Maintenance Strategy For Systems Research Articles

Multi-objective maintenance strategy for complex systems considering the maintenance uncertain impact by adaptive multi-strategy particle swarm optimization

Effective maintenance optimization strategies are crucial for improving the complex equipment reliability and reducing the maintenance costs. However, the effectiveness of the maintenance procedures applied to industrial equipment is affected by uncertainty, e.g. due to the professional skills of maintenance personnel, the actual condition of the equipment being maintained. The quantification of the uncertainty on the effect of maintenance has practical significance and must be accounted in the development of maintenance strategies for reducing equipment probability of failure. This paper proposes a multi-objective maintenance strategy considering the uncertain impact of the maintenance actions. The impact of maintenance actions on the reliability of components is first studied and a reliability assessment model is developed, which considers the skill of maintenance personnel and the actual condition of the equipment. To optimize the multi-objective maintenance strategy, a multi-strategy particle swarm optimization (MS-PSO) algorithm is proposed. Two case studies are considered to verify the effectiveness of the proposed approach for multi-objective maintenance strategy optimization. In the case studies considered, it turns out that the maintenance cost rate (MCR) is reduced throughout the system life cycle and the cumulative availability is improved.

Opportunistic maintenance strategy for wind turbine systems based on fault correlation and multi-level maintenance theory

Opportunistic maintenance strategy for wind turbine systems based on fault correlation and multi-level maintenance theory

Preventive maintenance strategy for multi-component systems in dynamic risk assessment

As the system operates, the system risk level will also dynamically change. In this paper, a dynamic risk assessment of the system is carried out by considering the system reliability and system risk losses, both of which vary over time. Then, based on the system risk level, different maintenance measures are applied to the components that have reached the preventive maintenance thresholds, including medium repair, major repair, and replacement. If the system risk is relatively light, low-cost medium repair will be adopted to save maintenance resources. Thus, a novel maintenance optimization strategy for multi-component systems considering the system risk level and aiming to minimize maintenance costs is proposed. Finally, the feasibility and effectiveness of the model are verified through a numerical case of the air conditioning temperature regulation subsystem.

New Approach for Tracking, Monitoring, and Diagnosing Faults in a Photovoltaic System in Real Time: An Experimental and Case Study

Photovoltaic installations have emerged as a cornerstone of sustainable energy production, playing a pivotal role in the global transition towards renewable sources of electricity. As the demand for clean energy surges, so too does the need for robust monitoring and diagnostic strategies to ensure the efficient operation of these systems. This study presents a novel methodology for the real-time tracking, monitoring, and diagnosing of faults in photovoltaic systems (PVSs), emphasizing their crucial role in sustainable energy production amid the global shift to renewable energy. The study was conducted in Guelma, Algeria, during the spring and summer seasons. The investigation utilized WatchPower simulation software over a 24-hour performance analysis of the photovoltaic system. On June 19, 2024, with a high temperature of 32°C, the system achieved a peak output power of 600W under optimal conditions, validating its efficiency in energy generation. The study also analyzed the effects of shading on energy output by comparing data from a shaded day on May 14, 2024, at 25°C, to an unshaded day on June 21, 2024, at 32°C, during peak sunlight hours from 9:00 AM to 2:15 PM, the period when sunlight is at its strongest. The results showed a significant drop in output power from 600W to 450W due to shading, underscoring the importance of real-time monitoring to detect performance inefficiencies. This research not only enhances operational reliability and maintenance strategies for PV systems but also demonstrates the effectiveness of integrating real-time data analytics to support decision-making in similar environments.

Semi-Markov process-driven maintenance scheduling for Tainter gate system considering multiplelimit states

In extended periods of operation, Taiwan’s reservoir electromechanical systems increasingly require substantial maintenance. This research adopts the semi-Markov process, which accommodates non-exponential distribution of state durations, to formulate optimal maintenance strategies for Tainter gate systems that are noted for their prolonged dormancy and significant operational uncertainties. The methodology initiates with the estimation of failure probabilities across four condition states, analyzing deterioration through the Weibull distribution for both general and latent limit states. The general limit state accounts for time-induced deterioration and effects of dormancy using a Bayesian–Weibull first-order reliability method, while the latent limit state addresses activation failures. Employing the semi-Markov process, an annual transition matrix is computed and combined with failure probabilities to assess the Tainter gates’ system reliability. To identify the most efficient maintenance schedule, a genetic algorithm is applied, targeting the minimization of failure probabilities for both limit states below predefined thresholds and cost reduction. Numerical simulations validate the framework’s efficacy, demonstrating its potential to enhance maintenance planning objectivity and decrease dependence on subjective assessments. The findings highlight the predominance of the general limit state in dictating system failure and underscore the risk Tainter gates face during transition from dormancy to activation, emphasizing the need for thorough monitoring.

