Maintenance Decision Support System Research Articles

Predictive digital twins for autonomous surface vessels

This paper presents an approach to improve the reliability and efficiency of autonomous surface vessels (ASV) by leveraging predictive digital twin and state estimation techniques. The predictive digital twin is based on a graphical model that shows the relationship between variables, and it is implemented and tested in an ASV based on a nonlinear discrete-time model and an adaptive exogenous Kalman filter to estimate the states and parameters associated with faults in the system. The state estimation method consists of a cascade of a nonlinear adaptive observer and a linearized adaptive Kalman filter, and the predictive algorithm is developed by creating a discrete-time state-space model from measured data and using the validated model to forecast the system response for a future time span. Experimental results in the Otter ASV developed by Maritime Robotics demonstrate the potential of the predictive digital twin as a decision support system for the predictive maintenance of ASVs.

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.

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.

Decision Support System for Predictive Maintenance

Predictive maintenance (PdM) is having vital importance in achieving optimum business objectives in industrial processes. Adapting to appropriate maintenance system is the necessity of today’s corporate world in view of cutthroat competitions for maximizing profits. A decision support system (DSS) added with PdM activities helps achieve organizational goals in structured manner. Bayesian Networks (BN) and Influence Diagrams (ID) have got unique features to be used in DSS. The present work uses these tools to develop a DSS for PdM of industrial equipment.

Integrating the Digital Twin of Decision Support Systems in Aeronautics

A Decision Support System (DSS) is a computerized system that helps users make decisions. In the field of air transport, RAL has developed DSSs to support decision making in various settings, including surface transportation and national security. The purpose of this chapter is to explore the maintenance hypothesis of conditional status checking and propose supporting concepts, such as enhanced care and proactive maintenance. These concepts are further enhanced by robust validation and strategies to improve the effectiveness of care in an extraordinary way. Therefore, a decision support system framework is necessary to build today's flying maintenance framework. Maintenance Decision Support Systems (MDSS) offer a valuable tool for aeronautical support, enhancing maintenance efficiency and cost control. However, further research is required to delve into the associated topics of flying MDSS. Due to the diverse configurations and intricate structures of modern aircraft, a significant number of aircraft parameters need to be evaluated. To facilitate further research, it is essential to establish systematic procedures for compiling a valuable database and define research criteria to assess the sufficiency of existing information. Considering the cost issues associated with continuous flight position information, determining the criteria for data download should guide further research.

Decision Support System for Industry Machine Maintenance Using Weight Product (WP) Method

Machine maintenance management is one of the important aspects in every industry. Implementation of machine maintenance management requires the management of various types of data such as machine age, machine condition and so on. Data management with the help of a decision support system will help improve the effectiveness and efficiency of machine maintenance management. In making the right and correct decisions, it is necessary to have decision support. Without machine maintenance management, it can harm the industry because the machine maintenance schedule cannot be determined optimally, and the costs incurred by repair, maintenance and production cessation activities cannot be determined directly. Therefore, the industry needs a decision support system that is able to improve the efficiency of its machine maintenance management. This research focuses on the design and development of a machine maintenance decision support system application using the weighted product (WP) method. Weighted product (WP) method is one of the multi criteria decision making methods used to determine the final decision by using several criteria. This method can provide a reliable methodology that helps to organize the most optimized result in the presence of many different opportunities, parameters, and constraints. Research method used in this study is the Web Engineering Method. This system is developed by using PHP programming language and MySQL as a database. This system is expected to increase the effectiveness and efficiency of machine maintenance management.

