leading to lower maintenance cost compared with other policies.

A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies

Condition-based maintenance (CBM) optimization utilizing prognostic information and methods for complex systems has attracted increasing attention. This paper presents a bi-level approach to optimize a CBM policy for multi-component systems subject to stochastic and economic dependencies. The objective of the decision rule at the system level is to address if preventive and/or corrective maintenance actions are necessary regarding the predictive reliability of the system. At the component level, the aim is to identify the optimal group of components to be preventively maintained when maintenance is triggered due to the decision made at the system level. A maintenance efficacy indicator is introduced to prioritize components to be maintained by taking into account both the resulting reliability improvement quantity and the maintenance cost incurred. The optimal inter-inspection interval, the thresholds of system and component conditional reliability for triggering maintenance are jointly determined for minimization of the long-run average cost rate. A numerical study and sensitivity analysis on a 2oo3:G system are carried out to validate the performance of the proposed maintenance policy. Compared to another opportunistic maintenance policy without considering the system health condition, the proposed CBM policy leads to more cost gain and effectiveness in the maintenance decision-making.

Opportunistic perfect preventive maintenance policy in presence of masked data

In this paper, the maintenance optimization problem of multi-component system is considered. It is assumed that the exact cause of system failure might be masked. That is, the exact cause of failure is unknown, and we only know that it belongs to a set called mask set. Both opportunistic perfect preventive maintenance (OPPM) and perfect corrective maintenance are considered. Threshold of OPPM and inter-inspection interval are considered as decision parameters which are optimized using long-run cost rate criteria. The applicability of the proposed maintenance policy is investigated using an illustrative example.

Condition-based maintenance strategies for stochastically dependent systems using Nested Lévy copulas

Nowadays, industrial systems are more and more complex and components are integrated dependently. Without consideration of dependence between components often leads to overestimate the reliability of system and high maintenance cost. In this paper, a multi-component system with hierarchical stochastic dependencies is considered. Components within subsystem are strongly correlated and dependence between subsystems is heterogeneous. Thanks to its capacity of modeling asymmetric dependence structures, the Nested Lévy copula is used in this paper. Based on the multiple sources of dependence among components and hierarchical structures, a novel condition-based maintenance policy is proposed. The inspection scheme as well as maintenance decision is dynamically planned according to the collected inspection information and the influence of stochastic dependence is investigated. The numerical results show the efficiency and robustness of proposed maintenance policy on reducing the inspection and maintenance cost.

Modeling the effects of dependence between competing failure processes on the condition-based preventive maintenance policy

This paper explores how the dependence between competing failure processes affects the condition-based preventive maintenance policy. The exposed failures caused by degradation and random shock are explicitly modeled respectively through two stochastic processes, i.e. Gamma process and compound Poisson process. Condition-based inspections are scheduled by introducing a convex function describing the relationship between degradation level and inspection interval. The preventive maintenance policy is planned to improve the operation efficiency which is adversely affected by the existing failures. The effect of failure dependence on the condition-based maintenance is analyzed through the preventive maintenance threshold of which initial value is fixed and is weaken by the following arrived shocks then. Imperfect maintenance effect is also considered to describe the irreversible aging process. Monte Carlo simulation technique and genetic algorithm are both employed to obtain the optimal parameters which determine the frequencies of inspection and maintenance. An illustrate example of a gearbox in the wind power industry is studied to demonstrate how the proposed maintenance policy balances the conflicting objectives in engineering practice.

Condition-based maintenance planning for multi-state systems under time-varying environmental conditions

Systems commonly operate under time-varying environmental condition (EC). Since the EC can affect the degradation process of a system, maintenance planning can be quite challenging. This study focuses on a case where the evolution processes of the system degradation and the EC are governed by Markov processes, for which the transition rate matrix of degradation states varies with the EC. To maintain the system, an inspection/maintenance policy is adopted, where a maintenance action is carried out immediately when a failure occurs during an inspection interval or upon an inspection epoch when the system’s degradation state reaches a certain threshold. Imperfect maintenance (IM) and replacement are considered in this study, where a certain number of consecutive IM actions can be carried out before each replacement. We first derive the long-run average cost based on the semi-regenerative properties of the system, and then jointly determine the inspection interval, preventive maintenance threshold and number of IM actions before a replacement by minimizing the long-run average cost. A numerical study is conducted to demonstrate the proposed maintenance policy.

Opportunistic Maintenance Policy for A Multi-Component System Subject To Random Failures

In this paper we study an opportunistic maintenance policy (OMP) for a multi-component system. The objective is to minimize the maintenance cost while guaranteeing a minimum level of reliability for the system and for each of its components. We suppose that each component is subject to random failures and at most one spare part of it should be kept in stock or ordering at any time. The lifetime of this system will be divided into several periods. At the beginning of each period we must determine the set of actions (among many others) that will achieve the objective mentioned above. The policy OMP is characterized by two parameters; the first one is the scheduled time for spare ordering and the second one is the period of realization of the maintenance action (if any). These parameters will be derived from the joint optimization of maintenance cost and the inventory cost for each component. Finally, we will give a numerical example to explain the proposed maintenance policy and the optimization procedure.