Preventive Replacement Research Articles

A condition-based maintenance policy for a two-component balanced system with dependent degradation processes

Preventive maintenance of balanced systems has garnered increasing research attention due to its significance in production and servicing processes. This paper investigates the optimization problem of a condition-based maintenance policy for a two-component balanced system with dependent degradation processes. The system’s degradation follows a bivariate gamma process. Once the degradation level of one component exceeds a critical value, the system fails and triggers an immediate corrective replacement. Moreover, if the degradation difference between two components exceeds a threshold, the system is in an unbalanced state, which incurs a penalty cost. Periodic inspections are implemented to reveal the degradation of the system. At each inspection epoch, the decision-maker chooses an action among do-nothing, repair, and preventive replacement. Different from previous research, a repair action does not reduce the degradation levels of either component but increases the degradation level of the lower one to rebalance the system. The objective is to determine the optimal condition-based maintenance policy that minimizes the long-run average cost rate. The optimization problem is analyzed in a semi-Markov decision process framework. A policy-iteration dynamic programming algorithm is developed to derive the optimal policy. A numerical example is presented to illustrate the effectiveness of the proposed approach.

Optimal maintenance policies for a k-out-of-n system with replacement bias costs

In this paper, the issue of determining an optimal age replacement is explored by incorporating minimal repair, preventive replacement, and corrective replacement into a k-out-of-n system subject to shocks. The k-out-of-n system in question consists of n identical components, and functions if at least k components function. Each shock causes the failure of a random number of components; if at least n-k + 1 components fail, the system fails and implements corrective replacement, otherwise the system is minimally repaired. The system is scheduled to implement preventive replacement before failure at age T or at the complement of a random working cycle, whichever occurs first or last. In order to balance the bias time between preventive replacement and corrective replacement, a replacement bias cost is also taken into account for planning replacement policies. The present paper develops a two-phase maintenance methodology and determines the optimal number of components and preventive replacement age that minimize the expected cost rate functions. For each model, the expected cost rate function is formulated analytically and optimized theoretically, and a numerical example is given to illustrate the applicability of the proposed methodology.

Reliability–flexibility integrated optimal sizing of second‐life battery energy storage systems in distribution networks

AbstractSecond‐life batteries (SLBs), which are batteries retired from electric vehicles (EVs), can be used as energy storage systems to enhance the performance of distribution networks. Two issues should be addressed particularly for the optimal sizing of SLBs. Compared with fresh batteries, the failure rate of SLBs is relatively high, and timely and preventive replacement is needed. In addition, the flexibility introduced by EVs and installed SLBs should be coordinated to achieve optimal economic benefits. This paper focuses on the efficient utilization of SLBs by highlighting reliability‐flexibility concerns in optimal sizing. The model is formulated as a bi‐level model. On the upper‐level, considering the operational reliability constraints of SLBs, decisions regarding the investment and replacement of SLBs are optimized. Distribution network operations are improved on the lowerlevel, with an effective spatiotemporal flexible dispatch strategy for EVs. Finally, a linearized process for the optimal sizing of SLBs is presented and efficiently implemented. The Sioux Falls network and IEEE 69‐node distribution network are coupled as the test system. According to the simulation results, when the state of health of the SLBs decreased to 70%, the conditions were unreliable. The differences in the optimal SLB size and costs considering reliability and flexibility are highlighted.

A general inspection and replacement policy for protection systems subject to shocks with state dependent effect

