Types Of Maintenance Activities Research Articles

Opportunistic maintenance optimization of continuous process manufacturing systems considering imperfect maintenance with epistemic uncertainty

Due to the production process for continuous process manufacturing system (CPMS) cannot be stopped, the “opportunities” for maintenance can only occur within the specified time intervals. To improve the completion rate of production process, the focus of opportunistic maintenance for CPMS is whether the maintenance is to be performed at known time. Meanwhile, several opportunistic maintenance optimization models assumed imperfect maintenance to develop more reasonable maintenance plan. However, the parameters of imperfect maintenance cannot be accurately assessed (namely epistemic uncertainty) due to inadequate knowledge of maintenance and degradation mechanisms. This epistemic uncertainty was ignored in previous studies. Therefore, a novel opportunistic maintenance optimization model for CPMSs is proposed. The opportunity time window (OTW) concept is introduced to constrain the production-constrained maintenance opportunity. Three types of maintenance actions, including do nothing, perfect maintenance, and imperfect maintenance with epistemic uncertainty, are considered during the OTW to improve the production efficiency and performance of the manufacturing system. In the context of random and epistemic uncertainty, a stochastic fuzzy flow manufacturing network (SFFMN) is established to describe dynamic production processes and evaluate machine reliability. Moreover, a simulated annealing-based adaptive immune algorithm (SAAIA) with multiple immune operators is developed to address the opportunity maintenance optimization model of CPMSs. Finally, a case study of the hot rolling manufacturing system is provided to better understand the proposed method and demonstrate its effectiveness.

Dynamic predictive and preventive maintenance planning with failure risk and opportunistic grouping considerations: A case study in the automotive industry

Recently, the Predictive Maintenance (PdM) concept has gotten increasing attention in industrial practices and academic research. It is often used with real-time data to monitor the health status, or in the best case, estimate the Remaining Useful Life (RUL) of certain components. Apart from the technical and economic challenges of data acquisition and RUL calculation, estimating RUL is not the final goal in a maintenance management system. As in industry, there are other types of maintenance activities, industrial constraints, and complexities that should be managed together to define overall maintenance planning. In this paper, a simultaneous PdM and Preventive Maintenance (PvM) planning problem of multi-machine and multi-component systems is studied. In this context, a mathematical programming model is proposed to minimize direct and indirect maintenance costs, considering opportunistic grouping of maintenance activities, unused life losses of spare parts, and breakdown costs related to failure risk. To validate the proposed method, a case study from the automotive industry (FPT Powertrain Technologies) is used, and a comprehensive sensitivity analysis is provided. The results indicate that consideration of the mentioned aspects could significantly impact maintenance planning and overall maintenance costs. Finally, the applicability of the proposed approach is discussed in managerial insights.

Comparative Study of Different Condition Indices Using Michigan Department of Transportation’s Flexible Distress Data

The condition of a country’s roadway infrastructure significantly affects its economic development. Highway agencies that manage pavements often communicate their road network condition by using single or multiple composite condition indices. Each agency relies on its condition index to make maintenance decisions and perform budget allocation. Generally, these condition indices follow a similar level of the rating scale (100 to 0, where 100 denotes perfect condition). However, because of different numbers of distress inputs with different units, different severity levels, and different deduct points and mathematical formulas, condition indices are often incomparable. Consequently, research focused on comparing different distress indices using a highway agency’s pavement distress data is lacking. The objective of this study is to provide a comparative analysis of different condition indices using Michigan Department of Transportation’s (MDOT) flexible distress data, as part of an effort to develop a new index for Michigan. In this process, 2,081 flexible pavement sections were identified from MDOT’s Pavement Management System database, and five different condition indices were evaluated and compared against MDOT’s Distress Index. Comparison results were presented through scatter plots and statistical analysis results. MDOT’s maintenance records were also used to compare the ranges of magnitudes of various indices computed at the time when various types of maintenance activities took place. Finally, this study resulted in recommendations for selecting a new condition index and calibration needs of deduct points and equations.

