Condition-based Maintenance Optimization Research Articles

Joint Decisions of Components Replacement and Spare Parts Ordering Considering Different Supplied Product Quality

In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3).

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.

Optimization of Condition-Based Maintenance With Multiple Times of Component Reallocation Using Markov Decision Process

Optimization of Condition-Based Maintenance With Multiple Times of Component Reallocation Using Markov Decision Process

Joint optimization of inspection and condition-based maintenance for a deteriorating product under extended warranty

Condition-based maintenance (CBM) has been widely applied in engineering practice, but it has been rarely integrated into extended warranty policies. When the inspection cost is high, it becomes important to schedule the inspection plan when making CBM decisions. This paper jointly optimizes inspection and CBM for a deteriorating product under extended warranty. The deterioration of the product is characterized as a non-stationary gamma process. At each inspection epoch, the decision maker needs to determine whether to repair the product and when to perform the next inspection. The objective is to simultaneously optimize the inspection policy and the maintenance policy that minimize the warranty servicing cost. The joint optimization problem is formulated by stochastic dynamic programming. The optimal policy is proven to be of a control-limit form. A backward induction algorithm integrating the obtained structural properties is proposed to find the optimization results. A numerical example is conducted to illustrate the effectiveness of the proposed approach. A graphical representation of the obtained policy is provided to support practical maintenance implementation. The comparison with a popular CBM policy confirms the superiority of the proposed policy.

Improving the reliability of power distribution substations equipment for life extension by considering Condition Based Maintenance

Maintenance of aged power equipment is a proven technique for establishing systems reliability and longevity. It is extensively relevant when combined with Condition Based Maintenance (CBM) approaches for predicting power system infrastructure life, especially with imminent obsolescence. The research focuses on distribution companies with mixed aged power infrastructure, which continuously degradate. A significant setback in older systems is the scarcity of replacement parts leading to unplanned investment in substantial infrastructure due to unexpected failed components that are no longer in production. To guarantee excellent infrastructure health in older equipment, utility companies should consider applying Condition Based Maintenance (CBM) using Artificial Neural Network (ANN) to stabilize older equipment and restore malfunctioning assets. Studies have shown that the life span of older equipment can be extended when adequate maintenance strategies are put in place through the prediction of possible failures using equipment inspections and risk matrix. CBM's strategy improved the Key Performance Indicator (KPIs) System Average Interruption Duration Index (SAIDI) has improved by lowering the average monthly customers power interruptions by 19,15 % lesser time spent without electricity. The System Average Interruption Frequency Index (SAIFI) was improved by lowering the average monthly power interruptions frequency by 20,63% lesser outage frequency. The Distribution Supply Loss Index (DSLI) was improved by dropping the average monthly power losses by 7,5% power losses saved. The Customers Average Interruption Duration Index (CAIDI) was improved by lowering the average customer interruption by 36.1% improvement from the average period before the CBM optimization. The Average System Availability Index (ASAI) was improved by shortening the average annual interruption durations by 1,36%. The ASUI was improved by increasing the average duration of electrical power availability by 1.28%. The optimization of CBM was measured over 24 moths period.

Joint Optimization of Spare Part Supply and Opportunistic Condition-Based Maintenance for Onshore Wind Farms Considering Maintenance Route

Maintenance and inventory control of spare parts are of great importance for efficiently managing onshore wind farms. Considering the economic and graphical dependencies among wind turbines and the stochastic degradations of the components in the turbines, this article studies the joint optimization of opportunistic condition-based maintenance and spare parts supply for onshore wind farms to minimize the operation and maintenance cost rate. For each field maintenance, the specific turbines, with failed components, are selected for replacement using a clustering method, and the shortest route for visiting the turbines with failed components is scheduled. The maintenance team departs from a maintenance center with spare parts, maintains the selected turbines with failed or degraded components, and then returns to the center. This complicated and practical problem has never been considered as a whole in the literature. An optimization model is established, and a simulation-based approach is developed to solve it. A special case, for which a solution can be obtained by directly solving a nonlinear programming model, is provided to verify the results obtained by the simulation-based approach. Examples for various scales of wind farms are provided, and an analysis is performed for determining the length of a recommended maintenance interval.