This paper proposes a new maintenance policy for protection systems which fail due to competing causes: internal deterioration and fatal external shocks. The study is motivated by a real problem in a power distribution company, in which a circuit breaker is designed to protect an electrical network from critical events such as overloads and short circuits. The failure of the system is hidden and can lead to a disaster if it is not replaced before a demand is received. So, to improve its readiness, we have developed a new two-phase (inspection & replacement) policy for a protection system with a heterogeneous population. Heterogeneity reflects factors such as variability in the quality of spare parts or in the installation of the components, which can be poorly or properly executed, resulting in some having a short life, while others may achieve a long life. Inspections are imperfect, which induces misclassification. The deterioration process is modeled via the delay-time concept. Furthermore, the shock rate depends upon the state of the system and follows a non-homogeneous Poisson process. Thus, using a numerical approach, the objective of this study is to determine the number of inspections during the first phase, the interval between consecutive inspections, and the age for scheduled preventive replacement. We compared this policy to two other classical policies via a case study and found that it has significant cost-saving potential. Generally, our results indicate when inspections should be prioritized over age-preventive replacement, and vice versa. Multi-phase policies are particularly effective for components with different characteristic lives, with inspections being crucial for maintaining readiness, often requiring investment in quality assurance to prevent misclassification. Conversely, age-based policies are more advantageous for strong (long-lived) components or when the probability of misclassification is high.

Novel condition-based opportunistic maintenance policy for series systems with dependent competing failure processes

This paper investigates a novel condition-based opportunistic maintenance model for a two-component series system. The system comprises two non-identical components, that is, Component 1 and Component 2, suffering from shocks with different intensities. The two components are subject to dependent competing failure processes, that is, soft and hard failures. The component fails whichever type of failure process occurs. At each inspection, the replacement decision for components is made according to the condition of both components. In addition, when Component 1 is preventively replaced and Component 2 reaches the opportunistic maintenance threshold, opportunistic maintenance (OM) is implemented on Component 2. The optimal inspection interval, preventive replacement (PR) threshold, and OM threshold are derived by minimizing the long-run expected maintenance cost rate. Finally, the superiority of the proposed maintenance model is verified by an illustrative example.

Optimal replacement policy based on number of failures for a system with multiple attempt minimal repairs

This study proposes a failure number-dependent replacement policy for a minimal repairable system with multi-attempt repairs. The motivation for this study is based on the novel notion of "multiple attempts minimal repair," first introduced by Cha and Finkelstein [1,2] recently. Their repair/replacement model, along with useful properties derived and discussed for optimal preventive maintenance (PM) problems, served as the motivation for this study. This paper analyzes the optimal replacement policy based on the number of failures for a multi-attempt minimal repairable system.Our policy's characteristic is that the corrective replacement (CR) of a system is only possible at a system failure instant, rather than implementing a scheduled preventive replacement (PR). Specifically, under our policy, the system is replaced not only after a fixed number of unsuccessful repair attempts (k), but also upon reaching a predetermined failure number (n). We show that the optimal policy (or optimal n*) that minimizes the average cost per unit of time in the long run can be determined by the unique solution of an equation under certain conditions. The numerical results demonstrate that our policy is more efficient in terms of expected cost rate than other policies with the scheduled PR.

Condition‐based maintenance management for two‐stage continuous deterioration with two‐dimensional inspection errors

AbstractInspections often perform imperfect outcomes during maintenance processes owing to human errors, management issues and other limitations. In particular, such imperfection affects the maintenance management of multistage deterioration significantly due to both false state identification and measurement errors, whose quantitative analysis, however, is seldom reported in the literature. To fill these gaps, this paper devises a condition‐based maintenance management strategy oriented to two‐stage continuous degradation under two‐dimensional inspection imperfection. Specifically, a threshold‐based replacement is executed under the normal‐working state if the detected degradation value exceeds the preset limit; additionally, preventive replacement is immediately performed once the defective state is identified. Notably, the detection outcome rather than the actual working condition decides how preventive maintenance operates. The long‐run cost rate is minimized via the optimization of the inspection cycle and replacement limit. Besides, numerical experiments conducted on train bogie bearing are provided, showing substantial superiorities over cost‐effectiveness promotion and performance improvement.

