Proposed Maintenance Strategy Research Articles

IoT-Enabled Fault Prediction and Maintenance for Smart Charging Piles

With the application of the Internet of Things (IoT), smart charging piles, which are important facilities for new energy electric vehicles (NEVs), have become an important part of the smart grid. Since the smart charging piles are generally deployed in complex environments and prone to failure, it’s significant to perform efficient fault diagnosis and timely maintenance for them. One of the key problems to be solved is how to conduct fault prediction based on limited data collected through IoT in the early stage and develop reasonable preventive maintenance strategies. In this paper, a real-time fault prediction method combining costsensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) is proposed. CS-LR is firstly used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data. The feasibility of the proposed model is illustrated through the case study on fault prediction of real-world smart charging piles. To demonstrate the advantage in prediction accuracy, the proposed fault prediction model is compared with the classic baseline models such as LR, SVM, decision tree (DT), K-nearest neighbor (KNN), and back propagation neural network (BPNN). Finally, based on the proposed fault prediction method, preventive maintenance based on a probability threshold with the minimum total expected cost is proposed. Simulation results show that the proposed maintenance strategy has a better performance in reducing the total maintenance cost compared with traditional periodic maintenance. This is valuable for the development of preventive maintenance strategies for repairable systems under early real-time monitoring data.

Cost effective maintenance strategy for centrifugal pumps using reliability centred maintenance

The aim of this work is to obtain a maintenance strategy that is cost effective for centrifugal pumps using reliability centred maintenance (RCM). The components of the centrifugal pump used for the study are the shaft, impeller, bearing, mechanical seal, coupling, shaft sleeve, wear-ring casing, suction flange and discharge flange. The motor that drives the pump was also considered. Two basic tools of the RCM strategy were applied. These are the failure modes, effects and criticality analysis (FMECA) and logic tree analysis (LTA). Failure data for a period of 10 years about a centrifugal pump was collected and the FMECA was applied to determine the failure modes, the effects of the failure, the criticality of the components, the mean time between failure (MTBF), the failure rate and the mean time to repair (MTTR). The risk priority numbers of all the components were estimated. The LTA was applied to come up with a new maintenance strategy for each of the components of the pump. The maintenance cost consisting of spare part cost and labour cost for the pump components were estimated for the current maintenance practice as well as the new or proposed maintenance practice. The most critical components observed were the motor, shaft, impeller and the mechanical seal. The motor has the highest MTTR value (58 hrs) while the mechanical seal has the highest failure rate (8.06E-04 per hr). The proposed maintenance strategy leads to reduction in spare part cost in a number of components, with the largest reduction of 19.88% occurring in mechanical seal. The proposed strategy also leads to reduction in labour cost in motor, shaft, impeller and the mechanical seal with the values 33%, 25%, 50% and 25% respectively. The methodology could be applied to other devices.

Imperfect Preventive Maintenance Optimization with Variable Age Reduction Factor and Independent Intervention Level

Maintenance policies are crucial for ensuring the reliability, safety, and longevity of a system, as well as reducing the risk of accidents. Preventive maintenance (PM) is an effective strategy to keep equipment and systems in good working order by fixing potential issues before they cause downtime or safety hazards. However, optimizing the time intervals between PM activities is essential for minimizing the overall maintenance cost. This paper proposes an innovative approach that considers the intervention level of maintenance activities as an independent variable of PM times. The approach provides greater flexibility in creating maintenance plans, as it considers practical aspects that may impact maintenance activities beyond the time interval between PMs. The proposed approach uses a reliability model that incorporates imperfect preventive maintenance and a variable improvement factor based on age reduction. The improvement factor of each preventive maintenance activity (PMA) is defined based on the intervention level of the activity itself, which is determined by the number of tasks performed, execution time, and the number of items replaced in the maintenance plan. The proposed maintenance strategy determines not only the optimal times for PMAs and the respective intervention level but also the optimal number of maintenance activities that minimize the total maintenance cost along a fixed and user-defined planning horizon. The effectiveness and precision of the approach have been demonstrated through a series of numerical examples and a comprehensive case study involving three heat exchangers situated within the hydroelectric power plant.