Data driven-based model for predicting pump failures in the oil and gas industry

Equipment reliability is a critical aspect of the oil and gas industry because the failure of such equipment can lead to catastrophic outcomes on different levels. In these industries, the need for safe, accurate, and cost-effective equipment operation requires reliable failure estimation and identification in order to improve performance and ensure that the maintenance strategy is efficient and effective. Thus, failure prediction is vital in the oil and gas industries for extending part lifetime and reducing unexpected equipment failures, thereby avoiding costly plant shutdowns and equipment damage. This paper proposes a simple and easy-to-implement data-driven model based on multiple linear regression for predicting the failures of a seawater pump in the oil and gas industry. Real-world historical data from equipment and process sensors mounted on the selected pump is collected and analyzed to develop the proposed model. The factors used to build the proposed model have been identified through a review of the literature and expert interviews. In addition, the correlation between the input factors is investigated. The results revealed that the proposed model has the capability to predict pump failure with more than 97% accuracy. Furthermore, sensitivity analysis revealed that temperature and pressure are the most important factors influencing pump failure. The findings of the proposed model can assist maintenance managers and decision-makers in properly detecting and predicting failure. It also helps to monitor and control the input factor levels in order to prevent failures. This study contributes to the development of failure prediction alerts, which will serve as a maintenance decision support system for operatives to avoid potential incoming failures.

Multi-objective decision support system for large-scale network pavement maintenance and rehabilitation management to enhance sustainability

Process of sustainable pavement maintenance and rehabilitation (M&R) management can be conducted using various technological and organizational systems. Such systems must meet the implementation conditions of a given project involving pavement M&R, and trade-offs between economic, environmental, and social impacts from various aspects involved. Hence, this study examines a systematic framework for large-scale pavement network decision-making considering sustainability dimensions, constructs a multi-stage decision model considering different modes and options using attribute mapping techniques, and develops a decision support system that (1) determines comprehensive sustainability drivers and quantification methods in the field of pavement management to evaluate the sustainability characteristics of predetermined M&R strategies; and (2) allows the execution of decision space dimensionality reduction process for any given network scales and M&R techniques dimensions considering spatial-temporal distribution, then generate a specific set of sustainable budget allocation and M&R strategy combination scenarios using a redesigned multi-objective optimization model, which can coordinate segment-level and network-level management. A case study was conducted to demonstrate the application of the proposed system. Results show that the approach can significantly reduce the computational cost compared to the scenario without dimensionality reduction, and also facilitates the improvement of agency costs, environmental impacts, as well as user costs from the life cycle perspective. Furthermore, the balance of M&R budget allocation depends on the mapped attributes selected in the dimensionality reduction process, the sustainability and reliability of the decision results can be improved by selecting as many attributes as possible while satisfying the pavement management system. The study provides an exciting opportunity to support decision-making for large-scale transportation infrastructure management.

From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0

The Internet of Things (IoT), Big Data and Machine Learning (ML) may represent the foundations for implementing the concept of intelligent production, smart products, services, and predictive maintenance (PdM). The majority of the state-of-the-art ML approaches for PdM use different condition monitoring data (e.g. vibrations, currents, temperature, etc.) and run to failure data for predicting the Remaining Useful Lifetime of components. However, the annotation of the component wear is not always easily identifiable, thus leading to the open issue of obtaining quality labeled data and interpreting it. This paper aims to introduce and test a Decision Support System (DSS) for solving a PdM task by overcoming the above-mentioned challenge while focusing on a real industrial use case, which includes advanced processing and measuring machines. In particular, the proposed DSS is comprised of the following cornerstones: data collection, feature extraction, predictive model, cloud storage, and data analysis. Differently from the related literature, our novel approach is based on a feature extraction strategy and ML prediction model powered by specific topics collected on the lower and upper levels of the production system. Compared with respect to other state-of-the-art ML models, the experimental results demonstrated how our approach is the best trade-off between predictive performance (MAE: 0.089, MSE: 0.018, R^{2}: 0.868), computation effort (average latency of 2.353 s for learning from 400 new samples), and interpretability for the prediction of processing quality. These peculiarities, together with the integration of our ML approach into the proposed cloud-based architecture, allow the optimization of the machining quality processes by directly supporting the maintainer/operator. These advantages may impact to the optimization of maintenance schedules and to get real-time warnings about operational risks by enabling manufacturers to reduce service costs by maximizing uptime and improving productivity.

