Maintenance Management System Research Articles

CREATION, MANAGEMENT, AND USE OF A DATABASE FOR THE MAINTENANCE PROCESS OF PRODUCTION EQUIPMENT IN AN INDUSTRIAL COMPANY

The paper presents the creation of a database of motion norms to support the maintenance management system in an industrial production company, implemented by a team from the Faculty of Mechanical Engineering, VSB - Technical University of Ostrava. This database is intended for filling and optimal use of maintenance modules of ERP systems and for use in the processes of preparation of maintenance and service orders. The article focuses on the methodology of creation, characteristics, use and availability of this database and its application in industrial practice.

Methodology for 3D Management of University Faculties Using Integrated GIS and BIM Models: A Case Study

Three-dimensional virtual modeling is one of the tools being rapidly implemented in the construction industry, leading to the need for strategies based on intelligent 3D models of cities and/or digital twins, which allow simulation by interacting with their real physical counterparts, anticipating the outcomes of decision making. In practice, problems arise when creating and managing these twins, as different data, models, technology, and tools must be used, and they cannot all be combined as desired due to certain incompatibilities. On the other hand, today’s traditional building management demands a more optimized process to prevent errors and enable timely reactions to failures and defects. Managing and using a large amount of complex and disparate data are required, which is why the use of CMMS-type software is common (Computerized Maintenance Management System). However, such software is rarely designed for management in a 3D format, often due to the absence of three-dimensional models of the assets. This research aims to contribute to the technological development of the digitalization of the built environment, providing a simple methodology for generating and managing 3D models of cities. To achieve this, the tools and information useful for generating an integrated GIS 3D and BIM model, and for Computer-Aided Maintenance Management in a three-dimensional format (CMMS-3D), are identified. The final model obtained is used to optimize the three-dimensional management of a classroom building on the “Campus de Las Llamas” at the University of Cantabria in Spain. The results demonstrate that it is possible to integrate digital models with simple linking mechanisms between the existing tools, thus achieving an optimal three-dimensional management model.

Multi-Criteria Decision-Making Framework (AHP-TOPSIS): Pavement Preventive Maintenance Case Study for Ordinary National Trunk Highways

Pavement maintenance and rehabilitation decision-making needs to weigh multiple strategic goals to achieve sustainable development through the pavement maintenance management system. Making decisions regarding pavement preventive maintenance is both intricate and costly. This study introduces a multi-criteria decision-making framework aimed at enhancing the scientific basis of such decisions. The framework first establishes an evaluation system for preventive maintenance strategies by considering three primary evaluation criteria—service functionality, pavement performance, and economic benefits, and then identifies nine specific evaluation indicators to influence these criteria, with a comparison matrix constructed to determine the weight of each indicator in relation to the maintenance decision hierarchy. Following this, the technique for order preference by similarity to ideal solution (TOPSIS) is employed to prioritize four commonly utilized preventive maintenance strategies. The results reveal that pavement condition and maintenance costs are the most influential factors in determining the appropriate preventive maintenance strategies for national highways. The priority rankings for the four strategies—slurry seal, micro-surfacing, chip seal, and ultra-thin overlays—are found to be 56.12%, 63.86%, 12.12%, and 83.52%, respectively, with ultra-thin overlays identified as the optimal choice for second-class highways. The decision-making model utilized in this study enables a multi-dimensional analysis, reducing the subjectivity inherent in expert evaluations and facilitating the prompt identification of the most suitable maintenance strategy.

Evidence based management of medical devices: A follow-up experiment

Ensuring the optimal operation and longevity of medical devices is essential for maintaining high safety standards in healthcare. This study presents a significant advancement in the field by enhancing an existing evidence-based maintenance (EBM) framework, which is crucial for the effective management of medical equipment. Building upon previous methodologies, this research introduces a novel, comprehensive taxonomy for categorizing maintenance reports by revising and updating existing failure codes. This refinement addresses gaps in previous models, enabling more precise and effective maintenance strategies.A key innovation of this study is the development of a standard XML protocol, which addresses the current lack of standardization in the field, providing a consistent and unified approach to data management. This standardization is critical for improving communication and data exchange across different healthcare systems and technologies.To validate the enhanced EBM framework, the study applies it to corrective maintenance work orders related to Digital Angiography Systems, Magnetic Resonance Imaging, and Computed Tomography, using data from the Pisa University Hospital’s Computerized Maintenance Management System from 2021 to 2022. This validation demonstrates the reproducibility and adaptability of the framework across different medical equipment, healthcare contexts, and timeframes.The findings represent a significant advancement in healthcare technology management, offering a robust, standardized approach that optimizes the safety, functionality, and longevity of medical devices. This research not only expands maintenance standards in the healthcare sector but also provides a critical tool for addressing the current challenges in medical device maintenance, paving the way for more reliable and effective healthcare technologies.