Analyzing the impact of opportunistic maintenance optimization on manufacturing industries in Bangladesh: An empirical study

The study investigates the impact of opportunistic maintenance (OM) optimization on manufacturing industries, especially in Bangladesh, to reduce maintenance costs. To that end, OM strategies have been proposed and optimized for multi-unit manufacturing systems, whereas most of the existing research is for single- or two-unit systems. OM strategies in this research cover one of the three policies: preventive replacement, preventive repair, and a two-level maintenance approach. The proposed two-level maintenance approach is a combination of lower-level maintenance, known as preventive repair, and higher-level maintenance, known as preventive replacement. Simulation optimization (SO) techniques using Python were utilized to evaluate the strategies. Historical data from two of Bangladesh's most promising and significant sectors, the footwear and railway industries, was used as the case study. Compared to the currently utilized corrective maintenance approach, the two-level maintenance approach is the most effective for both case studies, demonstrating cost savings of 16.9 % and 22.4 % for the footwear and railway industries, respectively. This study reveals that manufacturing industries can achieve significant cost savings by implementing the proposed OM strategies, a concept that has yet to be explored in developing countries like Bangladesh. However, the study considered the proposed approaches for major components of the system, and more significant benefits can be achieved if it is possible to apply them to all critical components of the system.

Penjadwalan Preventive Maintenance untuk Menurunkan Downtime Mesin Auto Front Wheel di Industri Otomotif

Permo (Motor Company) Industry is one of the companies that operates in the automotive industry, especially motorcycles. The production process in Permo Industry requires machines to support its production process. Among all production engines, the auto front wheel is the engine with the highest downtime rate. The high downtime of the auto front wheel engine is due to an engine failure of 12,000 seconds, or 200 minutes. To reduce downtime, the study included a timetable for checking and preventive replacement of components. The aim of the research is to reduce downtime due to damage to engine components so that production runs smoothly. This step of improvement uses Plan, Do, Check, Action. (PDCA). The results of this study can eliminate 100% of the downtime damage to spring pin press bearing components. The study resulted in an average replacement of spring pin press bearing components every 18 days, a standard using spring pin press bearing components (80.621 shoot), and monitoring of spring p pinpress b bearingcomponents 14 days after replacement.

Joint optimization of maintenance and spares inventory policy for a series-parallel system considering dependent failure processes

This paper investigates the joint optimization of condition-based opportunistic maintenance and spare inventory policies for a series-parallel system consisting of software and components. Software failure follows a homogeneous Poisson process. Components are subject to mutually dependent competing failure processes. When the software failure occurs, an opportunistic inspection will be implemented for the components. Besides the maintenance policy, the (s, S) inventory policy is applied for components. The maintenance and order decisions will be made according to the conditions of the components and inventory level at each inspection. The state transition probabilities and expected sojourn times can be obtained by the formulated semi-Markov decision process. The optimal inspection interval, the preventive replacement threshold of the component, and the order point can be derived by minimizing the expected average cost rate. Finally, the effectiveness and superiority of the proposed joint optimization model are verified by an illustrative example.

Intelligent optimal preventive replacement maintenance policy for non-repairable systems

This paper developed optimal maintenance policies for industrial machines to ensure uninterrupted production. Analytical and intelligent replacement models were constructed using real-time data from a commercial photocopier operator’s expenditure logbook between 2012 and 2023. A goodness-of-fit test affirmed a log-logistic failure distribution with shape and scale parameters of 1.7233 and 763.9220, respectively. Probability functions from this distribution established replacement models, determining optimal preventive maintenance conditions. Through feature engineering additional features crucial for operational and cost optimization were abstracted, with 4,881 unique data points simulated from boundary conditions defined by the original or raw data. Analytical results showed that the failure distribution-based replacement model outperformed the hazard function-based model in minimum replacement time and unit maintenance cost over time, while key parameters such as availability, reliability, and failure occurrence probability remained constant at 96 %, 94 %, and 0.07 %, respectively. Machine learning models (Extreme Gradient Boosting: XGBoost, Support Vector Machine: SVM, and Multilayer Perceptron: MLP) demonstrated very high accuracy in availability (XGBoost = 99.49 %, SVM = 99.18 %, RF = 100 %, MLP = 99.80 %), reliability (XGBoost = 99.49 %, SVM = 99.18 %, RF = 100 %, MLP = 99.80 %), and maintenance cost (XGBoost = 100 %, SVM = 100 %, RF = 100 %, MLP = 100 %), exceeding results of the analytical models.