Condition-Based Selective Maintenance Optimization for Large-Scale Systems Consisting of Many Homogeneous Units

Condition-Based Selective Maintenance Optimization for Large-Scale Systems Consisting of Many Homogeneous Units

Joint Optimization of Condition-Based Maintenance and Spare Parts Ordering for a Hidden Multi-State Deteriorating System

Joint Optimization of Condition-Based Maintenance and Spare Parts Ordering for a Hidden Multi-State Deteriorating System

Joint optimization of condition-based maintenance and condition-based production of a single equipment considering random yield and maintenance delay

Joint optimization of condition-based maintenance and condition-based production of a single equipment considering random yield and maintenance delay

Joint optimization of performance-based contracting, condition-based maintenance and spare parts inventory for degrading production systems

As a novel supporting approach, performance-based contracting (PBC) ties the service supplier’s compensation to the performance outcome of the system owned by the customer. It enables the service supplier to implement an effective maintenance and spare parts inventory policy so as to improve system performance. In this paper, we propose a principal–agent modeling framework for deteriorating production systems operating under PBC, with the objective of maximizing the expected utilities of both stakeholders. The customer (principal) offers PBC with a fixed payment plus performance incentive form to the service supplier (agent), who in turn implements a condition-based maintenance (CBM) policy based on the system prognostic condition at periodic inspections, and sets an investment level of spare parts inventory. Considering the partial observability of the service supplier’s actions, the optimal combination of performance incentive in PBC provided by the customer, and the joint CBM and spare parts inventory policy implemented by the service supplier, are determined through a two-stage and heuristic approach. Finally, a numerical example and sensitivity analysis based on a real-world scenario are presented to illustrate and verify the applicability of the proposed model.

Opportunistic condition-based maintenance optimization for electrical distribution systems

Opportunistic condition-based maintenance optimization for electrical distribution systems

Enhancing Rotary Machine Reliability Through Condition-Based Maintenance Optimization

Rotary machines possess an inherent characteristic of generating vibrations, leading to the deterioration of critical components, particularly bearings and gears, ultimately failing the system. Vibration analysis is widely acknowledged as the predominant diagnostic technique employed for assessing the state of machinery and informing maintenance strategies. Condition-based maintenance (CBM) is a crucial component of proactive maintenance strategies that optimize machine availability by implementing timely interventions and minimizing costly breakdowns. This research endeavour aims to establish a comprehensive framework that enables evaluating the operational state of rotary machinery and its critical components, specifically emphasizing bearings. The interdependence between maintenance and machine health is evident, as a well-maintained machine requires minimal maintenance, while a machine undergoing deterioration requires immediate intervention. Rotary machines, which hold significant importance in various industrial processes, often face significant obstacles to bearing issues. These challenges lead to considerable disruptions in output and escalate maintenance costs. The current study examines the efficacy of CBM as a potential solution to the aforementioned issues. CBM is a maintenance strategy that utilizes real-time data on machine conditions to make informed decisions regarding maintenance interventions. By leveraging this approach, maintenance activities can be executed at the most opportune time, maximizing efficiency and effectiveness. CBM is a strategic approach that empowers enterprises to proactively mitigate failures, optimize maintenance schedules, and improve overall operational efficiency by accurately estimating the remaining usable life of machine components. This research study contributes to the growing body of knowledge on CBM, offering valuable insights into predictive maintenance and its potential to enhance the reliability and efficiency of rotary machinery.

