Maintenance Strategy For Systems Research Articles

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.

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.

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 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

Optimal maintenance strategy for large-scale production systems under maintenance time uncertainty

Optimal maintenance strategy for large-scale production systems under maintenance time uncertainty

IoT-Enabled Fault Prediction and Maintenance for Smart Charging Piles

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

Analysis of variable system cost and maintenance strategy in life cycle considering different failure modes

Due to the complexity of multi-component systems, components failure modes are intricate. The failure modes lead to maintenance behavior combinations, which lead to a series of maintenance strategies. However, the failure modes of components are mixed in multi-component systems. Few researchers provide component failure modes comprehensively in life cycle, and integrate them into life cycle maintenance strategies for the selection schemes of different phases. In this paper, a variable system cost methodology in life cycle is studied, and maintenance strategies are developed by identifying components failure modes from a life cycle perspective to improve system reliability. One of the keys is determining maintenance behaviors for components in each phase of life cycle. The proposed four failure modes cover all possible failure scenarios of the components in different phases. Then, a selection scheme is developed to determine maintenance behaviors in life cycle. At last, a case of Huaneng wind farm in China is given to demonstrate the proposed method by comparing with a baseline maintenance strategy.

Condition-based maintenance optimisation for multi-component systems using mean residual life

This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations’ costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach.

Eddy Currents Assessment of Rail Cracks Using Artificial Neural Networks in a Laboratory Setup

Although flaws associated with rolling contact fatigue (RCF) and the corresponding traffic induced damage, which are a cause of failure in railways, have been of great concern in railway system maintenance and safety strategies in many countries for at least two decades, this serious problem has not been yet adequately tackled in the Argentine railway system. The present upgrading activities undertaken in the Argentine railway system (in infrastructure and in rolling stock) are prompting the need for R&D in non-destructive testing techniques and procedures, to satisfy requirements of the new rolling stock and to ensure safe and economic operation of passengers and cargo. RCF damage appears as surface and near surface defects and grows into cracks which in time will propagate along the running surface and through the cross-section. Eddy current testing (ET) is a very efficient in-service inspection method for this task, the near field technique being especially recommended for ferromagnetic components.
 In the present paper, an artificial neural network (ANN) method for automatic classification of flaws with lift-off compensation is presented and tested. The tests consist on the ET evaluation of right angle artificial cracks on a rail calibration coupon; the depth of the cracks studied ranged from 1 to 7 mm. The technique permitted to compensate the weakening of the signals caused by the lift-off effect, allowing signal cracks classification with lift-off variations of up to 5.4 mm.
 The effect of crack skewness on the ET signals is also studied. Because the RCF cracks penetrate the rail at oblique angles, (10° to 30° to the rolling surface), an additional uncertainty component is added to the experiments if calibration is made with a piece having perpendicular cracks. In order to estimate this additional uncertainty on the ANN method presented here, further tests were made with a second calibration piece with cracks at 25° to the surface. Comparison of results showed that the peak to peak amplitudes for both types of cracks are not equivalent at all the tested depths.

Intelligent framework for degradation monitoring, defect identification and estimation of remaining useful life (RUL) of bearing

The proposed intelligent framework seamlessly integrates degradation monitoring, defect identification, and remaining useful life (RUL) estimation for a comprehensive and holistic solution to bearing health assessment. It leverages a novel directed divergence (DD) measure to compare probability distributions, generating a Health Indicator (HI) that captures the health status of the bearings. This HI serves as the cornerstone for the framework's cohesive approach. An alert system, utilizing the modified Pauta criterion and iterative window scanning, enhances anomaly detection, while a double-check mechanism minimizes false alarms. For defect identification, dynamic analysis-assisted wavelet filtering harnesses a customized wavelet filter generated from simulated interactions between defective components and rolling elements. In parallel, the framework employs a graph convolution network (GCN) with a 1D health indicator, derived from the proposed directed divergence measure between the reference data and running conditions, to estimate RUL. By unifying these components, proposed framework offers an efficient, integrated, and proactive solution for monitoring, defect identification, and RUL estimation in bearing health assessment, ultimately enhancing system reliability and maintenance strategies.

A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

Industries increasingly rely on intricate multi-component systems, necessitating efficient maintenance strategies to ensure system reliability and minimize downtime. Predictive maintenance, an emerging approach that utilizes data-driven techniques to forecast and prevent failures, holds significant potential in this regard. This paper presents a predictive maintenance strategy tailored specifically for multi-component systems. In order to accurately anticipate the remaining useful life (RUL) of components, we develop a method that combines data and model fusion based on a particle filtering approach and a degradation distribution model. By integrating degradation data with models, our method outperforms traditional model-based approaches in terms of prediction accuracy. Subsequently, we apply an optimized maintenance model to individual components based on the trigger threshold for RUL. This model determines the most optimal maintenance actions for each component, with the aim of minimizing maintenance costs. Furthermore, we introduce an optimized maintenance strategy that incorporates opportunistic maintenance to further reduce the overall maintenance cost of the system. This strategy leverages predicted RUL information to schedule proactive maintenance actions at the opportune moment, resulting in a significant cost reduction compared to traditional periodic maintenance approaches. To validate the feasibility and effectiveness of our proposed strategy, we utilize experimental data from open-source lithium-ion batteries at the NASA PCoE Center. Through this empirical validation, we provide real-world evidence showcasing the applicability and performance of our strategy in a multi-component system.