Research on Classification Maintenance Strategy for More Electric Aircraft Actuation Systems Based on Importance Measure

In this paper, a practical maintenance algorithm is proposed to improve the reliability of actuation systems and their components, specifically addressing the consistency degradation caused by faults in the symmetric actuation system components of more electric aircraft (MEA). By integrating important measures with traditional genetic algorithms, the accuracy of the algorithm is improved. Prior to maintenance, a reasonable classification of components is built to mitigate the adverse effects of extreme fault conditions on the algorithm. This approach improves both the effectiveness and efficiency of the algorithm, rendering the overall maintenance strategy better suited for real-world needs. Finally, comparative simulations confirm the algorithm’s superior performance in reliability improvement, demonstrating its substantial contribution to the field of MEA maintenance and reliability.

Towards equitable infrastructure asset management: Scour maintenance strategy for aging bridge systems in flood-prone zones using deep reinforcement learning

Bridges play a critical role in transportation networks; however, they are vulnerable to deterioration, aging, and degradation, especially in the face of climate change and extreme weather events such as floodings. Furthermore, bridges can significantly affect social vulnerability; their damage or destruction can isolate communities, inhibit emergency responses, and disrupt essential services. Maintaining critical bridges in a cost-effective and sustainable manner is crucial to ensure their longevity and protect vulnerable communities. To address the maintenance optimization problem of bridge systems considering the effects of time deterioration, flood degradation, and social vulnerability, this study proposes a deep reinforcement learning algorithm to optimally allocate resources to bridges that are at expected cost of failure due to scour. The algorithm considers the effects of flood degradation with different return periods and is trained using a Markov Decision Process as the environment. The study conducts four flood simulation scenarios using Geographic Information System data. The findings suggest that the deep reinforcement learning algorithm proposes a sequence of repair actions that outperforms the status quo, currently employed by bridge managers. The significance of this study lies in its valuable insights for cities worldwide on how to effectively optimize their limited resources for the maintenance and rehabilitation of critical infrastructure systems to decrease portfolio cost and increase social equity.

AI and the Future of Network Administrator (LAN) Preventive Maintenance

This paper delves into the pivotal role of AI in predictive maintenance strategies for industrial systems, focused in the filed of LAN operation and maintenance. It will shed some insight on the current methodology and how AI will transform the traditional ways for conducting preventive maintenance tasks.

Reliability model and emergency maintenance strategies for smart home systems

After the smart home system is impacted by malicious attacks or other factors, the system reliability drops drastically, so the user's living environment is greatly disturbed. To determine the emergency maintenance strategy, firstly, we study the failure mechanism of the smart home system by the fault tree theory, and distinguish the components into two types: critical components and non-critical components. Secondly, a recovery prioritization is proposed to measure the extent to which changes in the reliability of each component affect the reliability of the system. Then, three scenarios of smart home system failure are proposed. Under the constraints of time and cost, the maintenance strategies for the three scenarios are determined to achieve emergency recovery. Finally, through the multi-objective particle swarm optimization algorithm, the system reliability is maximized, the maintenance cost is minimized, and the optimal reliability recovery level of each component is obtained, so that the reliability of the smart home system is further improved. The effectiveness and practicality of the emergency maintenance strategies proposed in this paper are demonstrated by a smart home system example.

Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features

Predictive maintenance to avoid fatigue and failure enhances the reliability of mechanics, herewith, this paper explores vibrational time-domain data in advancing fault diagnosis of predictive maintenance. This study leveraged a belt-drive system with the properties: operating rotational speeds of 500–2000 RPM, belt pretensions at 70 and 150 N, and three operational cases of healthy, faulty and unbalanced, which leads to 12 studied cases. In this analysis, two one-axis piezoelectric accelerometers were utilized to capture vibration signals near the driver and pulley. Five advanced statistics were calculated during signal processing, namely Variance, Mean Absolute Deviation (MAD), Zero Crossing Rate (ZCR), Autocorrelation Coefficient, and the signal's Energy. The Taguchi method was used to test the five selected features on the basis of Signal-to-Noise (S/N) ratio. For classifications, an expert system was used based on artificial intelligence where a Random Forest (RF) model was trained on untraditional parameters for optimizing the accuracy. The resulted 0.990 and 0.999, accuracy and AUC, demonstrate the RF model's high dependability. Evidently, the methodology highlights the features potential when progressed into expert systems, which advances predictive maintenance strategies for belt-drive systems.

Research on opportunity maintenance strategy for PV power generation systems considering random impact

Research on opportunity maintenance strategy for PV power generation systems considering random impact

An I–V characteristic reconstruction-based partial shading diagnosis and quantitative evaluation for photovoltaic strings

The partial shading condition (PSC) is the most common abnormality occurred in photovoltaic (PV) systems. Accurate quantitative evaluation of the shaded area and the severity of the shading is of potential importance in optimizing the maintenance strategy for PV systems. In this paper, we propose a PSC diagnosis and quantitative evaluation method by analyzing the measured string current–voltage (I–V) characteristic obtained from the PV inverter with the I–V scanning function, which includes pre-diagnosis of the system abnormality based on the operational power deviation, the diagnosis of PSCs based on the derivatives characteristics of the PV string, and the quantitative evaluation based on the I–V characteristic reconstruction. The quantitative evaluation of PSCs is the main focus in this paper, where the I–V characteristics of the unshaded PV modules in the partially shaded PV string are reconstructed according to different mismatch levels, respectively. The number of shaded PV modules and the corresponding severity of the partial shadings are estimated according to the reconstructed I–V characteristics. The simulation and experimental results verify that both the proposed diagnosis and quantitative evaluation method is effective with decent accuracy, especially for severe mismatch conditions. Experimental results show that the maximal mean absolute error of the quantified shaded area and quantified shaded rate are approximately 1.4446 units of 1/3 PV modules and 0.026, respectively.

