Condition-based Maintenance Strategies Research Articles

AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

As the oil and gas industry increasingly explores deeper and more remote offshore sites, the maintenance of subsea infrastructure becomes paramount. The use of Artificial Intelligence (AI) in subsea maintenance offers promising solutions to enhance safety and efficiency in these challenging environments. This review explores strategic approaches to integrating AI into subsea maintenance operations. AI facilitates predictive maintenance by analyzing vast amounts of data collected from sensors and historical maintenance records. Machine learning algorithms can detect patterns and predict equipment failures before they occur, enabling proactive maintenance scheduling. This predictive capability reduces downtime and minimizes the risk of accidents by addressing potential issues before they escalate. AI-enabled autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) play a crucial role in subsea inspections and repairs. These AI-enhanced robots can navigate complex subsea environments, perform inspections, and execute maintenance tasks with greater precision and efficiency than human divers. By reducing the need for human intervention in hazardous environments, AI-driven AUVs and ROVs significantly improve safety. Furthermore, AI algorithms can optimize maintenance schedules based on factors such as equipment condition, environmental conditions, and operational requirements. By dynamically adjusting maintenance plans, operators can maximize equipment uptime while minimizing costs and risks. This proactive approach ensures that maintenance activities are conducted at the most opportune times, reducing the likelihood of unplanned downtime and improving overall efficiency. Moreover, AI facilitates condition-based maintenance strategies, where equipment health is continuously monitored in real-time. Sensors installed on subsea infrastructure collect data on factors such as temperature, pressure, and vibration, which is then analyzed by AI algorithms to assess equipment condition. By detecting early signs of degradation or malfunction, AI enables timely interventions, preventing costly breakdowns and ensuring optimal performance. In addition to predictive and condition-based maintenance, AI-driven analytics offer insights into operational performance and asset integrity. By analyzing data from various sources, including sensors, historical records, and operational logs, AI can identify trends, anomalies, and optimization opportunities. These insights enable operators to make data-driven decisions that enhance overall system reliability and efficiency. Strategic approaches to implementing AI in subsea maintenance require collaboration between technology providers, operators, and regulatory bodies. Establishing industry standards and guidelines for AI applications in subsea operations is crucial to ensure safety, reliability, and interoperability. Furthermore, investing in research and development to enhance AI algorithms and robotics technology is essential to unlock the full potential of AI in subsea maintenance. AI-enhanced subsea maintenance offers significant benefits in terms of safety and efficiency. By leveraging predictive analytics, autonomous robotics, and real-time monitoring, operators can optimize maintenance activities, reduce downtime, and minimize risks. Strategic approaches to integrating AI into subsea operations require collaboration, investment, and a commitment to advancing technology to meet the challenges of offshore environments.

Advancing maintenance, monitoring, and control techniques for the optimization of power electronic systems

This paper explores the innovative methodologies and technologies in predictive maintenance, real-time monitoring, fault detection, and advanced control strategies for power electronic devices. Initially, we delve into data acquisition and preprocessing techniques crucial for ensuring the quality and reliability of data used in predictive models. These models, leveraging machine learning and time-series analysis, predict the remaining useful life of devices, guiding proactive maintenance strategies. Furthermore, we discuss the significance of risk assessment and decision support systems in prioritizing maintenance tasks and allocating resources efficiently. The narrative then shifts to real-time monitoring and fault detection, emphasizing the role of sensor integration, data fusion, anomaly detection, and diagnostics in maintaining system integrity. Condition-based maintenance strategies, underscored by real-time data analytics, are presented as a means to optimize maintenance activities and enhance operational performance. The paper concludes with a detailed examination of advanced control strategies, including model predictive control, reinforcement learning, and distributed control and optimization techniques, highlighting their potential to improve system efficiency, reliability, and adaptability. Through comprehensive research and analysis, this work aims to provide valuable insights into the development of sophisticated maintenance and control mechanisms for power electronic systems, ultimately contributing to their longevity and operational efficacy.