A comprehensive methodology for the optimization of condition-based maintenance in power transformer fleets

A comprehensive methodology for the optimization of condition-based maintenance in power transformer fleets

Joint Optimization of Condition-Based Maintenance and Performance Control for Linear Multi-State Consecutively Connected Systems

Industrial systems such as signal relay stations and oil pipeline systems can be modeled as linear multi-state consecutively connected systems, which comprise sequentially ordered elements and fail if the first and the final elements are not connected. The performance level of each element is controllable, which determines how many elements an element can connect and affects its degradation rate. Accumulated degradation can cause element failure, which may lead to costly system failure. This paper aims to minimize long-term maintenance-related costs, including system failure costs. We provide optimal maintenance planning and performance control for every system degradation state through Markov decision process modeling and a dynamic programming algorithm. Load-sharing, restricted maintenance capacity, maintenance setup costs, and the structural characteristics of the system are considered in the model, all of which influence the optimal maintenance and performance control policy. Regarding degradation management, reducing the difference in degradation levels between elements, e.g., replacing more-degraded elements first, can be cost-effective. However, increasing the difference in degradation by maintenance or performance control can also lower maintenance-related costs in specific situations, which is discussed in numerical experiments. We also illustrate structural insights regarding the proposed model, including sensitivity analyses of maintenance capacity, setup costs, and the difference between preventive and corrective replacement costs.

Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency

Recent developments in prognostic models have led to increased use of prognostic information to improve the benefits of condition-based maintenance (CBM). Existing CBM decision-making methods are often limited by the assumption that prognostics information is available for all components and by the neglect of economic loss due to system degradation in assessing benefits. This paper aims to find optimal maintenance decisions with maximum net revenue for multi-component systems using prognostics information. In contrast to the reported works, three main contributions are made. First, considering that the remaining useful life (RUL) of only some components in the system can be predicted continuously, maintenance decisions, including planned inspection intervals and prognostic thresholds, are optimized jointly at the system-level. Second, the loss of efficiency due to system degradation is investigated when assessing the benefits of maintenance decisions. A degradation-related system efficiency model is proposed, and its parameters can be determined from historical data. Third, we extend our model with prognostic error modeling to derive insights on the effects of the prognostic model on the relative benefit of CBM. A case of maintenance optimization of wind turbine farms is provided to demonstrate and validate the proposed method.

Integrated optimization of condition-based preventive maintenance and production rescheduling with multi-phase processing speed selection and old machine scrap

With the usage and aging of machine, condition-based preventive maintenance (CBPM) and old machine scrap are two common phenomena in the actual production, and the latter may lead to the original production-maintenance planning no longer available. Under this context, this paper addresses an integrated optimization problem of CBPM and production rescheduling with multi-phase processing speed selection and old machine scrap. More precisely, (1) a CBPM policy with sixteen inspection strategies and multi-phase processing speed selection is proposed to find some selectable maintenance plans for each machine; (2) a hybrid rescheduling strategy (HRS) is designed for responding to the dynamic event, and a rescheduling strategy is adaptively selected according to the average utilization rate (A1) of idle time of existing machines; and (3) an adaptive clustering-based bi-population co-evolutionary algorithm (ACBCA) is developed to solve the studied problem. In the numerical simulation, Taguchi method is first employed to find the optimal parameter setting for the proposed ACBCA. Second, the effect of predefined threshold of A1 is analyzed, and the optimal value is 0.3. Next, the superiority and competitiveness of the proposed ACBCA, CBPM policy and HRS are all demonstrated by comparing with other algorithms, CBPM policies and rescheduling strategies, respectively.

A data-driven approach for condition-based maintenance optimization

Developments in sensor techniques enable the continuous monitoring of the health of an operating system. The resulting condition data provides an opportunity for better prediction of failures and thereby for improving maintenance decisions. In this study, we consider condition-based maintenance for a single unit with an unknown, non-decreasing deterioration process and unknown failure behavior. Building on, but different from the existing maintenance optimization literature, we present the first fully data-driven approach, where the condition threshold triggering maintenance is based purely on past condition data and failures. Numerical results for a gamma deterioration process show that the maintenance threshold resulting from our data-driven approach converges to the optimal threshold. The threshold is set higher during the initial runs-to-failure, and this helps to explore the deterioration process. An encouraging result is that the convergence is especially fast during the first few runs-to-failure so that the expected cost rate quickly converges to the minimum cost rate.