A closed-loop maintenance strategy for offshore wind farms: Incorporating dynamic wind farm states and uncertainty-awareness in decision-making

The determination of maintenance strategies is subject to complexity and uncertainty arising from variable offshore wind farm states and inaccuracies in model parameters. The most common method in the existing studies is to adopt an open-loop approach to optimize a maintenance strategy. However, this approach lacks the ability to capture periodic operational state of the wind farm and the awareness of eliminating uncertainty. Consequently, the determined strategy is inadequate to instruct maintenance activities, inducing excessive revenue losses. In this paper, a closed-loop maintenance strategy optimization method is proposed for decision-makers to identify a more profitable manner of wind farm maintenance management. The life-cycle maintenance optimization problem is decomposed into a sequence of sub-optimization problems covering multiple time periods by using a rolling-horizon approach. Each sub-optimization problem is intentionally designed based on the monitored state of the wind farm and the available reliability, availability, and maintainability (RAM) database. Meanwhile, the decision maker consciously mitigates the parameter uncertainty in the maintenance model gradually by updating the current database. Compared to conventional strategies covering the entire lifetime of wind farms, the proposed maintenance strategy is periodically adjusted to provide a series of sub-strategies. The proposed approach was applied in a simulation experiment, a generic small-scale offshore wind farm, to assess its performance. Computational results show that adapting maintenance strategies based on the current state of the wind farm can reduce revenue losses in comparison to conventional open-loop strategies. In addition, the benefits of updating the RAM database in decreasing revenue losses is revealed.

Generalized non-renewing replacement warranty policy and an age-based post-warranty maintenance strategy

Nowadays, to protect the interests of customers, quite a few warranted products are sold with some kinds of repair thresholds. For example, a mobile phone manufacturer may provide a replacement service if the phone sold still cannot work after several repair attempts or the cumulative repair time reaches 7 days or 1 month. Motivated by this observation, we study a non-renewing replacement warranty policy with a repair number threshold and a repair time threshold, in which the manufacturer has responsibility for replacing a product when the number of failures is beyond the repair number threshold or the cumulative repair time reaches the repair time threshold, whichever occurs first. In addition, an age-based post-warranty maintenance strategy is developed for the non-renewing replacement warranted product, which provides a lower cost rate than the existing ones. Based on the indicator function method and renewal process theory, we derive the product’s life cycle cost rate. Finally, considerable numerical experiments are carried out to show the influences of various parameters on the proposed maintenance strategy.

Station-keeping strategy for tethered drag sail-based transverse formation

During general 2-satellite formation, the relative baseline between satellites periodically changes, resulting in limited time for interferometric imaging and poor observation quality. This study incorporates a drag sail into the tethered satellite system, where the chief satellite moves in a low Earth orbit (LEO) and connected deputy drag sail is in a non-Keplerian displaced orbit. Given the free-molecule flow property of the atmosphere in LEO, the drag sail can simultaneously provide normal pressure and tangential stress. By adjusting its attitude appropriately, the sail can move synchronously with the chief satellite with respect to a fixed relative position, during which a fixed baseline for formation flying can be achieved. Because the component of the baseline perpendicular to the ground tracks of the satellite is constant, the proposed system is denoted as a transverse formation. To better investigate the system, the nonlinear characteristics are analyzed for the controlled dynamical models. Considering the complexity of aerodynamic force based on rarefied gas dynamics, feasible formation configurations are investigated from the perspective of the control force direction. Then, a control strategy is provided to maintain the formation configuration based on the linear quadratic regulator (LQR) control framework. The parameter optimization of LQR control considers the coupling degree of modals, control force, and error. The attitude of the sail is inversely computed based on the Taylor expansion, and the results show that the proposed maintenance strategy is successfully realized with the smooth rotation of the drag sail.