A production-inventory model under quality-maintenance policy with rework process in the presence of random failures and multiple assignable causes

ABSTRACT This research presents simultaneous design of production planning, maintenance strategy and control chart in which two issues are considered: (1) multiple assignable cause and (2) equipment failure. The first one means that various factors may shift the process to an out-of-control state; while the second one means that the equipment is not always available and may be suddenly failed. Also, to reduce the production costs, a rework process is performed on the nonconforming items right after the regular cycle. Furthermore, three types of maintenance activities are applied based on system conditions. The corrective maintenance is conducted when the equipment failure occurs. The preventive maintenance is accomplished when the process continues until the end of the cycle, and the predictive maintenance is performed whereas the chart issues a true alarm. To check the effectiveness of the model, three comparative studies are examined. The first one compares the suggested model with a similar model without the rework process; the second one demonstrates the importance of employing the statistical process monitoring; and the last one compares the model with a similar model without considering the equipment failure. The results show the superiority of the integrated model compared to the others in the cost-saving.

Discussion of signature‐based models of preventive maintenance

Discussion of signature‐based models of preventive maintenance

Preventive replacement policy of a system considering multiple maintenance actions upon a failure

AbstractAge‐based preventive replacement policy has been extensively studied in the literature. Upon an unexpected failure, most studies assume that only one type of maintenance actions can be executed to restore the system back to work. For example, minimal repair or corrective replacement that bring the system to respective as‐bad‐as‐old and as‐good‐as‐new states. Few studies consider both two types of maintenance actions, while predetermine the structure of the maintenance policy. Modern systems are becoming increasingly complex. Besides minimal repair and corrective replacement, imperfect repair can also be carried out to restore a failed system back to work, which brings it to a state between as‐bad‐as‐old and as‐good‐as‐new. This study investigates an optimal age‐based replacement policy, where minimal repair, corrective replacement, and imperfect repair can be carried out upon an unexpected failure with different maintenance effects. We formulate the problem as a semi‐Markov decision process (SMDP), and prove the existence of the stationary optimal policy. The optimal preventive replacement age threshold and the corresponding optimal maintenance action upon each failure are jointly obtained to minimize the long‐term average maintenance cost per time unit. A numerical study is provided to illustrate the effectiveness of our proposed model.

Life Cycle Analysis of a Steel Railway Bridge over the Operational Period considering Different Maintenance Scenarios: Application to a Case Study

In the context of bridge management, three main types of maintenance actions can be considered. Maintenance actions can be taken preventively before the predefined limit condition is reached, or as a corrective measure in case those limits have been reached. The third possibility corresponds to the so-called “doing nothing” scenario, in which no action is taken on the bridge. To be able to implement preventive maintenance, it is necessary to know the current condition of the bridge, as well as to be able to predict its performance. On the other hand, it is also important to be able to identify potentially threatening events that might occur in the analysis life period. This paper describes an integrated methodology to help bridge managers in defining an efficient maintenance program, considering the specific case of a railway bridge. The novelty of the methodology is focused on updating an existing methodology proposed by COST TU1406, by extending it to railway bridges and also by including the resilience analysis in case of a sudden event occurrence. The analysis considers a multi-hazard future scenario, in which a flood event occurs while corrosion phenomena were already in place. The results show the feasibility of the proposed methodology as a support for the establishment of an efficient maintenance schedule to prevent bridge severe degradation, as well as to establish recovery plans in case of a sudden event.

An Imperfect Grouping Maintenance Strategy for Multi-Component Systems Using the Survival Signature

This paper presents an imperfect maintenance strategy for the multi-component systems. The proposed maintenance strategy takes into account two types of maintenance actions, namely preventive maintenance (PM) and corrective maintenance (CM). The imperfect effect of PM is modeled on the basis of the hybrid hazard rate model. Meanwhile, a new structure importance measure based on the survival signature is presented. Using this new importance measure method, an adjustment function is designed to update the PM maintenance threshold. For CM actions, a decision rule relying on the criticality level of components is introduced. In order to judge the criticality level of components, a novel structure updating method based on the survival signature is proposed. Moreover, a maintenance model considering economic dependence among components is developed. A 10-component system is finally introduced to illustrate the use and advantages of the proposed maintenance strategy.

