Maintenance Scenarios Research Articles

Application of Degradation and Optimization Models for digitization of Maintenance Management in Railway Infrastructures

This study presents an innovative methodology to improve the maintenance of railway infrastructures, integrating degradation models and economic optimization. The combination of a degradation model and a semi-Markovian model supported by the Weibull function, calculates the economically optimal degradation, to release the preventive intervention. The development of digital twins of assets and infrastructures makes possible the integration of online data and the incorporation of models that help the maintenance manager in decision-making. Organizations must determine the information to be collected from the assets. Transform the information into inputs for the models and, after their application, would allow them to identify the most economically favorable global maintenance scenarios. This work evolves the decision support on preventive maintenance implementation in the railway sector. From a decision table, used by railway managers, with three levels of risk established subjectively on an experimental basis, it evolves to the objective methodology, designed to determine the optimal degradation. The methodology can be applied to assets by maintenance managers but can also be incorporated into the digital twins. The case study illustrates the practical application of this methodology to the degradation of railway track sections. The research emphasizes the importance of data-driven decision-making for economic maintenance management. However, other priorities such as transportation safety and efficiency could complement the economic one.

Optimizing Network Reliability in Dynamic or Rapidly-Changing Topologies

With communication, power and transportation networks facing ever-greater dynamics, optimizing reliability poses significant modeling and computational challenges. Most existing reliability optimization research assumes static topologies with predefined failure probabilities and connectivity demands. This work puts forth a time-varying mathematical program to maximize expected network reliability under shifting topological uncertainties. Sets, parameters, decision variables and constraints are defined as functions of time to capture variability in links, capacities, risks and demands. The formulation adapts to detected changes through re-optimization triggered by model error thresholds. Approximation techniques based on constraint sampling and decomposition address solve efficiency for large fluid networks. Evaluations on simulated dynamic test cases demonstrate superior reliability versus periodic and static optimization approaches while fulfilling budget limits. Accuracy metrics assess model fidelity over increasing volatility levels. Implementation case studies exhibit optimized resilience in software-defined communication architectures, smart grid reconfiguration, and adaptive transportation maintenance scenarios. The mathematical programming foundation provides a pathway to achieve connectivity resilience for critical infrastructure networks facing intensifying dynamics. The integration of optimization, prediction and adaptive response provides a paradigm for decision making under modern uncertain conditions.

Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry

The well-known methods of scene extraction on video are focused on analyzing the similarity between frames. However, they do not all analyze the composition of the image scene, which may remain the same during maintenance. Therefore, this paper proposes an algorithm for equipment maintenance scene detection based on human hand tracking. It is based on the assumption that, when servicing technological equipment, it is possible to determine the change in repair action by the position of the service engineer’s hands. Thus, certain information and the algorithm that processes these changes allow us to segment the video into actions performed during the service. We process the time series obtained by moving the hand position using spectral singular value decomposition for multivariate time series. To verify the algorithm, we performed maintenance on the control cabinet of a mining conveyor and recorded the work on a first-person video, which was processed using the developed method. As a result, we obtained some scenes corresponding to opening the control cabinet, de-energizing the unit, and checking the contacts with a multimeter buzzer test. A third-person video of motor service was similarly processed. The algorithm demonstrated the results in separate scenes of removing screws, working with a multimeter, and disconnecting and replacing motor parts.

Dynamic Optimization Method of Knowledge Graph Entity Relations for Smart Maintenance of Cantilever Roadheaders

