Maintenance Decision Research Articles

Opportunities for the German gas grid by using synthetic fuels from an energy system perspective

The German government’s goals of climate neutrality by 2045 are exerting high pressure on the gas grids, as natural gas consumption has to cease. However, gas grids could be operated further by using synthetic energy carriers. As this heavily affects grid maintenance and future investment decisions, it is essential that operators gain clarity about the future of their assets. This paper applies the TIMES PanEU energy system model to examine opportunities for the gas grid arising from synthetic fuels from a system perspective. Therefore, the model is methodically enhanced by depicting infrastructures and conducting an integrative analysis of the gas grid. Up to 50% of the grids can be operated economically in case low-cost synthetic methane is available, low-cost hydrogen can allow around 2/3 of the gas transmission grids to be operated. However, since the building sector uses about 2/3 less gaseous fuels even in optimistic scenarios, the distribution grids are strongly deconstructed in all scenarios. From a system perspective, investments in the expansion of the gas grids therefore seem to be rather sunk costs in the long term, since a more or less strong dismantling appears to be inevitable, with permanently high fossil gas prices even accelerating this trend.

Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data

Pavement roughness serves as a crucial indicator for evaluating road performance. However, traditional measurement methods, such as laser detection vehicles, are limited to providing roughness values for a single profile, failing to capture the overall pavement condition comprehensively. To address this limitation, this study utilized high-precision light detection and ranging technology (LiDAR) to acquire three-dimensional point cloud data for a 25 km road section in Shanghai. Road elevations were extracted from different lateral survey lines. Subsequently, variance analysis and the Kruskal–Wallis non-parametric test were conducted to evaluate the differences in the lateral distribution and longitudinal variability of the pavement roughness. The findings revealed significant differences in the international roughness index (IRI) among the survey lines within the road section. Moreover, the observed variations in the lateral distribution of pavement roughness were influenced by the characteristics of the road section itself. Roads exhibiting discrete roughness patterns displayed a higher likelihood of significant detection disparities. Additionally, it was discovered that the discrepancy between the detection length and the actual road length introduced volatility in repeated detection results, necessitating a limitation of this discrepancy to 30 m. Consequently, it has been recommended to consider the lateral distribution of pavement roughness and to regulate the detection length in road performance evaluations to enhance reliability and facilitate more accurate maintenance decision making. The study highlights the importance of incorporating comprehensive assessment approaches for pavement roughness in road management practices.

Optimal switching and condition-based maintenance of cold standby systems subject to competitive failure and imperfect switchover

Cold standby redundant structures can improve system reliability and availability but are subjected to internal degradation and external shocks during operation. Thus, the switching process in a standby system is prone to failures. This study investigates the optimal switching and condition-based maintenance (CBM) decision of a two-unit cold standby system subject to mutually dependent competitive failure processes with an imperfect switchover. First, periodic switching and offline CBM policies under imperfect switchover are proposed; second, the reliability models of competitive failure—between soft failure caused by system degradation subject to nonfatal shock and hard failure caused by fatal shock—are established. An iterative model of the system state probability distribution is subsequently derived by analysing the characteristics of state transitions in alternate use and maintenance of the active and standby units. An analytical decision model is also established to minimise the average cost rate in a finite horizon to obtain the optimal switching period and maintenance threshold. Finally, a case study of a coal mine main ventilator system validates the proposed model.

Enhancing Winter Road Maintenance via Cloud Computing

In this study, we developed an AI-enhanced cloud computing framework to enable the autonomous decision-making quality and precision of winter road operations for connected living, namely Smart Maintenance Decision Support System (SmartMDSS). With the support from the Federal Highway Administration (FHWA) and Michigan Department of Transportation, we are among the first to develop AI-enabled tools for practical winter road maintenance decision-making. On the front end, SmartMDSS provides a user-friendly graphical interface showing all the valuable data and winter operations for specific points on the road and sends necessary warnings and notifications. On the back end, SmartMDSS extracts and analyzes data and makes winter road maintenance decisions. Convolutional Neural Network (CNN) has been successfully employed to identify the snow coverage on the road surface. Decision-making algorithms were proposed and implemented to support the road engineers and operators for real-time winter maintenance operations.