AN INTEGRATED MULTI-OBJECTIVES OPTIMIZATION APPROACH ON MODELLING PAVEMENT MAINTENANCE STRATEGIES FOR PAVEMENT SUSTAINABILITY

Addressing the multi-dimensional challenges to promote pavement sustainability requires the development of an optimization approach by simultaneously taking into account future pavement conditions for pavement maintenance with the capability to search and determine optimal pavement maintenance strategies. Thus, this research presents an integrated approach based on the Markov chain and Particle swarm optimization algorithm which aims to consider the predicted pavement condition and optimize the pavement maintenance strategies during operation when applied in the maintenance management of a road pavement section. A case study is conducted for testing the capability of the proposed integrated approach based on two maintenance perspectives. For case 1, maintenance activities mainly occur in TM20, TM31, and TM41, with the maximum maintenance mileage reaching 88.49 miles, 50.89 miles, and 20.91 miles, respectively. For case 2, the largest annual maintenance cost in the first year is $15.16 million with four types of maintenance activities. Thereafter, the maintenance activities are performed at TM10, TM31, and TM41, respectively. The results obtained, compared with the linear program, show the integrated approach is effective and reliable for determining the maintenance strategy that can be employed to promote pavement sustainability.

An Effective Predictive Maintenance Approach based on Historical Maintenance Data using a Probabilistic Risk Assessment: PHM14 Data Challenge

This paper presents an effective health assessment and predictive maintenance technique for industrial assets. The technique and algorithms applied to data sets provided by the Prognostics and Health Management Society 2014 Data Challenge. The data contains usage and part consumption for three years. In short, the usage data contains a parameter that roughly measures asset usage, and the part consumption data includes information regarding part replacement and maintenance actions. The first two years of data are considered as "Training" data and the third year is considered as "Testing" data. The proposed method built on the probability of the failure risk during training dataset. The main objective is to develop a model based on first two years data set (training) and determine the high risk and low risk times of failure for each individual asset for the third year.Training data shows many maintenance activities with 14 different codes. The principle difficulty is to detect the Preventive Maintenance (PM) in the training data. The paper presents the method in three main steps: the first step is to recognize the PM pattern based on time and type of maintenance activity via the training data. The second step is to determine the high-risk time intervals based on PM times by checking the frequency of the failures at specific times between each PM. The third step is to predict the high risk time intervals in the testing data using the information acquired from the training data. The score predicted by this probabilistic risk assessment method won the first place in the PHM Data Challenge Competition.

A Generalized Chart-Based Decision-Making Tool for Optimal Preventive Maintenance Time under Perfect Renewal Process Modeling

The most commonly used probabilistic model in reliability studies is the Perfect Renewal Process (PRP), which is characterized by the condition or type of maintenance represented: once the maintenance activities are executed, the equipment is restored to its original condition, leaving it “as good as new.” It is widely used since it represents an optimistic state when an item is replaced, assuming a perfect operational condition of the item after the maintenance. Some models have been developed for determining optimum preventive maintenance (PM) based on different criteria, and almost all aimed at PRP reliability modeling. The contribution of this paper is to analyze a model for determining the optimal preventive maintenance policy for a long time run under PRP and developing a general and chart-based tool for the problem, making it easier to solve the day-to-day practice and operation of equipment. As a result, a generalized chart was developed to support maintenance decisions through the elaboration of an original isometric table and complemented with a step-by-step methodology to determine the optimum time in which the preventive maintenance activities must be implemented. In most cases, these types of maintenance activities will consider a replacement activity.

Prediction of restoration factor for various maintenance types of railway systems using analytical hierarchy process

Purpose For repairable systems (RSs), reliability estimation is generally performed using virtual age models. Virtual age models consider the effect of maintenance actions by reducing system age using restoration factor (RF). RF is generally estimated from system failure data using various statistical methods. However, RSs such as railway systems experience various types of maintenance actions at different times during their life cycle. To consider all these different types of actions, we need multiple RFs in the virtual age model. As failure data are limited, the estimation of so many parameters becomes a complex problem and it can lead to erroneous inferences. These RFs are representative of effects of maintenance activities on the system. Therefore, these can be predicted from the information about the maintenance actions performed on the system. The paper aims to discuss these issues. Design/methodology/approach The paper considers different types of maintenance actions to predict RF of the system. These maintenance actions involve the replacement of components at some level of assembly. Each component in an assembly has its own impact on assembly restoration. RF for assembly/systems can be obtained by aggregating effects of multiple component replacement using analytical hierarchy process . The RF values obtained for different types of maintenance actions are then used to calculate the virtual age of the system at different failure points. Using these virtual age failure points, suitable distribution is fitted and parameters are estimated. The distribution and parameters provide information about reliability of the system at any point of time. Findings This paper provides an easier approach that gives different RFs for different types of PM and CM. To calculate RFs, it considers the impact of maintenance actions performed as well as the impact of the component on which they are performed. It is simpler and gives more consistent results than other approaches, which estimate RF using different statistical methods. Originality/value This paper provides an alternative approach to predict RF parameters instead of estimating these parameters using statistical methods. Estimation of parameters using different statistical methods is complex in nature and gives erroneous and inconsistent results. The approach given in this paper is simpler and gives more reliable results. This approach can be useful in estimating parameters for RSs when failure data are limited.