The fault maintenance scenario in coal-mine equipment intelligence is composed of videos, images, signals, and repair process records. Text data are not the primary data that reflect the fault phenomenon, but rather the secondary processing based on operation experience. Focusing on the difficulty of extracting fault knowledge from the limited textual maintenance process records, a forward static full-connected topology network modeling method based on domain knowledge from four dimensions of physical structure, internal association, condition monitoring, and fault maintenance, is proposed to increase the efficiency of constructing a fault-maintenance knowledge graph. Accurately identifying the intrinsic correlation between the equipment anomalies and the faults’ causes through only domain knowledge and loosely coupled data is difficult. Based on the static full-connected knowledge graph of the cantilever roadheader, the information entropy and density-based DBSCAN clustering algorithm is used to process and analyze many condition-monitoring historical datasets to optimize the entity relationships between the fault phenomena and causes. The improved DBSCAN algorithm consists of three stages: firstly, extracting entity data related to fault information from the static fully connected graph; secondly, calculating the information entropy based on the real dataset describing the fault information and the historical operating condition, respectively; and thirdly, comparing the entropy values of the entities and analyzing the intrinsic relationship between the fault phenomenon, the operating condition data, and the fault causes. Based on the static full-connected topology storage in the Neo4j database, the information entropy and density-based DBSCAN algorithm is computed by using Python to identify the relationship weights and dynamically display optimized knowledge graph topology. Finally, an example of EBZ200-type cantilever roadheader for smart maintenance is studied to analyze and evaluate the forward and four-mainlines knowledge graph modeling method and the dynamic entity relations optimization method for static full-connected knowledge graph.

Integrated optimization and engineering application for disassembly line balancing problem with preventive maintenance

The current research on the disassembly line balancing (DLB) problem has not yet considered the preventive maintenance of workstation equipment. The lack of preventive maintenance can easily lead to sudden failure of the disassembly line equipment, which will affect the disassembly efficiency. Preventive maintenance is planned maintenance, which can effectively reduce the probability of equipment failure in actual disassembly line engineering applications. This study integrates preventive maintenance into the DLB. The objectives focus on optimizing both cycle times under the regular DLB scenario and the preventive maintenance DLB scenario, and the number of task adjustments between the two scenarios. A mixed integer linear programming model integrating preventive maintenance is established. Then an improved genetic simulated annealing (IGSA) algorithm based on the disassembly characteristics is designed. The exact solver and the proposed IGSA are used to solve a small-scale case simultaneously to verify the correctness of the model and algorithm. The performance of the proposed IGSA is verified by comparing the solution results of 21 benchmarks. Finally, the proposed model and algorithm are applied to a microwave disassembly line as the engineering application case. The results show that the integrated optimization can ensure the disassembly efficiency of the regular disassembly scenario, and can fully utilize workstations of unperformed maintenance during the preventive maintenance scenario. This can also effectively improve workstation utilization and reduce time costs.

A Study on Quantities Driving Maintenance, Repair, and Overhaul for Hybrid-Electric Aeroengines

Abstract Hybrid-electric propulsion for commercial aircraft is currently a key industry interest. Consequently, publications on its design and performance estimation are manifold. However, models addressing characteristics of maintenance, repair, and overhaul (MRO) are virtually unavailable—even though direct maintenance costs (DMC) represent a significant part of direct operating costs (DOC) in commercial aviation. Detailed analysis of hybrid-electric aircraft propulsion degradation and maintenance scenarios must integrate both methods of sizing and design as well as operational factors for conventional and electric subsystems, as operator-specific utilization strongly influences MRO. Accordingly, a holistic engine analysis model is currently being developed using the example of an Airbus A320 aircraft, taking into account flight mission, engine performance, degradation, and MRO. This paper presents an implementation of hybridization into the gas turbine thermodynamic cycle calculation for parallel hybrid-electric (PHE) engine architectures with 2 and 5 MW electric motors, and the approach necessary for resizing hybridized gas turbine components. Turbomachinery loading throughout representative short-haul missions is analyzed for conventional and hybrid-electric configurations based on the V2500 high-bypass turbofan engine, whereby unknown or uncertain boundary conditions are considered in a probabilistic sensitivity study. As a result, MRO-driving quantities such as engine performance parameters, atmospheric conditions, and ingested aerosols can be compared. The findings suggest that DMC related to the gas turbine may be considerably lowered through hybridization, as it allows for reduced peak temperatures and more uniform gas turbine operation. However, these gains are at least partially offset by additional components' DMC. For electrical machines, bearings and the stator winding insulation are life-limiting, where the latter becomes increasingly dominant for higher power densities associated with high current densities and copper losses. Thermo-mechanical stresses are considered as driving mechanisms in power electronic systems degradation. Consequently, powerful lightweight machines must be balanced against tolerable thermal and electrical loads to achieve suitable service life.