A Review of Prognostic and Health Management (PHM) Methods and Limitations for Marine Diesel Engines: New Research Directions

Prognostic and health management (PHM) methods focus on improving the performance and reliability of systems with a high degree of complexity and criticality. These systems include engines, turbines, and robotic systems. PHM methods involve managing technical processes, such as condition monitoring, fault diagnosis, health prognosis, and maintenance decision-making. Various software and applications deal with the processes mentioned above independently. We can also observe different development levels, making connecting all of the machine’s technical processes in one health management system with the best possible output a challenging task. This study’s objective was to outline the scope of PHM methods in real-time conditions and propose new directions to develop a decision support tool for marine diesel engines. In this paper, we illustrate PHM processes and the state of the art in the marine industry for each technical process. Then, we review PHM methods and limitations for marine diesel engines. Finally, we analyze future research opportunities for the marine industry and their role in developing systems’ performance and reliability. The main added value of the research is that a research gap was found in this research field, which is that new advanced PHM methods have to be implemented for marine diesel engines. Our suggestions to improve marine diesel engines’ operation and maintenance include implementing advanced PHM methods and utilizing predictive analytics and machine learning.

RGCNU: Recurrent Graph Convolutional Network With Uncertainty Estimation for Remaining Useful Life Prediction

Dear Editor, This letter focuses on the problem of remaining useful life (RUL) prediction of equipment. Existing graph neural network (GCN)-based approaches merely provide the point estimation of RUL. However, the estimated RUL often varies widely due to the model parameters and the noise in data. It is important to know the uncertainty in predictions for reliable risk analysis and maintenance decision making. To map the relationship between noisy condition monitoring data and RUL with uncertainty, we propose a recurrent graph convolutional network with uncertainty estimation (RGCNU) for RUL prediction. In our approach, the correlation exploiting module captures the spatial-temporal correlations based on the learned graph structure. Furthermore, the fusion module associates the RUL prediction and data uncertainty to improve the robustness of the model to noisy data.

Landslide Hazard Assessment in Highway Areas of Guangxi Using Remote Sensing Data and a Pre-Trained XGBoost Model

This study presents a novel method for assessing landslide hazards along highways using remote sensing and machine learning. We extract geospatial features such as slope, aspect, and rainfall over Guangxi, China, and apply an extreme gradient boosting model pre-trained on contiguous United States datasets. The model produces susceptibility maps that indicate landslide probability at different scales. However, the lack of accurate data on historical landslides in Guangxi challenges the model evaluation and comparison between regions. To overcome this, we calibrate the model to fit the local conditions in Guangxi. The calibrated model agrees with the observed landslide locations, implying its capability to capture regional variations in landslide mechanisms. We apply the model at a 30 m resolution along the Heba Expressway and validate it against reports from July 2021 to March 2022. The model correctly predicts five of seven landslide events in this period with a reasonable alarm rate. This framework has the potential for large-scale landslide risk management by informing transportation planning and infrastructure maintenance decisions. More data on landslide timing and human disturbance events may improve the model’s accuracy across diverse geographical areas and terrains.

Automated and real-time anomaly indexing for district heating maintenance decision support system

Faults are estimated to cause a significant amount of energy consumption and waste in district heating systems (DHS), and lowering this is critical to reduce costs and energy waste, but also to increase the security of supply. There are multiple examples of data-driven fault detection and diagnosis methods being employed in DHS to detect faults, however, it has been observed that a great number of faults are detected and it is not sustainable to investigate all of them. This paper, therefore, proposes implementing Chernoff bound, which is a method that can determine the fault probability level of detected faults to filter out insignificant faults. This methodology is integrated into a general continuous fault detection maintenance framework which can utilize multiple fault detection and diagnosis methods. The Chernoff bound methodology is modified for better generalization and implementation, with regard to the input data and other methods employed. The methodology has been applied to a DHS case and has proven to be able to filter out insignificant faults while still retaining a known fault, and identifying other anomalies in the operation. This framework and the introduction of Chernoff bound as a means to rank the confidence of the various detection of faults, can help maintenance crews allocate their time and effort in a better way, to only focus on the most potential critical faults. The data used as a case study is of 1-hour frequency, and some, but not all faults can be observed in such data. The optimal data is hypothesized to be of higher frequency, increasing the capabilities of the framework.