A Multi-Scale Smart Fault Diagnosis Model Based on Waveform Length and Autoregressive Analysis for PV System Maintenance Strategies

A Multi-Scale Smart Fault Diagnosis Model Based on Waveform Length and Autoregressive Analysis for PV System Maintenance Strategies

Review of maintenance strategies for ship machinery systems

This study presents the state-of-art and future trends of the maintenance approaches to ship machinery systems. A systematic review process is conducted to identify relevant literature. Firstly, literature is examined by bibliometric analyses based on the determination of the distribution of papers by years, the top authors, papers, journals, and organisations. Then, studies in the literature are reviewed and presented. In addition, the advantages and difficulties of each maintenance strategy are discussed. Finally, an effective maintenance policy that can implement in new ship concepts such as autonomous marine vessels is introduced in line with the information from the literature. In the future, it is believed that maintenance practices will be performed via the condition-based approach when the evolution of followed maintenance operations in ship machinery systems is examined. However, it should be underlined that more effective and powerful maintenance management can be achieved by supporting the system performance monitoring with reliability and risk approaches.

IABSE Survey of Implemented Decision-making Models used by Public and Private Owners/Operators of Road- and Railway Infrastructures

Infrastructure systems, such as bridges, are a driver for the economic growth and sustainable development of countries. Similarly, the development of operation and maintenance strategies for infrastructure systems may aim at optimal management using Key Performance Indicators (KPIs) such as reliability, redundancy, availability, safety, economy, environmental performance and resilience. Recent research and development projects, such as COST TU1406, highlight that infrastructure managers make decisions based on a mix of qualitative and quantitative data from various sources paired with models of various levels of complexity as well as expert judgement. Similarly, recent state-of-the-art academia reports on a variety of different decision-making models applicable to the optimal management of infrastructure systems may be used. Within IABSE Commission 5 on Existing Structures, Task Group 5.4 has performed a survey on implemented decision-making models among 23 infrastructure managers from 20 countries. It highlights some similarities in relation to KPIs, condition rating and limit state checks. This has stimulated the standardisation of decision making. The application of risk-based methods, performance prediction and intervention modelling are somewhat more scattered and may call for further research and development as well as training. The need to bridge the gap between implemented decision-making models and research is of paramount importance.

Optimal maintenance tasks taking into account cost indicators

The reliability of technical systems plays a key role in ensuring their efficient operation over time. This article discusses the importance of reliability in maintaining preset parameters of the functioning of a system and its dependence on operating conditions. Maintenance is done to maintain the reliability of a system and control its functionality. The authors propose methods for selecting quality indicators and the optimal frequency of preventive maintenance and emphasise the importance of considering cost aspects when making decisions on maintenance. In conclusion, the article offers a brief algorithm for quality control of technical systems while placing an emphasis on system safety and economic efficiency. The study demonstrates the need to develop and apply new cost-based analysis methods to determine optimal maintenance strategies for technical systems. Overall, the article provides recommendations for improving maintenance operations and increasing the reliability of technical systems.

Remaining Useful Life Estimation for Underground Cable Systems Based on Historical Health Index

In this paper, a modeling method for estimating the remaining useful life (RUL) of aged underground cable systems is proposed that uses statistical health index (HI) and operating factor (OF) data of retired systems. The HI is an indicator which identifies the condition of an underground cable system and its components and is calculated from testing and inspection results. The OF takes actual operating conditions and technical data from the system into consideration. Both factors are then combined to determine the overall health index (OHI) of each system. For RUL estimation of underground cable systems, normal distribution and Weibull distribution analyses are first applied to determine a health index curve and an aging line. The relationship between these two curves gives an estimate of the system’s apparent age. The RUL of the system is then calculated in terms of the difference between its apparent age and its actual chronological age. In this study, thirteen retired systems and ten operating systems were evaluated and analyzed, and accurate results were obtained. Using the methods described here, the apparent age and the RUL of underground cable systems can be accurately estimated. Finally, a maintenance strategy for underground cable systems is recommended, which promises more efficient maintenance and greater cost-effectiveness in the management of underground cable systems.

Risk-Informed Bridge Optimal Maintenance Strategy Considering Target Service Life and User Cost at Project and Network Levels

As the concept and methods of maintenance of infrastructure systems are receiving increasing attention, the optimal maintenance strategy for a group of structures is a promising research topic. Among the factors affecting the decision-making of optimal maintenance strategy for civil infrastructure systems, the target service life and user cost estimation approach are significant. Currently, user cost estimation at the project level is widely adopted, whereas the user cost estimation at the network level is in the development stage. This paper investigates the impact of several factors on the risk-informed optimal maintenance strategy for a bridge network under corrosion, including target service life, user cost estimation approach, and correlation among bridge safety margins. The novelty of this paper consists of considering the effects of these factors on the optimal maintenance strategy of bridge networks. These effects, which are crucial for adopting the optimal maintenance strategy, were not investigated in a detailed manner previously. Two different maintenance strategies are considered for the maintenance of corroded steel girders. The former is to conduct replacement with new carbon steel girders, and the latter is to use A709-50CR steel, where CR stands for corrosion resistant, a new type of steel with a chromium content similar to that of martensitic stainless steel, in replacement actions. Using an existing bridge network under a low life-cycle risk threshold, it is shown that replacement based on A709-50CR girders results in a reduced life-cycle network maintenance cost than replacement based on carbon steel girders. Also, the project-level approach to estimate user cost can lead to a substantial increase in the life-cycle network maintenance cost compared with the network-level approach.