Vibration analysis for early pitting detection during operation

The economic efficiency of machinery operation is significantly impacted by maintenance strategies. In the realm of condition-based or predictive maintenance strategies, the early detection of fatigue-induced damages is crucial. Therefore, this study focuses on the early detection of pitting damages during operation. Experimental investigations are conducted on a test gearbox acquiring acceleration data for different sizes of pitting damages under diverse operating conditions. A successful detection of the pitting damages during operation is achieved at a very early stage of progression, with a minimum size of 0.41% of an active tooth flank area. The utilization of design of experiments techniques facilitates the identification of factors influencing the detectability of pitting damages. The obtained results are analysed to elucidate the physical basis for the reliable detection of pitting damages across diverse operating conditions.

A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies

A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies

A novel maintenance strategy for manufacturing system considering working schedule and imperfect maintenance

Condition-based maintenance (CBM) strategies that consider product quality lack a thorough analysis of the impact of manufacturing enterprise’s working schedule, which does not align with real-life production scenarios. To address this gap, this study proposes a novel CBM strategy model for manufacturing systems that takes into account working schedule to determine the optimal balance between maintenance and production while considering product quality. The maintenance set includes imperfect maintenance and perfect maintenance to capture real-life manufacturing scenarios. The model establishes an optimal maintenance strategy using a Markov decision process (MDP) framework. A numerical study is conducted based on internal information and data collected by sensors from a commercial vehicle manufacturing enterprise to demonstrate the effectiveness of the proposed strategy. We proposed a dual-value iteration algorithm to solve the MDP model. Finally, a comprehensive sensitivity analysis is performed to investigate the impact of cost-related parameters on the strategy.

An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction.

The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies. An effective health indicator (HI) plays a key role in the prediction of system degradation as it enables vital information for critical tasks ranging from fault diagnosis to remaining useful life prediction. To address this issue, a method for monitoring data fusion and health indicator construction based on an autoencoder (AE) and a long short-term memory (LSTM) network is proposed in this study to improve the predictability and effectiveness of health indicators. Firstly, an unsupervised method and overall framework for HI construction is built based on a deep autoencoder and an LSTM neural network. The neural network is trained fully based on the normal operating monitoring data and then the construction error of the AE model is adopted as the health indicator of the system. Secondly, we propose related machine learning techniques for monitoring data processing to overcome the issue of data fusion, such as mutual information for sensor selection and t-distributed stochastic neighbor embedding (T-SNE) for operating condition identification. Thirdly, in order to verify the performance of the proposed method, experiments are conducted based on the CMAPSS dataset and results are compared with algorithms of principal component analysis (PCA) and a vanilla autoencoder model. Result shows that the LSTM-AE model outperforms the PCA and Vanilla-AE model in the metrics of monotonicity, trendability, prognosability, and fitness. Fourthly, in order to analyze the impact of the time step of the LSMT-AE model on HI construction, we construct and analyze the system HI curve under different time steps of 5, 10, 15, 20, and 25 cycles. Finally, the results demonstrate that the proposed method for HI construction can effectively characterize the health state of a system, which is helpful for the development of further failure prognostics and converting the scheduled maintenance into condition-based maintenance.

Dynamic State Evaluation Method of Power Transformer Based on Mahalanobis–Taguchi System and Health Index

Health status assessment is the key link of transformer-condition-based maintenance. The health status assessment method of power transformers mostly adopts the method based on the health index, which has the problems of multiple parameters of each component and strong subjectivity in the selection of weight value, which is easily causes misjudgment. However, the existing online monitoring system for dissolved gas in transformer oil (DGA) can judge the normal or abnormal state of the transformer according to the gas concentration in a monitoring cycle. Still, there are problems, such as fuzzy evaluation results and inaccurate judgment. This paper proposes a dynamic state evaluation method for power transformers based on the Mahalanobis–Taguchi system. First, the oil chromatography online monitoring time series is used to screen key features using the Mahalanobis–Taguchi system to reduce the problem of excessive parameters of each component. Then, a Mahalanobis distance (MD) calculation is introduced to avoid subjectivity in weight selection. The health index (HI) model of a single transformer is built using the MD calculated from all DGA data of a single transformer. Box–Cox transformation and 3 σ criteria determine the alert value and threshold value of all transformer His. Finally, taking two transformers as examples, we verify that the proposed method can reflect the dynamic changes of transformer operation status and give early warning on time, avoiding the subjectivity of parameter and weight selection in the health index, which easily causes misjudgment and other problems and can provide a decision-making basis for transformer condition-based maintenance strategies.