Optimal maintenance policies for three-unit parallel production systems considering yields

Many production facilities consist of multiple parallel units to facilitate the completion of production plans, yet these devices are inevitably prone to malfunctions with increased operating time. Therefore, developing the optimal maintenance policy to maximize the production capacity of production facilities is a crucial issue. This paper focuses on determining the optimal maintenance policy for a three-unit production system, aiming to maximize its yield. The optimal solution is provided based on the given maintenance policy, and a general optimal maintenance policy for the system is derived. The properties related to the optimal maintenance policy are analyzed. The proposed model offers two notable contributions when compared to existing research. Firstly, a maintenance policy based on the number of visits and yields is developed. Secondly, the parallel assumption of the system and the assumption that the subsystems satisfy any distribution are in line with reality, enriching the research on maintenance strategies for production systems. To illustrate the results of the model, a numerical example is presented.

A Novel Hybrid Approach to the Diagnosis of Simultaneous Imbalance and Shaft Bowing Faults in a Jeffcott Rotor-Bearing System

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalances and shaft bowing in these systems can lead to decreased efficiency and increased vibrations. The early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry over the years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) methods has become a trend, and in the foreseeable future the combination of AI with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as often insufficient fault data are available. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalances and shaft bowing are demonstrated) in a Jeffcott rotor. To overcome the lack of real-world labeled fault datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose imbalance and shaft bowing faults using the simulated and real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-faults, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

Reliability-Based Preventive Maintenance Strategy for Subsea Control System

The subsea control system, a pivotal element of the subsea production system, plays an essential role in collecting production data and real-time operational monitoring, crucial for the consistent and stable output of offshore oil and gas fields. The increasing demand for secure offshore oil and gas extraction underscores the necessity for advanced reliability modeling and effective maintenance strategies for subsea control systems. Given the enhanced reliability of subsea equipment due to technological advancements, resulting in scarce failure data, traditional reliability modeling methods reliant on historical failure data are becoming inadequate. This paper proposes an innovative reliability modeling technique for subsea control systems that integrates a Wiener degradation model affected by random shocks and utilizes the Copula function to compute the joint reliability of components and their backups. This approach considers the unique challenges of the subsea environment and the complex interplay between components under variable loads, improving model accuracy. This study also examines the effects of imperfect maintenance on degradation paths and introduces a holistic lifecycle cost model for preventive maintenance (PM), optimized against reliability and economic considerations. Numerical simulations on a Subsea Control Module demonstrate the effectiveness of the developed models.

A maintenance strategy for hydraulic systems based on generalized stochastic Petri nets under epistemic uncertainty

A maintenance strategy for hydraulic systems based on generalized stochastic Petri nets under epistemic uncertainty

Instrument Automation Control System Faults and Maintenance

This article explores the topic of fault diagnosis and maintenance strategies for instrument automation control systems, analyzing them through specific cases. The aim of this research is to improve the stability and reliability of the system by conducting a thorough investigation of faults and maintenance in instrument automation control systems. By doing so, this research hopes to provide a strong guarantee for the smooth progress of industrial production.

Opportunistic maintenance strategies for PV power systems considering the structural correlation

Opportunistic maintenance strategies for PV power systems considering the structural correlation

Performance assessment of a potential maintenance strategy for legacy avionic systems

Performance assessment of a potential maintenance strategy for legacy avionic systems

Research on Operation and Maintenance Strategy of New Energy Photovoltaic Power Generation System Considering Extreme Weather Conditions

Abstract This paper employs the gray correlation analysis method to compute the correlation between irradiance, ambient temperature, wind speed, and photovoltaic power generation efficiency. This method relies on screened data for data normalization, and after processing, it determines the degree of similarity between the factors. We propose a dynamic combined maintenance strategy for PV power generation systems that considers reliability, overhaul resources, and overhaul time constraints. We propose a two-layer optimization strategy based on extreme weather factors and failure-related characteristics, and we use empirical analysis to investigate the maintenance strategy’s effectiveness. The analysis reveals a positive correlation between irradiance and PV power generation efficiency, with a multiplicative increase in the range of 1-3. In the correlation analysis of ambient temperature and wind speed variables, the power generation curve is not consistent with its trend, which is related to the influence of extreme temperature and extreme wind speed, and the meteorological factors affecting the PV power generation efficiency change at different moments of the day, where the correlation coefficient at 6:45 is 0.912. When the failure correlation strength increases to a value of 0.1, the number of repairs rises to 8. When the weather accessibility ratio is 0.2, the reduction in system availability is up to 62%. The two-layer optimization strategy, which considers extreme weather factors and failure-related characteristics, is both economical and universal.