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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.

Research questions: Is there a systemic relationship between different methods of screening candidates for predictive maintenance? How do the goals of a predictive project influence the choice of a dropout method? How do the company’s characteristics implementing the predictive project influence the selection of the dropout method?

The authors described and compiled a taxonomy of currently known methods of screening candidate aircraft components for predictive maintenance for maintenance, repairs and overhaul organizations; identified the boundaries of each way; analyzed the advantages and disadvantages of existing methods; and formulated directions for further development of methods of screening for maintenance, repairs and overhaul organizations.

The authors identified the relationship between various screening methods by developing the approach proposed by Tiddens WW and supplementing it with economic methods. The authors built them into a single hierarchical structure and linked them with the parameters of the predictive project. The principal advantage of the proposed taxonomy is a clear relationship between the structure of the screening methods and the goals of the predictive project and the characteristics of the company that implements the project.

The authors of the article proposed groups of screening methods for predictive maintenance based on economic indicators to improve the effectiveness and efficiency of the screening process.

This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.

For conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.

Based on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.

Papers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements.

The formalism applied to pursue this aim is association rules mining since it allows to discover the existence of relationships between sub-plant stoppages and components breakdowns.

The application of the maintenance policy to a three-year case highlighted that the extracted rules depend on both the kind of stoppage and the timeframe considered, hence different maintenance strategies are suggested.

This paper demonstrates that data mining (DM) tools, like association rules (AR), can provide a valuable support to maintenance processes. In particular, the described policy can be generalized and applied both to other refineries and to other continuous production systems.

The purpose of this paper is to explore maintenance strategies for manufacturing equipment in Chinese firms.

Data were collected from Chinese companies using a questionnaire administered during face‐to‐face interviews and two established methodologies in strategic management research, exploratory factor analysis and cluster analysis, were used to analyze the data.

The results suggest that despite increasing competitive capabilities of Chinese firms, their maintenance strategies are often restricted to corrective maintenance. Only very few Chinese firms have already implemented predictive maintenance approach, total productive maintenance programs or the strategic outsourcing of maintenance activities.

The research limitations stem from typical issues related to the use of exploratory factor analysis and cluster analysis (for example reliance on the subjective judgment of the researcher or the provision of clusters although no meaningful groups are embedded in the sample).

The findings highlight potential strategies for Chinese firms to improve their maintenance management.

This paper deals with a neglected area of operations management by exploring the maintenance approaches in fast growing Chinese manufacturing industries.

The purpose of this paper is to describe a methodology that has been set up to schedule predictive maintenance of distribution transformers at Cauca Department (Colombia) using machine learning.

The proposed methodology relies on classification predictive model that finds the minimal number of distribution transformers prone to failure. To verify this, the model was implemented and tested with real data in Cauca Department Colombia.

The implementation of the methodology allows a saving of 13% in corrective maintenance expenses for the year 2020.

The proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for distribution transformers.

This paper aims to prioritize preventive maintenance actions on process equipment by evaluating the risk associated with failure modes using predictive maintenance data instead of maintenance history alone.

In process plants, maintenance task identification is based on the failure mode and effect analysis (FMEA). To eliminate or mitigate risk caused by failure modes, maintenance tasks need to be prioritized. Risk priority number (RPN) can be used to rank the risk. RPN is estimated invariably using maintenance history. However, maintenance history has deficiencies, like limited data, inconsistency etc. To overcome these deficiencies, the proposed approach uses the predictive maintenance data clubbed with expert domain knowledge. Unlike the traditional single step approach, RPN is estimated in two steps, i.e. Step 1 estimates the “Possibility of failure mode detection” and Step 2 estimates RPN using output of step 1. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge. Fuzzy inference system is developed using MATLAB® 6.5.

The proposed approach is applied to a large gearbox in an integrated steel plant. The gearbox is covered under a predictive maintenance program. RPN for each of the failure modes is estimated with the proposed approach and compared with the maintenance task schedule. The illustrative case study results show that the proposed approach helps in detection of failure modes more scientifically and prevents “Over maintenance” to ensure reliability.

This approach gives an opportunity to integrate the predictive maintenance data and subjective/qualitative domain expertise to evaluate the possibility of failure mode detection (POD) quantitatively, which is otherwise purely estimated using subjective judgments. The approach is generic and can be applied to a variety of process equipment to ensure reliability through prioritized maintenance scheduling.

The after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.

We propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.

We found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.

This approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.

This approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.

Sustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.

This is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

This article evaluates maintenance management in an electric utility setting. It begins with a historical review before discussing present pressures on management and the new importance of maintenance costs. It discusses remedial, preventive and predictive maintenance, and the implications of each one of these maintenance functions.