Towards Group Decision Support in the Software Maintenance Process

Software maintenance is an element-key of the life cycle of software. However,the techniques of software maintenance do not consider the diversity and the complexity of decisions which do not stop increasing. So, there are at present a few tools, susceptible to insure the relevance and the efficiency of the decision-making in this phase. The work presented in this paper aims to eliminate or at least to reduce the effect to fall in an expensive change by reducing the time to find a compromise on the adequate change. The development of the decision support system for software maintenance is an answer to the problem. The developed tool allows:-to make a fast diagnosis on the software by using the coupling metrics;-to help the decision-makers of the maintenance, according to their preferences often conflicting, to adopt a change among several proposed. To answer this group decision where various points of view are considered, we propose a negotiation protocol. This protocol try to find a compromise that suits best all the decision-makers.

Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study

Recent increased enthusiasm towards Industrial Artificial Intelligence (IAI) coupled with advancements in smart sensor technologies have resulted in simultaneous incorporation of several Condition Monitoring (CM) technologies within manufacturing/industrial sectors. Smart utilization of CM data leads to enhanced safety, reliability and availability of manufacturing systems. Conventional system monitoring techniques, however, cannot efficiently cope with such rich CM information content. In this regard, the paper proposes a novel hybrid Maintenance Decision Support System (MDSS) for fault diagnostic and prognostics considering event-triggered CM data. The proposed MDSS is a hybrid framework designed by coupling Machine Learning (ML)-based models and statistical techniques. More specifically, the MDSS is a time-dependent Proportional Hazard Model (PHM) augmented with semi-supervised ML approaches and Reinforcement Learning (RL) to find an optimal maintenance policy for systems subject to stochastic degradations with focus on cost minimization. The developed hybrid model is capable of inferring and fusing high-volume CM data sources in an adaptive and autonomous fashion to recommend optimal maintenance decisions without human intervention, which is a step-forward contribution in the maintenance context. To evaluate the structure and performance of the proposed model, comprehensive ML-based solutions are developed based on a dataset provided by NASA containing run-to-failure and CM data associated with aircraft engines.

Operation and Maintenance Decision Support System for Photovoltaic Systems

Operation and maintenance (O&M) and monitoring strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to faults. An O&M decision support system (DSS) was developed in this work for providing recommendations of actionable decisions to resolve fault and performance loss events. The proposed DSS operates entirely on raw field measurements and incorporates technical asset and financial management features. Historical measurements from a large-scale PV system installed in Greece were used for the benchmarking procedure. The results demonstrated the financial benefits of performing mitigation actions in case of near zero power production incidents. Stochastic simulations that consider component malfunctions and failures exhibited a net economic gain of approximately 4.17 €/kW/year when performing O&M actions. For an electricity price of 59.98 €/MWh, a minimum of 8.4% energy loss per year is required for offsetting the annualized O&M cost value of 7.45 €/kW/year calculated by the SunSpec/National Renewable Energy Laboratory (NREL) PV O&M Cost Model.

Application of machine learning and rough set theory in lean maintenance decision support system development

Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.

A Web-Based Decision Support System for Aircraft Dispatch and Maintenance

Aircraft dispatch involves determining the optimal dispatch option when an aircraft experiences an unexpected failure. Currently, maintenance technicians at the apron have limited access to support information and finding the right information in extensive maintenance manuals is a time-consuming task, often leading to technically induced delays. This paper introduces a novel web-based prototype decision support system to aid technicians during aircraft dispatch decision-making and subsequent maintenance execution. A system architecture for real-time dispatch decision support is established and implemented. The developed system is evaluated through a case study in an operational environment by licensed maintenance technicians. The system fully automates information retrieval from multiple data sources, performs alternative identification and evaluation for a given fault message, and provides the technician with on-site access to relevant information, including the related maintenance tasks. The case study indicates a potential time saving of up to 98% per dispatch decision. Moreover, it enables digitalization of the—currently mostly paper-based—dispatch decision process, thereby reducing logistics and paper waste. The prototype is the first to provide operational decision support in the aircraft maintenance domain and addresses the lack of correlation between theory and practice often found in decision support systems research by providing a representative case study. The developed custom parser for SGML-based documents enables efficient identification and extraction of relevant information, vastly contributing to the overall reduction of the decision time.