Benefits of Non-Destructive Evaluation and Probabilistic Damage Tolerance Cosimulation for Fracture Risk Reduction

In the context of the damage tolerance approach used to drive aircraft maintenance operations, it is essential to demonstrate the reliability of NDE inspections in detecting structural damage especially for additively manufactured components since this process produces components that may introduce material anomalies at any location within a component. The Probability Of Detection curve (POD) that links the probability to detect a detrimental flaw to its size is generally used as a key indicator for that purpose by giving the maximum flaw size that a NDE process can miss with a given level of probability and confidence. This information can then be used along with other inputs such as component geometries, mechanical properties, constraints, service and residual stresses and damage evolution speed to assess fracture risk and then adapt maintenance scenarios to optimize safety and component service life. NDE reliability and risk assessment are based on statistical indicators that need a large amount of data to provide reliable metrics. It is difficult to obtain such indicators using a purely empirical approach that is based on physical trials and measurements as it may involve many mock-ups and costly processes. Simulation tools can achieve this goal via their ability to include and precisely monitor many parameters as well as high computing capacities that are now available. The work presented in this paper involved cosimulations performed between the DARWIN® probabilistic damage tolerance software and the CIVA NDE simulation software. DARWIN computes fracture risk levels throughout a component, while CIVA can efficiently provide Probability Of Detection curves at different locations in a component and for various NDE methods. The presented application deals with a titanium gas turbine engine impeller disk and involves an Ultrasonic NDE inspection technique. It appears to be a very interesting approach to connect together NDE and Fracture mechanics simulations, two disciplines that generally work on their own. Indeed, DARWIN helps the user to determine detrimental flaw types, locations and sizes which are key inputs to develop an effective NDE inspection method. CIVA can provide location-specific POD curves that enable DARWIN to quantity the effect of dedicated NDE on potential risk reduction. This highlights the importance of NDE simulation for safety and helps to identify potential changes in the physical NDE process and the maintenance cycle that provide the best compromise between detection performance and cost.

Remote maintenance scenario of the caesium ovens and beam source of the ITER heating neutral beam

Maintenance of the Heating Neutral Beam (HNB) injectors that are located in the ITER Neutral Beam (NB) cell needs Remote Handling (RH) operations because the NB cell is a radiation controlled area. This paper describes the remote maintenance sequences of the caesium ovens and the Beam Source (BS) of the ITER HNB by using the Neutral Beam Remote Handling System (NBRHS). Caesium oven maintenance is a regular maintenance that needs to be performed every Long-Term Maintenance (LTM) period as caesium is a consumable to produce caesium vapor to enhance negative ion production. The BS maintenance is a corrective maintenance to do if it fails during the lifetime of ITER. Maintenance of the caesium ovens and the BS involves many RH operations that require collaboration of the NBRHS subsystems and RH tools. Moreover, the maintenance operation needs to be done without spreading contamination from the HNB vessel to the NB cell. The developed maintenance scenario relies on the current NB cell environment and the existing interfaces in the plant system.