Virtual Tours as Effective Complement to Building Information Models in Computer-Aided Facility Management Using Internet of Things

This study investigates the integration of Building Information Models (BIMs) and Virtual Tour (VT) environments in the Architecture, Engineering and Construction (AEC) industry, focusing on Computer-Aided Facility Management (CAFM), Computerized Maintenance Management Systems (CMMSs), and data Life-Cycle Assessment (LCA). The interconnected nature of tasks throughout a building’s life cycle increasingly demands a seamless integration of real-time monitoring, 3D models, and building data technologies. While there are numerous examples of effective links between IoT and BIMs, as well as IoT and VTs, a research gap exists concerning VT-BIM integration. This article presents a technical solution that connects BIMs and IoT data using VTs to enhance workflow efficiency and information transfer. The VT is developed upon a pilot based on the Controlled Environments for Living Lab Studies (CELLS), a unique facility designed for flexible monitoring and remote-control processes that incorporate BIMs and IoT technologies. The findings offer valuable insights into the potential of VTs to complement and connect to BIMs from a life-cycle perspective, improving the usability of digital twins for beginner users and contributing to the advancement of the AEC and CAFM industries. Our technical solution helps complete the connectivity of BIMs-VT-IoT, providing an intuitive interface (VT) for rapid data visualisation and access to dashboards, models and building databases. The practical field of application is facility management, enhancing monitoring and asset management tasks. This includes (a) sensor data monitoring, (b) remote control of connected equipment, and (c) centralised access to asset-space information bridging BIM and visual (photographic/video) data.

Subgrade Geotechnical Study for Riverbank Road Construction and Maintenance Management System Quality Control

Subgrade Geotechnical Study for Riverbank Road Construction and Maintenance Management System Quality Control

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

Analysis of Extrusion Blow Molding Machine Maintenance Planning with Reliability Centered Maintenance and IRRO Method

High demand for plastic products from year to year creates the need for plastic molding machines that can mass produce plastic products. An example of a plastic molding machine used at PT. X is a VK type extrusion blow molding (EBM) machine. Based on data from PT. X, the downtime of the VK type EBM machine reached 215.74 hours in 2021-2023. Therefore, improvements are needed in the machine maintenance management system. The purpose of this research is to determine the critical components, determine the reliability value of the critical components, and also establish a preventive maintenance schedule along with a cost budget plan for each critical component of the VK type extrusion blow molding machine at PT. X to increase the effectiveness of existing production activities. The method used is Reliability Centered Maintenance (RCM) with the application of RCA and FMEA methods to find critical components, then calculate the reliability of critical components as a reference for making preventive maintenance schedules. The data used are the failure data of VK type EBM machine in 2021-2023 period. The results of this study obtained 4 critical components with a reliability value of 0.5, namely the clamping unit at 210.3149 hours, the cutting unit at 79.9911 hours, the head unit at 677.5324 hours, and the hydraulic unit at 424.1550 hours. Maintenance planning is based on the IRRO (Inspection, Repair, Replace, Overhaul) method. The total maintenance cost for the period 2025 is Rp 35,093,903, which reduces the cost by 8.5% compared to 2023.

Research and Design of Industrial Equipment Maintenance Management System Based on the Internet of Things

With the continuous increase of industrial production scale, traditional industrial equipment management methods are no longer applicable to current industrial production. In this context, the application of industrial equipment management systems is in line with the current development trend of the industry. This article starts from the perspective of system design and implementation, and designs an industrial equipment maintenance and management system from the aspects of system architecture, system functional modules, etc. for practical application needs. By combining IoT technology with industrial equipment, real-time monitoring, intelligent diagnosis, predictive maintenance, and remote control functions are achieved, thereby achieving equipment health management.