Condition and criticality-based predictive maintenance prioritisation for networks of bridges

Numerous bridges are exposed to increasingly frequent and intense extreme events due to climate change, while serving more traffic than originally designed due to population growth. Thus, predictive maintenance of bridges is of paramount importance for securing structural safety and bridge network reliability. However, the application of predictive maintenance for networks of bridges, considering condition and criticality of bridges within the network, has not seen much attention in practice and literature. Presented herein is a maintenance prioritisation method for networks of bridges, based on: a deterioration model for individual components considering uncertainty; life-cycle cost analysis; grouping of components maintenance to reduce traffic interruption and setup cost; and criticality evaluation of bridges using a specifically tailored version of closeness vitality and traffic simulation. This method has been applied to a network of 21 bridges in Portugal, composed of several heterogeneous elements. It showed a substantial decrease in maintenance cost, compared to the Structures Investment Toolkit, and significant differences between criticality of bridges within a network. The proposed methodology can be applied to any networks of bridges or serve as the basis for updated maintenance decision support systems for infrastructure asset networks.

Making the Sourcing Decision of Software Maintenance and Information Technology

Outsourcing has been getting a significant growth for the last few years. Organizations tend to outsource Information Technology (IT), primarily to take advantage of the availability of qualified, trained and skilled workforce in low cost countries across the globe. Outsourcing of IT and software maintenance seem very promising, but a number of factors, risks, and challenges associated with the outsourcing process that make the sourcing decision very complicated. The present study aimed at gaining in-depth understanding of the three aspects of outsourcing, namely; perceived benefits of IT outsourcing, influencing factors of IT outsourcing and software maintenance offshoring. The findings of the current study will lead us to develop a sourcing framework for outsourcing decision as well as a decision support system for software maintenance. A systematic literature review is performed that presents perceived benefits of IT outsourcing, the influencing factors of IT outsourcing and software maintenance. Furthermore, the identified factors are analyzed based on their occurrences in literature as well as chi square test is performed to derive the significant differences amongst the factors based on decades. Similarly, critical success factors are derived both for IT outsourcing and software maintenance offshoring. Our article shows that how the critical success factors impact the IT as well the software maintenance in global delivery perspective. The findings of the current study will help the IT experts and decision makers in making suitable sourcing decisions.

A data mining approach for lubricant-based fault diagnosis

PurposeThe purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers select an appropriate classifier given a specific lubricant data set.Design/methodology/approachThe DSS embeds a framework integrating cluster and principal component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB), random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is developed in conjunction with practitioners for comparing the classifier models.FindingsThe results show the importance of embedded knowledge, explored via a knowledge discovery approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly, the proposed framework is demonstrated as plausible for decision support due to its high accuracy and consideration of practitioners needs.Practical implicationsThe proposed framework will potentially assist maintenance managers in accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors.Originality/valueAdvances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in practice, however, may be incorporated in the information management systems offering high predictive accuracy. The classification models' comparison approach, will inevitably assist the industry in selecting amongst divergent models' for DSS.

Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry

The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms—the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)—are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures.

A machine learning methodology to predict alerts and maintenance interventions in roads

This contribution is about predicting maintenance alerts in roads and selecting the most appropriate type of interventions recommended for preventing the occurrence of future failures. The objective is aligned with that covered by pavement maintenance decision support systems (PMDSS), though the methodology presented can be applied to other non-pavement road linear assets. The purpose is to summarise the main findings in the development of an approach based on testing the four most extended machine learning techniques (ML), namely Decision Trees (DT), K-Nearest Neighbourhood (KNN), Support Vector Machines (SVM) and Artificial Neural Networks (ANN), using data from the historical inventory of inspections and maintenance interventions of a case study to illustrate the potential that such approach can offer to road maintenance managers. The correlation process embodies supervised and unsupervised training of models. The maintenance predictions are presented and compared over various segments corresponding to the real maintenance interventions conducted on an existing road network of a geographical zone.

Automated wind turbine maintenance scheduling

While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses (i.e. doing maintenance when there is least wind). For the DSS, routine maintenance tasks are grouped using the findings of an agglomerative nesting analysis. Then, the task execution windows are searched within pre-planned maintenance day.