An optical flow-based method for condition-based maintenance and operational safety in autonomous cleaning robots

Autonomous professional cleaning robots are ubiquitous today, coexist with humans, and the demand continues to improve hygiene and productivity, especially in large indoor workplaces. Hence, a proper maintenance strategy is essential to monitor the robot’s health, assuring flawless operation and safety. Manual supervision and periodic maintenance are adopted in general, which is challenging to detect failures in advance, resulting in higher maintenance costs and operational hazards. Anomalous vibration due to system degradation or environmental factors is an early symptom of failure or potential threats. Hence, predicting the sources of such abnormal vibration will help prompt maintenance action or set a hazard-free environment. However, such condition-monitoring research studies are not common for indoor cleaning robots. To fill this gap, we proposed an automated Condition Monitoring (CM) system, predicting the abnormal vibration sources that will enhance Condition-based Maintenance (CbM) and Operational Safety. A novel vibration-based CM method is presented for mobile robots, suitable for indoor environments under typical illumination states, using a monocular camera and optical flow technique instead of their usual perception-related applications. The sources of abnormal vibration were classified as terrain, collision, loose assembly, and structural imbalance. We modelled vibration as sparse optical flow, and the change in optical flow vector displacement due to the robot’s vibration is derived from consecutive camera frames used as vibration data. A practical framework is developed adopting the One Dimensional Convolutional Neural Network (1D CNN) model to fit this vibration data and tested predicting the vibration source class with an average accuracy of 93.8%. In addition, a vibration source map (CbM map) is proposed by fusing the predicted class in the workplace environment map for real-time monitoring. The case studies conducted using our in-house-developed cleaning robot show that the proposed CM framework will help the maintenance team for CbM, operational safety, and select proper maintenance strategies.

AI-Based Exhaust Gas Temperature Prediction for Trustworthy Safety-Critical Applications

Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.

RUL Prediction for Lithium Batteries Using a Novel Ensemble Learning Method

The remaining useful life (RUL) is the key element of fault diagnosis, prediction and health management (PHM) during the equipment operation service period. The prediction result of RUL is the premise for equipment to adopt preventive maintenance, condition-based maintenance, fault maintenance and other maintenance strategies. Lithium battery is an important energy component of new energy vehicles, mobile phones, etc. Its RUL is related to the state of its equipment system. Many model-based methods have been used to predict the lithium batteries’ RUL, and some studies have begun to use lithium battery monitoring data to predict its remaining service life. With the continuous detection and monitoring capability of equipment throughout its life cycle gradually improved, a large number of monitoring and detection data promote the wide application of data-driven residual life prediction in the field of equipment. At present, the data-driven prediction method of the lithium batteries’ RUL mostly adopts a single time-series forecasting model. The robustness and generalization of the prediction method are insufficient. It needs to be further improved to improve the prediction accuracy and robustness. Preventive maintenance measures shall be taken immediately according to the prediction results to ensure the effective supply of energy at any time. In this paper, an integrated learning algorithm based on monitoring data is proposed to fit the degradation model of lithium batteries and predict their RUL. The ensemble learning method consists of 5 basic learners to achieve better prediction performance, including relevance vector machine (RVM), random forest (RF), elastic net (EN), autoregressive model (AR), and long short-term memory (LSTM) Network. The genetic algorithm (GA) is used in the ensemble learning method to find and determine the optimal weights of the basic learners, and obtain the final prediction result of lithium batteries. Then, the simulation is carried out on the CS2_35 lithium battery data set. The simulation results show that the method proposed in this paper has a smaller Root Mean Square Error (RMSE) than another 5 single methods. The RMSE is respectively 0.00744 for RVM, 0.01097 for RF, 0.01507 for EN, 0.03223 for AR, 0.01541 for LSTM, and 0.00483 for ensemble learning, and the RMSE of ensemble learning is reduced by 0.0274 at the highest and 0.00261 at the lowest, so the ensemble learning algorithm has better robustness and generalization effect.