Concept of the FMEA method-based model supporting proactive and preventive maintenance activities

Proper planning of preventive maintenance (PM) is crucial in many industries such as oil transmission pipelines, automotive and food industries. A critical decision in the PM plans is to determine frequencies and types of maintenance actions in order to achieve a certain level of system availability with a minimum total cost. For any given interval, a decision must be made to perform one of the three actions on each component; inspection, preventive repair and preventive replacement. Any of these activities has different effects on the reliability of the components and the corresponding distinct costs based on the required recourses. The intention of the authors is to develop a concept of supporting the process of planning and scheduling of preventive and proactive activities using the FMEA method for failure analysis. According to the concept of sustainable production, the failure will be assessed from different perspectives, not only in terms of production, but also considering operators and process safety as well as threats to the environment. The concept developed and presented in the paper in combination with IT support is the basis for risk assessment and establishment of cause and effect relationships to prevent failures and / or minimize their consequences.

Data-Driven Prediction of Unscheduled Maintenance Replacements in a Fleet of Commercial Aircrafts

Aerospace industries have become increasingly concerned about system availability and reliability. Data driven based prognosis is an emerging application aimed at building predictive models from readily available maintenance and operational databases. After validation, these models can be integrated into PHM systems to monitor equipment health and predict component failures before such events disrupt operations. For this project legacy data collected from two databases associated to a fleet of civil aircrafts during a period spanning 5 years have been used. The first database contains Central Management System (CMS) data (BIT messages and Flight Deck Effects), the second logs of maintenance activities. Part of the data collected from 2012 to mid 2015 have been used for the learning phase the rest spanning 2012-2016 period have been used for validation The goal is to predict failure events within an interval ranging from two to ten flights in advance to avoid unscheduled maintenance activities and operational disruptions. Hence two flights represent the minimal notice period/prognostics horizon whilst 10 flights is the maximal acceptable wasted life. Data-driven based prognostic uses pattern recognition and machine learning techniques to train historic data. In the proposed approach both techniques have been used. Through Support Vector Machine a prognostics anomaly detection step is initially performed to select the flight legs candidate for a prognostics alert. In a further step a subspace technique, borrowed from image processing domain and named Eigenface, allows to produce the signatures of the different types of maintenance actions and a template matching algorithm determines among the prognostics alert candidates the component to be replaced. Several tests have been conducted for different types of replacements and results will be presented using Receiver Operating Characteristic (ROC) curves and precision/recall metrics. Information contained in ROC allows the airliner to identify, according to its economic criteria, the optimal prognostics operating points.

Application of Multivariate Control Charts for Condition Based Maintenance

Condition monitoring is the foundation of a condition based maintenance (CBM). To relate the information obtained from the condition monitoring to the actual state of the system, it is usually required a stochastic model. On the other hand, considering the interactions and similarities that exist between CBM and statistical process control (SPC), the integrated models for CBM and SPC have been developed. These models apply control charts as a condition monitoring technique, and the inference about the operational states of the system is based on the collected information about the quality of the produced items. Finally, it is decided whether to implement certain type of maintenance actions. This paper describes the application of multivariate control charts as a condition monitoring technique for CBM purposes. To this end, an integrated model is developed, while it is used a chi-square control chart. Also, to determine the inspection time points, a constant hazard policy is applied.

