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The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.

A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.

The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.

The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.

The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.

Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.

The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.

This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.

This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.

The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.

Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

The purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.

We propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.

A case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.

Previous research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.

This study aims to address the critical issue of machine breakdowns in industrial settings, which jeopardize operation economy, worker safety, productivity and environmental compliance. It explores the efficacy of a predictive maintenance program in mitigating these risks by proactively identifying and minimizing failures, thereby optimizing maintenance activities for higher efficiency.

The article implements Logical Analysis of Data (LAD) as a predictive maintenance approach on an industrial machine maintenance dataset. The aim is to (1) detect failure presence and (2) determine specific failure modes. Data resampling is applied to address asymmetrical class distribution.

LAD demonstrates its interpretability by extracting patterns facilitating the failure diagnosis. Results indicate that, in the first case study, LAD exhibits a high recall value for failure records within a balanced dataset. In the second case study involving smaller-scale datasets, enhancement across all evaluation metrics is observed when data is balanced and remains robust in the presence of imbalance, albeit with nuanced differences in between.

This research highlights the importance of transparency in predictive maintenance programs. The research shows the effectiveness of LAD in detecting failures and identifying specific failure modes from diagnostic sensor data. This maintenance strategy exhibits its distinction by offering explainable failure patterns for maintenance teams. The patterns facilitate the failure cause-effect analysis and serve as the core for failure prediction. Hence, this program has the potential to enhance machine reliability, availability and maintainability in industrial environments.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

This study describes how maintenance capability should be created during the design and construction phases of construction projects. Purpose of the abstract to define the elements for creating the maintenance capability and the process to be used in construction life cycle projects for buildings.

An inductive and qualitative research method was used to construct the proposed process based on the literature and 18 interviews in two large construction companies.

The results indicate that the maintenance phase is usually overlooked during the design and construction phases, and capabilities are not systematically built. In particular, processes are lacking in data management, causing severe problems in maintenance.

This study presents a process including key requirements and activities for creating maintenance capability in conjunction with the design and construction phases, which is novel to the literature. The validated process can be adapted based on the needs of the construction company.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.