Condition-based maintenance policy for systems under dynamic environment

This paper addresses the challenge of optimizing maintenance for systems with varying degradation characteristics in dynamic environment. The focus is on repairable systems, where degradation is characterized by a Wiener process that incorporates the dynamic influence of the environment. We investigate a condition-based maintenance model aiming at minimizing the expected cost over a finite and infinite horizon. Firstly, the model considers the evolution of environment as a Markov process, which influences the drift parameter in the Wiener process. The choice between a corrective and preventive replacement rests on the periodic inspections on the system and environment. Subsequently, the maintenance model is formulated in the framework of Markov decision process. Meanwhile, the backward dynamic programming algorithm and value iteration algorithm are employed to gain the optimal maintenance policies over the finite and the infinite planning horizons, respectively. Finally, the proposed model is further explained with concrete examples and a comprehensive sensitivity analysis. Numerical results clearly demonstrate notable variations in the optimal maintenance strategies in different periods and underscore the importance of taking proactive maintenance measures in harsh environmental conditions, even if the degradation level of the system is relatively low.

Modified age-based replacement

The maintenance policy age-based replacement (ABR) is widely specified in OEM instructions. The practical application of ABR raises concerns about ensuring consistent adherence to prescribed replacement schedules for extended periods. ABR lacks periodicity, resulting in scheduling asynchrony with designated time slots, while alternative policies such as block replacement (BR) provide periodicity at the expense of efficiency. Additionally, scepticism about ABR is based on its simplicity and restrictive assumptions, which include ideal replacements and the one-component system assumption. The task of estimating component lifetime distributions and defining critical parameters such as cost of failure presents significant challenges. We study “modified age-based replacement” (MABR) in response to the limitation of aperiodicity, so that preventive replacements exhibit quasi-periodic behavior. We quantify the cost-inefficiency of MABR compared to ABR, thus informing the practical implications of introducing periodicity into the ABR policy and highlighting the need to incorporate real-world constraints, such as time slots for maintenance actions. The findings indicate that MABR and a special case are reasonably efficient provided the slot-interval is not too large. This is a useful insight for practical application of ABR type policies for scheduling preventive maintenance.

Studies of tractor maintenance and replacement strategies of Wonji Shoa Factory, Ethiopia

This study mainly focuses on tractor maintenance and replacement strategies to assess the impact of various parameters on the economic life of tractors in order to improve the value of a profitable management choice on selected tractor samples. Considering the preventive replacement policy, the total annual costs were estimated taking into account the repair and depreciation costs. At a 95% level of confidence for each approach, the statistical analysis program “IBM SPSS Statistics 26” was used. An empirical relation based on multiple regression analysis has been generated to predict the economic operational life of a tractor using per-unit repair cost and annual usage (hours). From the analysis, John Dear 333, SAME 130, New Holland 80, and Massy Ferguson 150 are not supposed to be used economically in the field after the fifth, seventh, sixth, and eighth years respectively at Wonji Shoa Sugar Factory due to increasing maintenance cost in present condition.

Optimizing multilevel maintenance in multi-component systems under s-dependent competing risks

This paper presents a novel reliability-centered multilevel maintenance optimization model tailored for multicomponent systems subjected to stochastic dependent (s-dependent) competing risks (CRs). Four types of maintenance actions, including minimal repair (MR), medium preventive maintenance (MPM), overhaul preventive maintenance (OPM), and preventive replacement (PR), are incorporated into the development of the multilevel maintenance model. The Copula function is utilized to quantify the s-dependence among failure risks, and subsequently, all unknown parameters are estimated via the inference function for margins (IFM) method. Three types of decision variables are designed to minimize the long-run expected cost rate of the system. Finally, sequential decision process method is utilized to solve the model, and a numerical example of a metro door system illustrates the effectiveness of our proposed model.