Data-driven ballast layer degradation identification and maintenance decision based on track geometry irregularities

ABSTRACT Ballast layer defects are the primary cause for rapid track geometry degradation. Detecting these defects in real-time during track inspections is urgently needed to ensure safe train operations. To achieve this, an indicator, the track degradation rate (TDR) was proposed. This rate is calculated using track geometry inspection data to locate and predict railway-line sections with ballast layer defects. The TDR is determined by the monthly standard deviation of the rail longitudinal level, which is one aspect of track geometry. The Ballast Layer Health Classification (BLHC) is designed by assessing the two successive TDRs before and after track geometry maintenance actions. The BLHC is used to categorize the conditions of the ballast layer, including normal periodic deterioration, abrupt deterioration, effective maintenance, rising deterioration, and severe deterioration. Both the TDR and BLHC were validated through field assessments of ballast layer conditions, where the two indicators were found to be effective in revealing defects. The results indicate that the TDR is sensitive to ballast layer defects, while the BLHC can quickly identify the location of these defects. Consequently, the BLHC can provide real-time guidance for ballast layer maintenance.

Dual-frequency enhanced attention network for aircraft engine remaining useful life prediction

Accurately evaluating the remaining useful life (RUL) of aircraft engines is crucial for ensuring operational safety and reliability, and serves as a critical foundation for making informed maintenance decisions. In this paper, a novel prediction framework is proposed for forecasting the RUL of engines, which utilizes a dual-frequency enhanced attention network architecture built upon separable convolutional neural networks. First, the information volume criterion (IVC) index and information content threshold (CIT) equation are designed, which are applied to quantitatively quantify the degradation features of the sensor and remove redundant information. In addition, this paper introduces two trainable frequency-enhanced modules, namely the Fourier transform module (FMB-f) and the wavelet transform module (FMB-w), to incorporate physical rules information into the prediction framework, dynamically capture the global trend and local details of the degradation index, and further improve the prediction performance and robustness of the prediction model. Furthermore, the proposed efficient channel attention block generates a unique set of weights for each possible vector sample, which establishes the interdependence among different sensors, thereby augmenting the prediction stability and precision of the framework. The experimental demonstrate that the proposed RUL prediction framework can deliver accurate RUL predictions.

A new method for remaining useful life prediction by implementing joint learning of sensor dynamic graph and spatio-temporal features

Accurate implementation of remaining useful life (RUL) prediction of equipment is essential for health management and maintenance decisions. Advances in sensing and big data technologies have provided the possibility for research on data-driven life prediction methods. However, the current RUL prediction methods still need to improve in utilizing multi-source information. The present techniques consider more the relationship between the temporal information and RUL prediction among the multi-source information and rarely target the research and exploitation of the potential connection between sensor networks and RUL. Therefore, this paper proposes a spatio-temporal feature extraction network based on the sensor dynamic graph: DST-GT model. The method extracts the unidirectional relationship between sensors from monitoring data to construct a dynamic spatio-temporal graph reflecting the sensor relationship. The DST-GT model uses graph convolution based on message selection (MSGCN) to model the spatial dependencies of sensors, and uses multi-scale gated temporal convolution module (MGTCN) to model the temporal dependencies in sensor state monitoring data. In this paper, graph learning, graph convolution and temporal convolution modules are jointly learned in an end-to-end framework. The results on two widely used datasets and comparisons with other methods demonstrate the accuracy and advancement of DST-GT networks for RUL prediction.

Research on the evaluation method of power grid equipment input–output efficiency based on LCC

The quantitative evaluation of the comprehensive efficiency of the equipment has important significance for making accurate maintenance and other decisions. From the perspective of equipment life cycle cost, this paper constructed the fixed cost function and dynamic cost function of power equipment and obtained the economic life model. The total cost of the whole life cycle of the optimal economic life was obtained by the derivation method. Then, a comprehensive performance evaluation index system including reliability, availability, maintainability and economy had been established. The comprehensive efficiency evaluation model based on economic life and the equipment maintenance optimization model based on comprehensive efficiency evaluation were designed. At last, taking 35 kV circuit breaker as an example, the economic life model and comprehensive efficiency evaluation model were analyzed and calculated. The results proved the correctness of the comprehensive efficiency evaluation model designed in this paper, which could provide technical guidance and support for the selection and maintenance of power equipment.

MODULAR MAINTENANCE DECISION ARCHITECTURE

AbstractThe operation of large production assets requires many decisions from the acquisition and design of new assets to the choice of lubricant for a specific piece of equipment. The decisions made in maintenance have a direct effect on the management of the production process, making it important to ensure correct maintenance decision making. However, studies on maintenance decision making tend to focus on smaller areas of decisions being made in a process, but rarely the entire process. To introduce more studies that consider the entire maintenance process, this paper proposes using a modular Maintenance Decision Architecture. The paper introduces a framework for structuring information sources into standardized information modules and mapping them to maintenance decisions made across the entire organization. The application of approaches from product, system, and service engineering are used to support the management of the complexities of maintenance of large production facilities.