Optimum maintenance strategy for CRTS II slab track based on time-dependent system reliability

CRTS (China Railway Track System) II slab track is a vertical multi-layer and longitudinal anisotropic strip system, which has been widely constructed in China’s high-speed railway. In this study, an innovative optimum maintenance strategy is proposed for CRTS II slab track, based on the concept of time-dependent system reliability and life-cycle cost analysis. System limit state function of the track structure is constructed with consideration of important serviceability failure modes. The time-dependent system reliability of the track structure is investigated and utilized as the indicator of track performance, which is estimated by an innovative outcrossing method based on the system limit state function considering time-dependent correlation among failure modes. At each maintenance time, all critical failure modes and components are identified and repaired to decrease the system failure probability under the acceptable level. Application of the proposed maintenance strategy is demonstrated by a worked example of a track structural system on bridge. It is found that: (1) appropriate maintenance strategy is important to ensure service reliability of the track system and extend the service life; and (2) the proposed maintenance strategy can be utilized to predict when, where, and what failure mode for maintenance and it can help to minimize the expected life-cycle cost without compromising the service reliability of the track structure.

A Predictive Maintenance Strategy Using Deep Learning Quantile Regression and Kernel Density Estimation for Failure Prediction

Failure prediction and maintenance decision-making are two core activities in a prognostics and health management (PHM) system. However, they are often studied independently and hierarchically. The main goal of this article is to combine system failure prediction with maintenance decision-making to develop a predictive maintenance strategy. System failure prediction is achieved by constructing an ensemble model of deep autoencoder (DAE), long short-term memory (LSTM), quantile regression (QR), and kernel density estimation (KDE), namely DAE-LSTMQR-KDE. Then, based on the probability density of system failure time obtained from the ensemble model, a replacement cost function (RCF) and an ordering cost function (OCF) are proposed to support maintenance and inventory decisions. Finally, optimal decisions are determined by minimizing the two cost functions. A score equal to 246.59 and a coverage width-based criterion (CWC) index equal to 0.35 were obtained when the DAE-LSTMQR-KDE ensemble model was applied to the C-MAPSS dataset, while the average maintenance cost rate (MCR) of the proposed maintenance strategy was 0.74. The results demonstrated that the proposed prediction and maintenance method outperforms several state-of-the-art methods. In addition, different cost structure scenarios are also investigated to illustrate the flexibility of maintenance decisions based on failure prediction information.

Improved maintenance strategy for the wind turbine system under operating and climatic conditions

This paper studies and proposes a novel joint policy of production, imperfect and priority maintenance for a wind turbine system connected to battery storage and supply grid. The failure rate of wind farm is closely related to production rate and working time. The objective of this paper is to establish an economical energy production and imperfect maintenance plans minimizing the various costs incurred, taking into account the electricity demand variation, uncertainty of wind velocity, and the service level. The proposed maintenance strategy based on the priority and selective maintenance actions aims to choose the priority components for maintenance, while minimizing the total maintenance cost and ensuring a minimum reliability level for the wind turbine system. To achieve the latter goal, we formulate the reliability model of the wind turbine components by considering the influence of operating and environmental conditions. Numerical examples and sensitivity analyzes are presented to illustrate the significance and the effectiveness of the proposed methodology.

A fault location strategy based on information fusion and CODAS algorithm under epistemic uncertainty

Application of new technology in modern systems not only substantially improves the performance, but also presents a severe challenge to fault location of these systems. This paper presents a new fault location strategy for maintenance personnel to recover them based on information fusion and improved CODAS algorithm. Firstly, a fault tree is adopted to develop the failure model of a complex system, and failure probability of components is determined by expert evaluations to handle the uncertainty problem. Moreover, a fault tree is converted into an evidence network to obtain importance degrees, which are used to construct a diagnostic decision table together with the risk priority number. Additionally, these results are updated to optimize the maintenance process using sensor information. A novel dynamic location strategy is designed based on interval CODAS algorithm and optimal fault location strategy can be obtained. Finally, a real system is analyzed to demonstrate the feasibility of the proposed maintenance strategy

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.