Design and research of bilateral teleoperation for CFETR tile assembly using haptic feedback

Bilateral teleoperation is a control strategy used in remote handling (RH) to enable an operator to control a robot or system while perceiving the interactive force in the environment. To enhance the immersive operating experience by reducing time delays, a control system based on OROCOS and DDS has been presented to rapidly establish the bilateral teleoperation system with real-time performance. Additionally, a bilateral control algorithm combining closed-loop pose tracking and force feedback is proposed to improve the pose tracking accuracy of the slave robot and enhance the sense of telepresence. As a part of the pre-study on the tile assembly conducted by CMOR in Chinese Fusion Engineering Test Reactor (CFETR), a lightweight experimental setup has been constructed based on the maintenance scenario in EAST. Relative experiments have been conducted to validate the accuracy and effectiveness of the bilateral teleoperation system. The results demonstrate low latency and smooth control during the bilateral teleoperation process.

Multi-objective fuzzy partial disassembly line balancing considering preventive maintenance scenarios using enhanced hybrid artificial bee colony algorithm

In the existing researches about partial disassembly line balancing problem (PDLBP), all workstations are assumed always be available. In the real world, however, aging and failure of machines are inevitable, which may result in the shutdown of the whole disassembly line. To solve this problem, this study introduces preventive maintenance scenarios into PDLBP to prevent unexpected breakdowns and promote the productivity and smoothness of the disassembly line. Then, considering the substantial level of uncertainty in the state of end-of-life products, a multi-objective fuzzy mathematical model is established to minimize the cycle time, disassembly profit, and total assignment plan alteration simultaneously. And an enhanced hybrid artificial bee colony algorithm is developed to solve it. The attribute-driven heuristic strategy is applied to improve the quality of initial food sources, and the adaptive neighborhood search is designed to improve the exploitation efficiency of the employed bees and onlooker bees. Additionally, a hybrid global learning strategy is developed to guide the scout bees to raise the diversity of food sources. Finally, three case studies are conducted to validate the proposed algorithm. The results indicate that the proposed algorithm can achieve superior performance.

Artificial intelligence and perceived effort in relationship maintenance: Effects on relationship satisfaction and uncertainty

Maintaining satisfying close relationships is important for individuals’ well-being. In the digital age, artificial intelligence (AI) has growing applications for relationship maintenance and thus implications for relational well-being. We hypothesize that although using AI to help with relational maintenance may reduce an individual’s effort, their partner may perceive AI-augmented activities negatively. According to the investment model and equity theory, perceptions of diminished effort in a relationship may lead to less satisfaction and greater uncertainty about the partner’s involvement in the relationship. In an online experiment, we presented participants ( N = 208) with hypothetical scenarios of relational maintenance initiated by a fictional close friend, with a 3 (agency: self-without-augmentation vs. AI-augmented vs. human-augmented) × 3 (relational task: support-giving vs. advice-giving vs. birthday celebration) between-subjects design. Compared to the self-without-augmentation condition (i.e., the control condition) where the friend completed a relational task with no external aid, using AI assistance led participants to perceive the friend expended less effort, reducing participants’ relationship satisfaction and increasing uncertainty. Getting help from another person was not significantly different from using AI in terms of perceived partner effort, relationship satisfaction, uncertainty, and perceived appropriateness. We discuss the implications of the findings for relational maintenance and technology-mediated communication.

Developing a Comprehensive Quality Control Framework for Roadway Bridge Management: A Case Study Approach Using Key Performance Indicators

Transportation infrastructures, especially roadway bridges, play a pivotal role in socioeconomic development. Recently, bridge engineers are increasingly facing the challenge in terms of shifting their strategy from building new facilities to maintaining the existing aging infrastructures, to preserve their service performance during the operational stage. In fact, the infrastructure administrators lack a quality control (QC) strategy for the existing roadway bridges, which leads to the decision-making application and tool being still minor. To overcome those challenging issues, this paper proposes a quality control framework for roadway bridge management using key performance indicators (KPIs). The case study methodology is suggested to be used and then conducted for several bridges, mostly in European countries. In which the performance indicators (PIs) and goals (PGs) are defined, after assessing the bridges and vulnerable zones, the derivation KPIs from those PIs are introduced and developed considering time functions and different maintenance scenarios. Eventually, a two-stage quality control framework will be proposed in which the static stage includes preparatory works, inspection responsibilities, and a quick assessment of KPIs; while the dynamic stage helps the decision maker in estimating the time remaining of the bridge service life, managing the evolution of KPIs as well as planning the best possible maintenance strategy. The selected two case studies are present and curated, which show the excellent potential to develop a long-term strategy for roadway bridge management on a lifecycle level.