Time series prediction based on LSTM neural network for top tension response of umbilical cables

The umbilical cable is an essential component of offshore oil and gas extraction systems. The severe marine environment poses a high challenge to the safety of the umbilical cable structure during operation. The analysis of an umbilical cable requires complex and resource-demanding finite element time-domain simulations to obtain their nonlinear dynamic response. Therefore, in order to solve the problem of structural safety monitoring and real-time assessment of remaining life of umbilical cables under extreme sea states, there is a great need to predict the dynamic response of umbilical cables quickly and accurately during operation, for ease of making fast decisions for system operation and maintenance before the arrival of extreme sea states. Given the strong nonlinear function-approximation ability of the neural network, this study proposes an efficient method for the prediction of the time series of umbilical cable top tension response based on LSTM (long short-term memory) neural network. We use LSTM neural network and ARIMA (autoregressive integrated moving average) model in a real engineering case for time series prediction of the top tension response of the umbilical cable, and the results of the two models are analyzed and compared, and the efficiency and accuracy of the LSTM neural network model are verified. Furthermore, the hyperparameter, dataset and generalization ability of LSTM model are discussed. The results indicate that feasibility of the tension response prediction of umbilical cables under dynamic load in complex marine environments.

Predicting Rutting Development of Pavement with Flexible Overlay Using Artificial Neural Network

Pavement maintenance and repair is a crucial part of pavement management systems. Accurate and reliable pavement performance prediction is the prerequisite for making reasonable maintenance decisions and selecting suitable repair schemes. Rutting deformation, as one of the most common forms of asphalt pavement failures, is a key index for evaluating the pavement performance. To ensure the accuracy of the commonly used prediction models, the input parameters of the models need to be understood, and the coefficients of the models should be locally calibrated. This paper investigates the prediction of the rutting development of pavements with flexible overlays based on the data of the Canadian Long-Term Pavement Performance (C-LTPP) program. Pavement performance data that may be related to rutting were extracted from the survey of Dipstick for data analysis. Then, an artificial neural network (ANN) was adopted to analyze the factors affecting the rut depth, and to establish a model for the rutting development of pavements with flexible overlays. The results of the sensitivity analysis indicate that rutting is not only affected by traffic and climatic conditions, but it is also greatly affected by the thickness of the surface layer and voids in the mixture. Finally, a rutting evaluation index was provided to describe the rutting severity, and the threshold of the pavement maintenance time was proposed based on the prediction results. These results provide a basis for predicting rut development and pavement maintenance.

Prioritization of factors influencing outsourcing maintenance decisions in thermal power plants – A case study

PurposeAlthough the factors that affect the decision of outsourcing are variant from one industry to another and from one application to another, this paper aims to investigate the factors that influence the decision of outsourcing using a systematic literature review (SLR) approach to fill the gap of the literature and focus on maintenance activities in terms of industrial firms. Furthermore, the analytical hierarchy process (AHP) method will be developed to prioritize the important factors according to the experts' judgments and reveal the factors' weights to support decision-makers in thermal power plants to realize the factors that have a high impact on outsourcing maintenance decisions and enhance their decision-making to increase power plant competitive advantages over quality, availability and safety requirements.Design/methodology/approachThe purpose of this research is to prioritize the factors that influence outsourcing maintenance decisions to be considered by decision-makers in power plants before making a decision of outsourcing maintenance activities. In this research, an SLR approach was applied to identify the factors in terms of industry and rank them accordingly using factors' frequency process. A questionnaire survey was designed to seek the experts' opinions to prioritize the factors that influence the decision of outsourcing maintenance activities in power companies using AHP. The research found that “Improve service quality”, “acquire new skills or technical knowledge” and “Function difficult to manage” have the first priority before making the decision of outsourcing maintenance activities in the context of power plants.FindingsThe decision of outsourcing regarding maintenance activities in the context of industry, as well as determining the most critical factors among the extracted factors from the literature and rank them accordingly using factors' frequency process. Next, a questionnaire survey was designed and conducted to seek the experts' opinions to prioritize the factors that influence the decision of outsourcing concerning maintenance activities in thermal power companies using AHP method. According to the SLR, industrial companies are mainly pursuing outsourcing maintenance services to focus on core activities, save overall costs and improve service quality.Originality/valueThis is an original research paper out of an MSc thesis submitted at the Industrial Engineering Department, University of Jordan.