FAILURE MODEL OF NON-RESTORABLE COMPLEX TECHNICAL OBJECT OF MILITARY EQUIPMENT

Maintenance (MT) is a necessary component the process of operating a complex technical object of military equipment, intended for long-term operation in wartime and harsh operating conditions. The volume, content and timing of maintenance must be completely determined by the reliability properties of the object, conditions and modes of its use. Effective implementation of any maintenance operation is possible only if the design of the object provides special means for this purpose (for measuring the defining parameters) and ensures the accessibility and convenience of performing the operation. Complex technical objects in modern society are extremely important. Such objects belong to the class of restored objects long-term repeated use. They are usually expensive and require significant operating costs. To ensure the required level of failure-free operation during their operation, maintenance is usually carried out, the essence of which is the timely preventive replacement of elements that are in a pre-failure state, which is very important for military equipment. A characteristic feature of complex technical objects for special purposes (military equipment) is the presence in their composition of a large number (tens, hundreds of thousands) different types of components that have different levels of reliability, different patterns of their wear and aging processes. This feature requires a more subtle approach to organizing and planning maintenance during operation (of military equipment). The above statements fully justify the conclusion about need to determine the main characteristics of the maintenance system in early stages of its design, when it is still possible to make changes to design of the object. In this work, a model of failure-free operation non-repairable complex technical object of military equipment is developed. The work also confirms the general idea that data obtained fully confirm the assumption that a maintenance strategy that is not restored is more preferable in the case of unreliable (inaccurate) information about the reliability indicators of elements of a military equipment object.

Reliability-Based Model for Incomplete Preventive Replacement Maintenance of Photovoltaic Power Systems

Reliability-Based Model for Incomplete Preventive Replacement Maintenance of Photovoltaic Power Systems

Condition-based maintenance considering imperfect inspection for a multi-state system subject to competing and hidden failures

Unexpected failures may cause huge economic losses and bring about uncontrolled risks and damage to humans and the environment. Condition-based maintenance is considered one of the most effective maintenance methods, which can reduce and eliminate possible failures and consequently ensure the safety and operating process effectiveness of systems. Inspection quality is an important factor affecting the effect of condition-based maintenance since imperfect inspection may result in inappropriate maintenance decisions. However, the factor of imperfect inspection is rarely considered when maintaining multi-state systems subjected to competing and hidden failures. In this paper, a periodical inspection policy is utilized to identify all states of a system subject to a three-state degradation process and random shocks. Inspections for all system states are considered imperfect. Upon the inspection outcome, the system may be replaced by a failure-based corrective replacement once it is detected as failed, or is renewed by a defect-based preventive replacement if a defect is identified. Moreover, an age-based preventive replacement is also required if the system is still normal up to the preset number of inspections. Subsequently, a condition-based maintenance model is constructed based on the probabilities of different renewal scenarios. Eventually, the applicability and effectiveness of the proposed model are verified by a numerical example. The results indicate that minimizing the constructed maintenance model can achieve the optimal inspection interval, which is greatly influenced by the factor of imperfect inspection. This research can provide references for decision-makers to optimize inspection and maintenance strategies.

Reliability Analysis and Preventive Maintenance Strategy for Competitive Failure Process of Wind Turbine Blade with Hard Failure-Caused Threshold Variation

During the operation of wind turbines, various failure forms are often caused by the interaction of their own performance degradation and external shocks due to the complexity of the operating environment and the form of the load. Whereas these failures are not obvious, improvement of life cycle and reduction of maintenance cost can be realized by identifying potential failures using a sound maintenance strategy and inspecting each system. In this study, we further extend the applicability of the competitive failure model by considering the existence of multiple shock types in the degradation process based on the competitive failure model of threshold variation caused by hard failure. We discuss two different cases of varying failure thresholds due to the multiple shock types. In case 1, cumulative degradation causes the system to fail at less than a predetermined failure threshold and with the presence of multiple shock types. In case 2, the cumulative degradation of the system reaches a predetermined failure threshold but is less than or equal to the set maximum failure threshold under multiple shock types. Finally, the validation is performed by two special cases. A status maintenance model based on preventive maintenance state thresholds is investigated to ensure that the system works properly under different degradation stages and to obtain the optimal preventive replacement threshold based on minimizing the desired cost rate. At last, the feasibility of the method was verified by reliability assessment and sensitivity analysis of wind turbine blade.

Age based preventive replacement policy for discrete time coherent systems with independent and identical components

Age based preventive replacement policy for discrete time coherent systems with independent and identical components