A new integrated strategy for optimizing the maintenance cost of Production systems using reliability importance measures

This paper presents an opportunistic condition-based maintenance strategy for multi-component systems. This strategy allows the selection of systems’ components to be replaced or to undergo maintenance repairs based on the effect of importance measure concepts (IMC). IMC models proceeds by determining the contribution of each component in terms of success or failure degrees respecting two factors: (1) the probability of components to remain in operations under various conditions, and (2) the location of such components in the system structure. However, evaluating IMC factors is not so easy for complex systems; it requires knowing their operational structure, their reliability, and the calculation of the IMC degree and ranking them for the selection. Then, the IMC results are integrated into an opportunistic maintenance strategy to constitute a maintenance system's framework. In addition, a multicomponent system composed of 14 nodes and 23 links is provided to illustrate the proposed maintenance strategy that incurs a maximum of cost saving and to reduce spare parts unnecessarily usage.

Optimizing imperfect preventive maintenance in multi-component repairable systems under [formula omitted]-dependent competing risks

Existing maintenance optimization models for single-component repairable systems are extensively studied. However, maintenance optimization in multicomponent reparable systems (MCRS) is still a challenging problem, especially when dealing with stochastic dependent (s-dependent) competing risks between components. This paper develops a novel reliability-centered maintenance optimization model for MCRS operating under s-dependent competing risks. The s-dependency is captured by the joint reliability function constructed using Copula method and unknown parameters are estimated by maximum likelihood estimations (MLE). Maintenance strategies are carried out both at components and system levels. At components level, a preventive maintenance (PM) is performed whenever the reliability of the MCRS reaches a given threshold. PM actions are imperfect and modeled using the hybrid hazard rate approach. At system level, the whole system is preventively replaced after a certain number of PM cycles. The objective of the proposed maintenance strategies is to determine the optimal reliability threshold together with the optimal number of PM cycles that minimize the total expected maintenance cost rate in the infinite time horizon. The optimality conditions of the resulting maintenance optimization problem are formally discussed via two propositions. A case study that investigates a ball screw mechanism with multiple components is provided to validate the proposed approaches.

Reliability and maintenance models for a time-related multi-state flow network via d-MC approach

This study applied the approach of minimal cuts for demand d (d-MC) to evaluate the time-related reliability of a multi-state flow network (MSFN). The maximal capacity vectors that did not meet the demand rate were generated by a proposed algorithm. The Weibull reliability function was adopted to derive the probability distribution for arc capacity in the MSFN. In terms of the generated d-MCs and derived probability distributions, the MSFN system unreliability was calculated by the recursive sum of the disjoint products. Furthermore, the system reliability was 1 – system unreliability, which is a performance index of an MSFN. Based on the system unreliability, a maintenance model with three strategies was proposed to retain the service level of the MSFN. Experimental results show the performance of the proposed maintenance strategies. The advantages of different maintenance strategies are further discussed for managers to determine an appropriate alternative to maintain MSFN capability.

Integrated condition‐based maintenance modelling and optimisation for offshore wind turbines

AbstractMaintenance is essential in keeping wind energy assets operating efficiently. With the development of advanced condition monitoring, diagnostics and prognostics, condition‐based maintenance has attracted much attention in the offshore wind industry in recent years. This paper models various maintenance activities and their impacts on the degradation and performance of offshore wind turbine components. An integrated maintenance strategy of corrective maintenance, imperfect time‐based preventive maintenance and condition‐based maintenance is proposed and compared with other traditional maintenance strategies. A maintenance simulation programme is developed to simulate the degradation and maintenance of offshore wind turbines and estimate their performance. A case study on a 10‐MW offshore wind turbine (OWT) is presented to analyse the performance of different maintenance strategies. The simulation results reveal that the proposed strategy not only reduces the total maintenance cost but also improves the energy generation by reducing the total downtime and expected energy not supplied. Furthermore, the proposed maintenance strategy is optimised to find the best degradation threshold and balance the trade‐off between the use of condition‐based maintenance and other maintenance activities.