Real-Time Humidity Monitoring Using Distributed Optical Sensor for Water Asset Condition Assessment

It is well accepted that moisture ingress in concrete reduces durability and life span of water assets. Condition assessment is an important tool to inform decision for maintenance, retrofit or replacement. However, the most significant challenge is to obtain accurate condition information, particularly when the inspection points are physically difficult to access or inaccessible. Therefore, a reliable and cost-effective monitoring (sensor) system, preferably real-time with ability to streaming online, would be a useful management tool, particularly for water utilities. This paper describes an approach to develop a distributed optical fibre humidly sensor for condition assessment and environmental monitoring both inside and outside of infrastructures, such as inside the concrete and surrounding soil. A new polyelectrolyte multilayer (PEM) coating with higher sensitive was evaluated for relative humidity measurement in soil and concrete, respectively. In this study, two simulated conditions, in concrete and soil, were conducted to evaluate the sensing concept with the development of appropriate measuring methodologies including fibre installation and protection. The optical fibre sensor setup in laboratory environment showed that optical sensor can detect and indicate voltage change with the variation of moisture contents in both soil and concrete. The test results indicate a good correlation between high levels of relative humidity/moisture and transmitted optical power. A simple relative humidity (RH) calibration can be used to convert signal to RH in percentage for soil and concrete measurements and the procedure used to imbed the fibre in both samples is effective. Nevertheless, the sensor measures soil humidity (not moisture content); therefore, further investigation is required to identify the consequence for the variation of the measured parameter.

Optimization of maintenance for complex manufacturing systems using stochastic Remaining Useful Life prognostics

This paper leverages Remaining Useful Life (RUL) prognostic information for preventive maintenance planning in complex manufacturing factories. Such a factory consists of multiple complex systems that use redundant components as backups to ensure system availability. The purpose is to minimize production fluctuations in the factory due to maintenance or system breakdown. To achieve this, we propose a Mixed-Integer Linear Programming (MILP) model that minimizes the overall production loss. Furthermore, we incorporate random RUL decrease rates according to reliability theory and extend the MILP model using chance-constrained programming. A novel approximation method for dealing with chance constraints is proposed to solve the stochastic model and approximation properties are analyzed. Computational results show that (i) our MILP model can provide more reasonable maintenance decisions compared to the literature. (ii) Sensitivity analysis reports that RUL prognostics and thresholds significantly impact maintenance decisions. (iii) The analysis of the impacts of confidence levels in chance constraints provides decision-makers with the flexibility to adjust maintenance planning under different levels of uncertainty.

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.

Deep Learning–Based Autonomous Road Condition Assessment Leveraging Inexpensive RGB and Depth Sensors and Heterogeneous Data Fusion: Pothole Detection and Quantification

Poor condition of roads is a major factor for traffic accidents and damage to vehicles. A significant portion of car accidents is attributed to severe three-dimensional (3D) pavement distresses such as potholes, ruttings, and ravelings. Insufficient road condition assessment is responsible for the poor condition of roads. To inspect the condition of the pavement surfaces more frequently and efficiently, an inexpensive data acquisition system was developed that consists of a consumer-grade RGB-D sensor and an edge computing device that can be mounted on vehicles and collect data while driving vehicles. The RGB-D sensor is used for collecting two-dimensional (2D) color images and corresponding 3D depth data, and the lightweight edge computing device is used to control the RGB-D sensor and store the collected data. An RGB-D pavement surface data set is generated. Furthermore, encoder-decoder deep convolutional neural networks (DCNNs) consisting of one or two encoders, and one decoder trained on heterogeneous RGB-D pavement surface data are used for pothole segmentation. Comprehensive experiments using different depth encoding techniques and data fusion methods including data- and feature-level fusion were performed to investigate the efficacy of defect detection using DCNNs. Experimental results demonstrate that the feature-level RGB-D data fusion based on the surface normal encoding of depth data outperform other approaches in terms of segmentation accuracy, where the mean intersection over union (IoU) over 10-fold cross-validation of 0.82 is achieved that shows a 7.7% improvement compared with a network trained only on RGB data. In addition, this study explores the efficacy of indirectly using depth information for pothole detection when depth data are not available. Additionally, the semantic segmentation results were utilized to quantify the severity level of the potholes assisting in maintenance decision-making. The result from these comprehensive experiments using an RGB-D pavement surface data set gathered through the proposed data acquisition system is a stepping stone for opportunistic data collection and processing through crowdsourcing and Internet of Things in future smart cities for effective road assessment. Finally, suggestions about the improvement of the proposed system are discussed.