In-Service Power Transformer Life Time Prospects: Review and Prospects

Power transformers are essential assets in power system networks that must be meticulously monitored throughout their operational life cycle. Given that a substantial percentage of in-service power transformers around the world have reached the end of their projected technical lifespan, utilities are adopting various transformer condition-based maintenance strategies to minimize potentially devastating equipment failure. Thus, further usage of aging fleet of power system assets can be improved by gaining a better understanding of life threatening factors. In-service power transformers and their auxiliary components are susceptible to a variety of operational risks, which can compromise their performance and efficiency, thus resulting in devastating failures, financial losses, and power outages. Hence, it is necessary to investigate transformer life time issues so as to fully grasp the operational and performance status in order to reduce failures and operating costs. This paper presents a unified framework of attributes pertaining to life time issues for transformer operation evaluation together with a summary of recent findings in order to explore the current status and progress of this rapidly progressing field. The failure statistics analysis is presented first, followed by an examination of the transformer’s insulation degradation and aging mechanism. Furthermore, emphasis was on detailing the commonly adopted models and strategies in transformer condition evaluation, fault diagnostics, and remnant life estimation. Despite significant advancements in ascertaining transformer health diagnosis and prognosis technologies, including test accuracy, quick, precise fault localization, and fault type classification, there are still several shortcomings that require additional research, and thus future research perspectives are also discussed.

Reliability and availability assessment of railway locomotive bogies under correlated failures

Railways play a pivotal role in transportation systems worldwide. Effective maintenance policies are crucial to guarantee the reliability and availability of such systems inserted in a challenging and competitive environment. In this scenario, condition-based and predictive maintenance strategies emerge as key components of the European rail traffic system, where common advanced monitoring solutions of railway assets serve as performance metrics for the development of maintenance rules. However, the effectiveness of a good maintenance policy is intrinsically dependent on a clear assessment of the system on target, including not only aspects related to its complexity and financial importance, but fundamentally the physical characteristics and the environmental and operational conditions to which the system is subject to. In this sense, simulation models are useful as they can mimic the behaviour of systems, especially those too complex to solve analytically, incorporating the characteristics and the inherent stochastic behaviour associated with them, including their correlation structure. This study focuses on the locomotive bogie, which is a complex subsystem of the train onto which the wheels of the vehicle are fixed. The main components of the bogie are described as well as the stochastic behaviour associated with the occurrence of failure. A Discrete Event Simulation (DES) model is proposed to study the impact on the system, in terms of reliability and availability, under different scenarios with varying assumptions on the underlying failure modes, repairs, and on the failures’ correlation structure. The results point out that the model is suitable for representing the main characteristics of this train subsystem, and it identifies the parameters and components that are more critical in terms of reliability and availability fluctuations when contrasted with the variations imposed on the model by considering different scenarios. Overall, the DES model allows the robust assessment of where the focus should be put according to the uncertainty embedded in the correlations of failures and/or in the maintenance durations.

Reduced Scale Laboratory for Training and Research in Condition-Based Maintenance Strategies for Combustion Engine Power Plants and a Novel Method for Monitoring of Inlet and Exhaust Valves

This paper presents the practical aspects of development of a reduced scale laboratory and a set of monitoring tools for Internal Combustion Engines used in Thermal Power Plants. The reduced scale laboratory is based on the necessity of researchers to test new sensors and monitoring strategies that, otherwise, are seldom allowed to be installed in real plants without certification. In addition, the reduced scale laboratory allows the flexibility to insert failures on purpose, in order to evaluate the performance of new sensors/strategies in a safe and controlled environment. The paper also presents the development of a set of reduced cost sensors for monitoring in-cylinder pressure, crank angle, and the position of inlet and exhaust valves (without using ultrasound sensors, which may produce noisy readings on engines operating on gas-diesel fuel mode).