Determining the significant contributing factors to the occurrence of human errors in the urban construction projects: A Delphi-SWARA study approach

The construction industry is believed to be more susceptible to human errors than other industries because of its unique characteristics, particularly when it comes to urban construction projects (UCP). Despite the considerable attention given to human errors in construction sector, there has been a lack of emphasis on analysing these errors in specific projects like construction in urban environments with distinct complexities. Hence, this paper seeks to determine and assess the critical factors influencing human errors associated with the UCP. In this vein, Three rounds of Delphi surveys were done with 17 specialists in safety and construction management. According to the Delphi survey results, 35 substantial factors that contribute to the incidence of human errors in the UCP were discovered. Then, an empirical questionnaire based on the 5-point Likert scale of measurement was developed and distributed among 37 construction experts to assess the level of impact that each factor on occurring human error in the UCP. The questionnaire had 35 influential factors related to human errors, categorised into five primary divisions (environmental, technological/information systems, individual (permanently related), individual (temporarily related), and organisational). Before the distribution, the reliability and validity of the questionnaire were evaluated and confirmed. The factors were ranked using the Step-wise Weight Assessment Ratio Analysis (SWARA) technique at this point. The research findings indicated that the criterion of “technological factors/information systems” is the most crucial, with the criterion of “individual factors (permanently related)” coming in second and the criterion of “environmental factors” coming in third. The sub-criterion “weak maintenance management systems” scores first in the general ranking of sub-criteria, indicating traditional network systems, the absence of appropriate tools and equipment, and a lack of understanding of required resources. The sub-criterion “defects in details and information and lack of design dynamism” is placed second, while the sub-criterion “violation of safety regulations (use of drugs, etc.)” is ranked third. The study results can help industry practitioners make more educated judgements to minimise and manage human errors in the UCP.

The Use of Business Intelligence Software to Monitor Key Performance Indicators (KPIs) for the Evaluation of a Computerized Maintenance Management System (CMMS)

The increasing use of electromedical equipment in hospital care services necessitates effective management of complex devices often unsupported by existing control systems. This paper focuses on developing a pool of evaluation indices for the Clinical Engineering Department (CED) of the ASST Grande Ospedale Metropolitano Niguarda in Milano (Italy), aiming to enhance awareness of the economic value, assess operational units, and optimize maintenance processes. Leveraging business intelligence, this study identifies 18 key performance indicators (KPIs) across logistics, technical, and equipment management categories. An interactive dashboard, implemented using Power BI, facilitates dynamic analysis and visualization of these KPIs, providing insights into the maintenance efficiency and obsolescence of medical devices. It offers a comprehensive framework for ongoing monitoring and decision-making. The results showcase the potential of the developed KPIs and dashboard to enhance operational insights and guide improvements in the healthcare facility’s maintenance processes.

Enhancing maintenance efficiency in energy assets through AI: a case study of maintAI

Energy assets demand periodic regular maintenance to ensure safe, reliable and efficient operations. The dynamic nature of operating conditions and evolving best practices necessitate frequent optimisation of maintenance programs. Traditionally, these programs were labour-intensive and costly to deploy, often yielding unclear outcomes due to subjective decision-making. The advent of artificial intelligence (AI) presents an opportunity to challenge traditional approaches. This paper presents a case study where asset knowledge, data, and AI capabilities are leveraged to streamline maintenance optimisation using our ‘maintAI’ approach. The program addresses maintenance strategy, backlog, spares, and predictive maintenance optimisation with a focus on value creation, data-driven decisions, and consistent recommendations. A systematic methodology employs AI to sift through and eliminate non-value-adding tasks, enabling prioritisation of work and enhancing reliability and productivity throughout the production facility lifecycle. AI, including Natural Language Processing and Generative AI algorithms, enhances the speed and accuracy of failure mode classification from operational maintenance data. Reliability modelling techniques provide insights into equipment reliability. Recommendations undergo expert review before integration into a Computerised Maintenance Management System. Implementation of this data-driven approach demonstrates rapid deployment and sustained efficiency, yielding substantial gains in production uptime, cost reduction, and safety. The user-centric design ensures agility and ease of configuration. A recent project, which took only 6 weeks to deliver, led to a ~28% reduction in maintenance backlog, freeing capacity for critical focus areas. The maintAI approach proves a meaningful change for energy producers, offering a new solution in maintenance optimisation for enhanced reliability and productivity.

A Comprehensive Review on the Global Aviation Industry and Aircraft Maintenance Management Processes

The vision of the Turkish Republic is to surpass the level of contemporary civilization, and one of the main principles to achieve this is "The future is in the skies." To make this a reality, it is imperative to establish a national and domestic aviation industry. The first step towards this goal is to ensure the safe operation of aircraft, which requires a maintenance management system. To create and use the necessary maintenance documents, an international standard management process should be followed for airframe manufacturers and component and part manufacturers in the aviation industry. Airline operators should then create their maintenance documentation according to this system and work with airplane maintenance organizations to ensure the airworthiness of their aircraft. This study discusses and evaluates the documents used within the essential management and organization principles framework, primarily focusing on the aircraft maintenance management system.