Joint optimization of production, quality control and maintenance for serial-parallel multistage production systems

This paper presents a joint model of production, quality control and preventive maintenance for a serial-parallel multistage production system. In each stage, there are multiple machines to meet the productivity and line-balance requirement. The machines deteriorate with usage and thus influences the product quality. A make-to-stock production policy is adopted to provide protection to stock against uncertainties. All items processed in each stage undergo quality checks. Conforming items are delivered to the next stage for further processing, whereas non-conforming items are scrapped. During production runs, based on the quality information feedback, preventive maintenances are performed to improve the machines reliability and hence the product quality. At the end of production runs, machines are inspected and maintained if necessary. The novelty of the proposed maintenance strategy consists in that both the machine structure importance measure and productivity are considered when selecting machines for maintenance. It aims to simultaneously determine the length of production run, quality control-threshold and maintenance-threshold such that the average cost rate is minimized. A stochastic mathematical model is developed which is solved by a simulation-based optimization approach coupling Monte Carlo Simulation and genetic algorithm. Finally, an illustrative example and some comparisons are provided to demonstrate the model.

Multi-objective imperfect maintenance optimization for production system with an intermediate buffer

In several production systems, buffer stocks are built between consecutive machines to ensure the continuity of supply during interruptions of service caused by breakdowns or planned maintenance actions. However, in previous research, maintenance planning is performed individually without considering buffer stocks. In order to balance the trade-offs between them, in this study, an integrated model of buffer stocks and imperfective preventive maintenance for a production system is proposed. This paper considers a repairable machine subject to random failure for a production system by considering buffer stocks. First, the random failure rate of a machine becomes larger with the increase of the number of random failures. Thus, the renewal process is used to describe the number of random failures. Then, by considering the imperfect maintenance action reduced the age of the machine partially, a mathematical model is developed in order to determine the optimal values of the two decision variables which characterize the proposed maintenance strategy and which are: the size of the buffer stock and the maintenance interval. The optimal values are those which minimize the average total cost per time unit including maintenance cost, inventory holding cost and shortage cost, and satisfy the availability constraint. Finally, a heuristic procedure is used to solve the proposed model, and one experiment is used to evaluate the performance of the proposed methods for joint optimization between buffer stocks and maintenance policy. The results show that the proposed methods have a better performance for the joint optimization problem and can be able to obtain a relatively good solution in a short computation time.

Remote handling strategy and prototype tooling of the ITER vacuum vessel pressure suppression system bleed line valve assembly and rupture disk assembly

As part of the development work performed under the ITER Robotic Test Facility (IRTF) program, development and substantiation of the maintenance strategy for the Vacuum Vessel Pressure Suppression System (VVPSS) must be investigated. The VVPSS is a key safety aspect of the vacuum vessel of ITER. It ensures that after a coolant leak, and subsequent pressure event, the vessel is protected from the expanding gasses by controlling and venting them appropriately. After an Ingress of Coolant Event (ICE), or during maintenance, the bleed line valve and rupture disk assemblies will require removal and replacement. During removal the confinement function of vessel must be retained. The remote handling of these components while maintaining the first confinement boundary is challenging due to the access restrictions around the pipe flange and environmental conditions in the area. To understand and develop the remote handling strategy for ITER, prototype tooling a mock-up environment has been created. This allows development and confidence in the proposed maintenance strategy leading to the final solution for use at ITER. In this paper we will discuss the prototype tests, the strategies and challenges which need to be overcome for the ITER application, and design considerations that must be taken to future designs to achieve a feasible solution for ITER.