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

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

Cost Benefit of Implementing Advanced Monitoring and Predictive Maintenance Strategies for Offshore Wind Farms

Advancements in wind turbine condition monitoring systems over the last decade have made it possible to optimise operational performance and reduce costs associated with component failure and other unplanned maintenance activities. While much research focuses on providing more automated and accurate fault diagnostics and prognostics in relation to predictive maintenance, efforts to quantify the impact of such strategies have to date been comparatively limited. Through time-based simulation of wind farm operation, this paper quantifies the cost benefits associated with predictive and condition-based maintenance strategies, taking into consideration both direct O&M costs and lost production. Predictive and condition-based strategies have been modelled by adjusting known component failure and repair rates associated with a more reactive approach to maintenance. Results indicate that up to 8% of direct O&M costs can be saved through early intervention along with up to 11% reduction in lost production, assuming 25% of major failures of the generator and gearbox can be diagnosed through advanced monitoring and repaired before major replacement is required. Condition-based approaches can offer further savings compared to predictive strategies by utilising more component life before replacement. However, if weighing up the risk between component failure and replacing a component too early, results suggest that it is more cost effective to intervene earlier if heavy lift vessels can be avoided, even if that means additional major repairs are required over the lifetime of the site.

Optimal Service Points (OSP) for PHM Enabled Condition Based Maintenance for Oil and Gas Applications

In recent times, the oil and Gas industry has faced many challenges resulting from a tightening climate policy environment on oil and gas exploration, as well as the increasing risk of oversupply due to new discoveries globally. This has given stakeholders in the industry an incentive to integrate new technologies to optimize the operational efficiency of their assets, leading to the optimal recovery of hydrocarbons especially in marginal fields. Various Original Equipment Manufacturers (OEM) now provide different service offerings using data driven methods to provide condition monitoring of assets for oil and gas operators. However, a significant part of the value proposition by OEMs in their service delivery focuses on value generated at the component level with a reduction in asset downtime. This limits the broad economic benefits that a condition-based approach can provide, at the enterprise level. Therefore, the purpose of this paper is to develop a cost benefit analysis framework for assessing the implementation of condition and performance monitoring of oil and gas assets used in surface applications. The framework utilizes a combined technical-economic approach to determine a minimum predictive requirement for the implementation of condition-based principles to maintenance of assets in a hydrocarbon project from first oil to abandonment. This financial analysis framework uses a condition monitoring approach based on prognostics as well as a regression approach for fault detection and system performance. The paper will present a case study to evaluate the costs and benefits associated with implementing a condition-based maintenance approach for a set of valves in a Christmas tree subsystem, as part of a typical onshore production system. The framework illustrated using the case study compares a constant failure rate Time-Based approach to the PHM enabled condition-based maintenance. The results demonstrate that a prognostic enabled system can provide commercial benefits at the component level for a Condition Based Maintenance strategy but not necessarily at the enterprise level for an oil and gas project. The cumulative reduction in downtime at the component level over a project lifecycle offsetting the present value of the total cost of integrating a PHM enabled system into the overall maintenance strategy creates the ideal situation for commercial viability. However, the commercial viability of the PHM integration would depend on the accuracy of predicting failure events and monitoring asset degradation by the PHM enabled system which ultimately defines the performance of the condition-based maintenance approach. The accuracy level of asset failure therefore provides OEMs with a benchmark for executing their condition-based maintenance services with a minimum performance threshold. Secondly, an enterprise level financial viability, as well as OEM profitability in the implementation of a condition-based maintenance approach, requires an Optimal Service Point (OSP) which is a function of the minimum predictive requirement of the PHM system. The utility that the OSP provides is that, it gives the minimum value of the framework’s decision criteria that an operator can use a basis for incorporating a condition-based approach in its maintenance strategy. It also provides the maximum Annual Service Fee (ASF) derived from the cumulative OEM NPV needed for structuring and pricing servitization agreements with operators. This OSP cost benefit analysis approach ultimately provides OEMs and operators with a practical guide in the provision as well as the adoption of condition-based maintenance strategies respectively. It balances the risk of PHM integration by operators with a minimum PHM system performance threshold required for commercial viability for project lifecycle.