Maintenance scheduling using data mining techniques and time series models

Condition-based maintenance (CBM) should be derived carefully to reduce maintenance costs along with useless maintenance shifts and to predict ideal time to do the maintenance. In this paper, a new method is proposed by the combination of data mining techniques and time series models to schedule maintenance activities. Considering a real database which contains failures and values of factors degrading the pump in the time of failure, a clustering algorithm is used to categorize failures based on the similarity in types of maintenance activities. Then, rules are extracted for characterizing the clusters and presenting a range for each factor by applying a proper association rule algorithm. Subsequently, time series models are applied to predict the time period that a factor may meet its rule’s range. Thus, a novel method is presented for a relative comparison between rules and predicted factor’s values and a prognostic scheduling is designed with respect to the effects of previous maintenance activities. The results of numerical experiments reveal that the proposed method can effectively determine when and which maintenance activities should be performed.

A control-chart-based queueing approach for service facility maintenance with energy-delay tradeoff

Maintenance planning and energy consumption control are critical issues in facility operations management. In practice, the energy consumption of a facility, which will be affected by the operation condition, is closely connected with the associated maintenance policy. Specifically, for an energy-consuming service system, though a frequent maintenance activity can keep the facility in a good condition with low energy consumption, it makes the delay time longer and leads to a poor customer experience. In this paper, we study a single-server queueing system with different energy consumption levels in the associated running states to address the conflict between energy consumption and customer delay. Two types of maintenance activities are implemented for the server, i.e., the planned maintenance and the reactive maintenance. The planned maintenance is adopted based on a frequency parameter at the beginning of an idle period, and the reactive maintenance is initialized by the Shewhart’s individual control chart (condition-based maintenance). To capture the energy-delay tradeoff, our objective is to develop an optimal maintenance policy that minimizes the long-run expected total cost of the system under a customer waiting time constraint. Numerical experiments are conducted to analyze the problem, in which useful managerial insights are obtained for the optimal maintenance policy. The results demonstrate the robustness of the proposed maintenance model, its advantage over the model without control chart, and its applicability in general situations.

Continuous-Observation Partially Observable Semi-Markov Decision Processes for Machine Maintenance

Partially observable semi-Markov decision processes (POSMDPs) provide a rich framework for planning under both state transition uncertainty and observation uncertainty. In this paper, we widen the literature on POSMDP by studying discrete-state discrete-action yet continuous-observation POSMDPs. We prove that the resultant $\alpha$ -vector set is continuous and, therefore, propose a point-based value iteration algorithm. This paper also bridges the gap between POSMDP and machine maintenance by incorporating various types of maintenance actions, such as actions changing machine state, actions changing degradation rate, and the temporally extended action “do nothing.” Both finite and infinite planning horizons are reviewed, and the solution methodology for each type of planning horizon is given. We illustrate the maintenance decision process via a real industrial problem and demonstrate that the developed framework can be readily applied to solve relevant maintenance problems.

Measuring pavement maintenance effectiveness using Markov Chains analysis

This study assesses the effectiveness of road network pavement maintenance using Markov Chain analysis based on historical costs and road roughness progression data. The analysis is based on a database developed by the State of Victoria, Australia, consisting of 2197 road sections. The analysis measures maintenance effectiveness using the criterion of whether road sections remain in the same condition state or move to the next worst state based on a predefined roughness value. Principal inputs for the stochastic models, such as the development of transition probability matrices and associated cost functions, are discussed. Results show that, within the current budget envelope and when undertaking only routine maintenance, the probability of road sections remaining in the same condition state, which is a determinant of maintenance effectiveness, exhibits a declining tendency from good to worst condition states. This finding prompts the discussion on when to begin intervention using high types of maintenance together with their respective higher expenditure levels. The method discussed in this paper, provides tool for road authorities to select the appropriate maintenance action for each condition state of pavements based on the comparison analysis of the results of Markov Chain for different types of maintenance actions.

A Computational Framework for Infrastructure Asset Maintenance Scheduling

This paper presents a computational framework for the optimization of maintenance activities for infrastructure assets, with particular emphasis being placed on road network assets. This framework incorporates degradation and maintenance models for infrastructure assets along with multi-objective optimization for searching optimal maintenance schedules. Given a schedule of maintenance actions, the future performance is estimated by means of a Monte Carlo simulation that enables to account for inherent uncertainties. The design variables of optimization are the types of maintenance actions and their timing over the planning horizon. The objectives are to minimize both the asset degradation and maintenance cost. This includes satisfaction of constraints representing performance demands. The proposed framework is general and can be applied to different types of infrastructure assets. The numerical results, obtained for a road bridge managed by a highway operating agency, demonstrate the validity and usefulness of the proposed framework.