Handling Type-I and Type-II endogenous uncertainties in simultaneous production planning and condition-based maintenance optimization in continuous production

This work proposes a novel stochastic programming formulation that can handle Type-I and Type-II endogenous uncertainties with the goal of simultaneously optimize production planning and condition-based maintenance of continuous production plants. A degradation model that is able to predict the Remaining Useful Life of the equipment is embedded into the optimization problem and we consider this prediction to be uncertain. Each scenario of the stochastic program represents an uncertain realization of the predicted Remaining Useful Life and has a certain probability of realization. These probabilities can be altered by the decision maker, thus rendering some predictions more likely than the others by acting on the utilization of the plant (Type-I endogenous uncertainty). The maintenance activities recover the health of the equipment and the decisions on maintenance make some scenarios not realize any more and, therefore, modify the structure of the scenario-tree (Type-II endogenous uncertainty). We present two different modelling approaches for this novel class of stochastic programs based on superstructure scenario trees and on conditional non-anticipativity constraints. For a simple generic example, we analyse the influence of the modelling approach on the computational time that is needed to solve the resulting MINLP problem with the global solver BARON. We also investigate several branching priority strategies to reduce the computational effort for this class of problems. We demonstrate the advantages that can be achieved using the proposed formulation compared to the deterministic counterpart.This work sets the ground for modelling and solving simultaneous production planning and condition-based maintenance problems. To deal with larger-scale industrial problems, additional work is required to develop custom solution techniques to reduce the computational burden.

Condition-based maintenance and production speed optimization under limited maintenance capacity

Many industrial facilities consist of multiple units working in parallel to reach a production target or to maximize production revenues. These units deteriorate due to stress that is often dependent on the production speed, and thus maintenance needs to be performed to ensure that the system remains functional. Maintenance generally requires a considerable amount of time and maintenance capacity is often limited. Therefore, during maintenance of a specific unit, a failure of another unit may lead to additional downtime until maintenance capacity becomes available again. In this study we examine the value of condition-based production in such a system, and jointly optimize production and maintenance decisions. The problem is formulated as a Markov decision process, and the results are compared with a benchmark policy without an adjustable production speed. A numerical analysis shows that adjustable production speeds are used to actively desynchronize the deterioration levels of units for systems with a production target. For systems that aim to maximize total production output, considerable profit increases are obtained by lowering the production speed (i) at high deterioration levels to reduce the risk of failure, and (ii) when the maintenance station is busy to reduce the risk that multiple units require maintenance simultaneously. A sensitivity analysis is used to illustrate under what circumstances joint condition-based production and maintenance decisions are most beneficial.

Deep reinforce learning for joint optimization of condition-based maintenance and spare ordering

Condition-based maintenance (CBM) policy can avoid premature or late maintenance and reduce system failures and maintenance costs. Most existing CBM studies cannot solve the dimensional disaster problem in multi-component complex systems. Only some studies consider the constraint of maintenance resources when searching for the optimal maintenance policy, which is hard to apply to practical maintenance. This paper studies the joint optimization of the CBM policy and spare components inventory for the multi-component system in large state and action spaces. We use Markov Decision Process to model it and propose an improved deep reinforcement learning algorithm based on the stochastic policy and actor-critic framework. In this algorithm, factorization decomposes the system action into the linear combination of each component's action. The experimental results show that the algorithm proposed in this paper has better time performance and lower system cost compared with other benchmark algorithms. The training time of the former is only 28.5% and 9.12% of that of PPO and DQN algorithms, and the corresponding system cost is decreased by 17.39% and 27.95%, respectively. At the same time, our algorithm has good scalability and is suitable for solving Markov decision-making problems in large-scale state and action space.