Towards AI-assisted digital twins for smart railways: preliminary guideline and reference architecture

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.

Knowledge graph based AR content adaptive generation for 5G communication network maintenance services

5G technology puts forward new requirements for communication network human maintenance services. However, with the complexity of 5G maintenance knowledge and the diversification of maintenance scenarios, the ability of maintenance engineer cannot be greatly improved in a short time. This paper proposed a knowledge graph (KG) based augmented reality (AR) content adaptive generation method for 5G communication network maintenance services. The KG and AR technology are used to reduce the cognitive burden of manual maintenance and improve the efficiency of communication network maintenance.

4G Radio Access Network Simulator for Lab as a Service: Operation, Administration and Maintenance Scenarios in Indonesia

4G Radio Access Network Simulator for Lab as a Service: Operation, Administration and Maintenance Scenarios in Indonesia

Using modern virtual reality techniques to perform analysis of ITER ECH EL port cell maintenance

In order to maintain an ITER Port Plug after failure, the Port Cell needs to be cleared to allow for a transport cask to access the Port Plug and transfer it for maintenance to the Hot Cell Complex. The Port Cell clearing process is a complex, manual and machine assisted procedure, taking out the equipment blocking cask access in several stages.For the Equatorial Launcher (EL) of the Electron Cyclotron Heating (ECH) system, which is located in Equatorial Port Cell #14 of the ITER tokamak, the Port Cell clearing operation includes the removal of 24 Ex-vessel Waveguides, a complex set of CCWS (component cooling) and PHTS (primary cooling) lines, several manifolds, and other ancillaries, like the Service Vacuum System (SVS) to monitor the torus vacuum tightness.This paper shows how modern Virtual Reality (VR) simulation techniques, in combination with a state of the art, high-end game-PC environment and Head Mounted Display, can be used as a tool to visualize and analyse the maintenance procedures, to predict the time to repair, and to assist in minimizing radiation exposure to take in to consideration the ALARA principles.Special emphasis is put on the verification and validation of manual and machine-assisted maintenance procedures and the identification of tooling requirements. The possibility to simulate logistics and repair actions in an early stage of the design process allows for the identification of those maintenance actions that require dedicated tests or the development of dedicated tools.The added value of using modern VR techniques to analyse the maintenance scenarios are:•Immersive & intuitive – very little training required to experience the process first-hand•Quick and accurate assessment of maintenance scenarios, based on the actual CATIA models•The ability to assess in detail geometrically very complex maintenance sequencing•Less need for expensive hardware mock ups

Performance degradation of air source heat pumps under faulty conditions

The ongoing European regulations towards the decarbonization of energy intensive sectors such as heating and cooling will lead to an increase of heat pump appliances. Apart from anomalies that can be detected in a preliminary phase after the start-up, soft faults such as refrigerant leakage and heat exchanger fouling may cause a performance degradation in such systems that evolves in time and that must be detected before it becomes too big. From this perspective, in a previous paper (Pelella et al. [1]) it has been demonstrated that, in cooling mode, a seasonal performance penalization up to 50% can be reached in case of a not-planned maintenance scenario, whereas wherever a timed or intelligent maintenance strategy is implemented based on data-monitoring, this impact can be significantly reduced. Therefore, in order to extend the analysis previously carried out to the heating cases, and to evaluate the consequent energetic degradation of the system in case of soft faults occurring, depending on different climate conditions and maintenance scenarios, this paper develops a physic-based model to simulate system components and to describe the fault phenomenology on a residential 2.6 kW air-to-air heat pump, operating in winter mode. The results show that in heating mode a 40% condenser fouling and a 30% refrigerant leakage cause a performance degradation of respectively 16% and 12%, whereas in case of evaporator fouling the performance penalization is only of 3.2%. Moreover, the performance degradation is enhanced by the overlapping effect of simultaneous faults. Finally, from seasonal simulations of the heat pump along an entire machine lifetime of 12 years, it is found that none of the maintenance strategies analysed is able to significantly reduce the number of scenarios penalized by faults, opening to the potential development of systems for the automatic fault detection, diagnosis, and evaluation (FDDE).

Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

To effectively manage the heterogeneous and discrete knowledge of the bridge maintenance domain, this study adopts knowledge graph technology to build a knowledge base of bridge maintenance, called the bridge maintenance knowledge graph (BMKG). The BMKG uses an ontology as the knowledge organization and representation framework and a graph database as the knowledge storage tool. To facilitate the construction of the BMKG, a hybrid method combining a top-down approach and a bottom-up approach is proposed. Firstly, a bridge maintenance domain ontology (BMDO) is coded with Protégé and represented in Web Ontology Language. Secondly, rule reasoning and ontology reasoning are implemented on the BMDO in Protégé in order to automatically complete missing relations or attribute values. Thirdly, ontology reasoning is adopted to perform consistency check on the BMDO. Lastly, the BMDO model is stored in the Neo4j graph database through data format conversion, thus completing the construction of the BMKG. The BMKG is applied in a typical scenario of bridge maintenance to demonstrate its application value. Results show that the proposed hybrid method can create a knowledge graph that can realize the transformation from discrete data into interconnected knowledge. Knowledge graph offers a novel idea to create a knowledge base in the bridge maintenance domain.

Effect of primary suspension and friction wedge maintenance parameters on safety and wear of heavy-haul rail vehicles

Wheel wear is of major interest in railways due to the high cost of wheel reshaping, rail wear, and safety issues in operation. Recent researches employ onboard sensors to evaluate the dynamic behavior of the wagon and determine railway running conditions. However, sensors and computing units are expensive and some maintenance scenarios are impossible to be deliberately generated. In this work, several maintenance parameters for primary suspension, friction wedge, and flange backspacing are evaluated within operational ranges in a Box-Behnken design of experiment (DoE), set in a quadratic surface model. Such a modeling approach uses few experimental points and a significantly small computational cost when compared to a full factorial method, which uses multibody dynamic models on a simulating platform, like Simpack, to determine which parameters are more influential in safety against derailment (Y/Q) and wheel and flange wear (Tγ). The present work evaluates such parameters for heavy-haul gondola rail cars, with meter and broad gauge, running on tangent track and curves, with and without FRA irregularities. The analysis makes it possible to identify non-uniformities in wheel wear and to set different inputs for condition-based maintenance for freight rail wagons. Results show that the primary suspension longitudinal clearance and stiffness affect Y/Q and wear by over 100%, increasing the operational risk and maintenance cost because of wheel wear.

Using sensor data to detect time-constraints in ontology evolution

In this paper, we present an architecture for time-constrained ontology evolution comprised of two tools: the J2OIM (JSON to Ontology Instance Mapper), which uses JavaScript Object Notation (JSON) objects to populate an ontology, and TICO (Time Constrained instance-guided Ontology evolution), which analyses streams or batches of instances as they are generated and attempts to identify potential changes to their definitions that may trigger evolutionary processes. These tools help compensate for identified gaps in literature in instance mapping and modular versioning. The case-study for these tools involves a predictive maintenance (PdM) scenario in which near real-time data sensor enriched by contextual data is continuously transformed into ontology individuals that trigger ontology evolution mechanisms. Results show it is possible to use the instance mapping mechanisms in an incremental fashion while assuring no duplicates are generated and the aggregation of similar information from distinct data points into intervals. Furthermore, they show how the ontology evolution processes effectively detect variations in ontology individuals, generating and updating existing concepts and roles.