Dynamic maintenance strategy with iteratively updated group information

Maintenance grouping methods such as the rolling horizon approach are effective in reducing maintenance costs of multi-component systems. Despite the theoretical advancements of this approach, it still faces three challenges. First, the extensively adopted minimal repair assumption upon failures limits its application. Second, opportunistic maintenance upon corrective maintenance is overlooked, unable to fully take advantage of economic dependence. Third, maintenance plans are not based on actual maintenance history and health information, which may increase failure risks. To address these challenges, this paper formulates a novel dynamic planning framework that captures economic dependence in both preventive and opportunistic replacement. Unlike conventional approaches that restrict all maintenance activities into a finite planning horizon, our proposal focuses on activity-to-activity scheduling without specifying the horizon. As such, the subsequent maintenance schedule is dynamically updated once a system maintenance is executed. A flexible dynamic programming algorithm is developed to optimize the maintenance grouping, and the strategy framework is further extended to condition-based maintenance scenarios. The effectiveness and generality of the proposed maintenance strategy are demonstrated by numerical experiments.

Effectiveness Analysis of the Application of the Failure Mode and Effect Analysis Method with Failure Defense Task for Reliability Management Improvement in Sea Water Desalination System of PLTU Paiton Baru (1 X 660 MW)

PT PJB UBJOM Paiton Baru as the O & M Service Manager of PLTU Paiton Baru 1 x 660 MW has approved the concepts and policies of PT PJB on the Reliability Management Program using the FMEA method along with the Failure Defense Task (FDT). However, the FMEA method that has been impelemented not yet effective. First, because first has not been aligned with existing reference books and international journals where there is no process of determining the value of Severity (S), Occurrence (O), and Detection (D), and calculation of Risk Priority Number (RPN), and the process of decreasing the risk value by using the right Failure Defense Task. Second, based on the evaluation of the results of the site visit at UBJOM Paiton Baru for Main Equipment and Sub Equipment for Seawater Desalination Systems, among 21 Sub-Systems in the Seawater Desalination System using Reverse Osmosis Technology, only 7 Sub-Systems have conducted The FMEA and FDT Workshop or 33.3% of the total Sub-Systems, and 14 Sub- Systems have not conducted FMEA and FDT Workshops or 66.6% of the total Sub-Systems. Third, based on further research on the results of the FMEA and FDT Workshop at UBJOM Paiton Baru in 5 Sub- systems namely Mechanical Accelerated Clarifier, Air Scouring Filter, Self Cleaning Filter, Ultra Filtration Device, Energy Recovery Device, that for determining Failure Mode sourced from Manual Book , PM Base, Existing Unit, and Browsing the Internet, so there is no data in identifying Failure Mode. Then FDT for interval 1Y (Annually) is an Overhaul with several types of inspections that have not been implemented / executed for Mechanical, Electrical, Control & Instrumentation Skills which include Assembly and Disassembly, Visual Inspection, repair or replacement if needed according to the results of Visual Inspection, testing, I / O Check , and calibration, so that FDT with Frequency 1Y (Annually) has not been effective. Meanwhile, based on the results of the Workshop on Failure Mode and Effect Analysis (FMEA) and FDT which has been aligned with reference books and international journals, by calculating the Risk Priority Number (RPN) on Failure Mode - Failure Mode identified and assessed Severity , Occurrence and Detection Levels for 6 Sub Systems, 14 Failure Modes have been mapped with risk categories of Major Risk, 31 Failure Modes with risk categories of Moderate Risk and 2 Failure Mode with risk categories of Minor Risk according to Ranking based on Risk Priority Number. From 47 Failure Modes that have been mapped risk categories, have been submitted for approval Proposed Maintenance Strategy as an effort in recalculating the value of Risk Priority Number (RPN), based on the results of Review and Evaluation of FMEA and Existing FDT Workshop Data Results and Analysis of Equipment Condition