Development and Usability Evaluation of VulcanH, a CMMS Prototype for Preventive and Predictive Maintenance of Mobile Mining Equipment

This paper details the design, development, and evaluation of VulcanH, a computerized maintenance management system (CMMS) specialized in preventive maintenance (PM) and predictive maintenance (PdM) management for underground mobile mining equipment. Further, it aims to expand knowledge on trust in automation (TiA) for PdM as well as contribute to the literature on explainability requirements of a PdM-capable artificial intelligence (AI). This study adopted an empirical approach through the execution of user tests with nine maintenance experts from five East-Canadian mines and implemented the User Experience Questionnaire Plus (UEQ+) and the Reliance Intentions Scale (RIS) to evaluate usability and TiA, respectively. It was found that the usability and efficiency of VulcanH were satisfactory for expert users and encouraged the gradual transition from PM to PdM practices. Quantitative and qualitative results documented participants’ willingness to rely on PdM predictions as long as suitable explanations are provided. Graphical explanations covering the full spectrum of the derived data were preferred. Due to the prototypical nature of VulcanH, certain relevant aspects of maintenance planning were not considered. Researchers are encouraged to include these notions in the evaluation of future CMMS proposals. This paper suggests a harmonious integration of both preventive and predictive maintenance practices in the mining industry. It may also guide future research in PdM to select an analytical algorithm capable of supplying adequate and causal justifications for informed decision making. This study fulfills an identified need to adopt a user-centered approach in the development of CMMSs in the mining industry. Hence, both researchers and industry stakeholders may benefit from the findings.

Quality Evaluation of Equipment Maintenance Process Based on Interpretive Structure Model

Based on the "5M1E" analysis method, the maintenance process quality evaluation system was established, and then the interpretive structure model (ISM) was used to analyze the hierarchical relationship of the influence of each index factor on the maintenance process quality, and the rationality of the ISM model analysis method was verified through the analytic hierarchy process. The results show that maintenance equipment and facilities A4, assembly process A5 and spare parts quality A7 are surface factors of maintenance process quality. Raw material A3 and maintenance personnel A2 are the middle-level factors of maintenance process quality; Maintenance management system A1 and maintenance environment A6 are the deep factors of the quality of maintenance process, and are the influential factors to focus on and improve. In addition, the corresponding maintenance suggestions are given for each factor, which provides theoretical guidance for the formulation of equipment maintenance management procedures.

Creation of a Computerized Maintenance Management System (CMMS) with Features and Planning Requirements

Creation of a Computerized Maintenance Management System (CMMS) with Features and Planning Requirements

A Smart Management System for Operation and Maintenance of Facilities

A Smart Management System for Operation and Maintenance of Facilities

An ontology-driven model for hospital equipment maintenance management: a case study

Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

Research on Fault Prediction and Health Management System of Power Electronic Devices Based on Computer Deep Learning Algorithm

This paper proposes a power equipment maintenance management system based on network and deep learning algorithm. First of all, the actual power network equipment maintenance and maintenance management of the power company, so as to clarify the tasks to be completed by the system. ASPNET technology is used to realize the management of business form data and various transaction processes of each system, and the related technology of process engine is applied to the maintenance of each equipment. A load forecasting method of power system based on SVR is proposed. It makes the operation and maintenance work of this system more intelligent. Each function module of the whole system is designed in detail. The system basically meets the needs of power equipment maintenance and repair management.

CMMS- Streamlining Maintenance management for Food and Beverage Industry to capture breakdowns

CMMS stands for Computerized Maintenance Management System. In today’s world, when technology is advancing each day at an astonishing rate, any organization must have a detailed knowledge and understanding of its current state of machinery, the number of resources spent on the production floor to maintain equipment reliability, and resource allocation. CMMS is a critical and powerful tool that assists and helps any manufacturing industry to make full use of its capabilities, manage or direct workforce, improve the machine reliability, find innovative ways to improve productivity, stay in compliance with food and dietary regulatory standards as per the safe quality foods (SQF) and national sanitation foundation (NSF) standards and achieve series of goal and benefits the organization overall. Today, the food and beverage industry has grown tremendously and is the backbone of the manufacturing industry. Every company looks at creative ways to help them excel by selecting a reliable CMMS that suits their operations and processes to make educated decisions by monitoring their KPIs. This theoretical study aims to provide a building framework to set up plant maintenance modules to manage work orders, including emergency breakdowns and scheduled maintenance repairs in the food and beverage industry.