Condition-Based Maintenance Strategies to Enhance the Durability of ETICS

The increase of awareness with sustainability and the desire of reducing the energy consumption in the construction sector haved increased the application of External Thermal Insulation Composite Systems (ETICS) across Europe in the last decades. Nevertheless, the implementation of appropriate maintenance strategies is still neglected. The aim of this study is to analyse the impact of different maintenance strategies. For that purpose, a condition-based maintenance model, based on Petri nets, is used to evaluate three maintenance strategies: MS1—total replacement only; MS2—combination of minor intervention and total replacement; and MS3—combination of cleaning operations, minor intervention, and total replacement. In the end, a multi-criteria analysis is used to discuss the impact of the three maintenance strategies proposed, evaluating the remaining service life, the global costs over time, the ETICS’ degradation condition, and the number of replacements (end of service life) over the time horizon. For this purpose, a sample of 378 ETICS was analysed, based on in situ visual inspections, carried out in Portugal. The results from this study reveal that maintenance plays an important role to increase the durability of ETICS, and therefore their sustainability. Regular maintenance can promote the extension of the ETICS’s service life between 88% and 159% (between 15 to 27 years), improve the global degradation condition of the ETICS, and reduce the impact on users by reducing the number of deeper interventions. Further research is essential to optimise the maintenance strategies (time interval between inspections, stakeholders’ performance criteria, and environmental exposure).

Adding interpretability to predictive maintenance by machine learning on sensor data

Condition-based maintenance (CBM) is becoming more commonplace within the petrochemical industry. While we find that previous research leveraging machine learning has provided high accuracy in the predictive aspect of machine breakdowns, the diagnostic aspect of these approaches is often lacking. This paper implements a supervised machine learning approach, with the goal of both prediction and diagnosis of machinery breakdowns, emphasizing the latter. To achieve this, it uses an XGBoost model trained on a combination of sensor and report data, and enriches the model with Shapley values for diagnostic insights. We show that this combination of statistical methods, combined with a proper data treatment, can be used to great effect and can vastly improve the diagnostic value of machine learning approaches. The insights that follow from the analysis can subsequently be leveraged by plant operators in CBM strategies or root-cause analyses.

Reinforcement learning with Gaussian processes for condition-based maintenance

Condition-based maintenance strategies are effective in enhancing reliability and safety for complex engineering systems that exhibit degradation phenomena with uncertainty. Such sequential decision-making problems are often modeled as Markov decision processes (MDPs) when the underlying process has a Markov property. Recently, reinforcement learning (RL) becomes increasingly efficient to address MDP problems with large state spaces. In this paper, we model the condition-based maintenance problem as a discrete-time continuous-state MDP without discretizing the deterioration condition of the system. The Gaussian process regression is used as function approximation to model the state transition and the value functions of states in reinforcement learning. A RL algorithm is then developed to minimize the long-run average cost (instead of the commonly-used discounted reward) with iterations on the state-action value function and the state value function, respectively. We verify the capability of the proposed algorithm by simulation experiments and demonstrate its advantages in a case study on a battery maintenance decision-making problem. The proposed algorithm outperforms the discrete MDP approach by achieving lower long-run average costs.

Piecewise deterministic Markov process for condition-based maintenance models — Application to critical infrastructures with discrete-state deterioration

In recent decades, the technology and techniques for condition monitoring have experienced a rapid development. However, there is still a need for reducing unnecessary inspections and/or preventive maintenance actions and their associated cost, through optimal design of condition-based maintenance (CBM) strategies. Accordingly, mathematical modelling and optimization of CBM has become of interest for industry managers and researchers. This work explores on the application of a piecewise deterministic Markov process (PDMP) to encompass different modelling assumptions as non-negligible maintenance delays and inspection-based condition monitoring. These assumptions are relevant for many critical infrastructures in civil engineering or in oil & gas industry whose deterioration states are classified at a very high level of abstraction among a finite and small set of possible states. A formalism to model this type of problems is proposed in which the deterministic motion of the PDMP is reduced to a trivial differential equation to track the time elapsed between events. A numerical scheme for quantification, as an approximation of the Chapman–Kolmogorov equation, is presented. Later, an illustration case dealing with CBM of road bridges by the NPRA (Norwegian Public Roads Administration) is presented, guiding through the modelling and quantification approach.