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An analysis of failures in a capital city's wastewater collection system was carried out and recommendations were made for sustainable preventive measures based on a risk of failure assessment. Most failures in sewer lines were associated with blockage caused by sediment accumulation and clogging from fats, oils and/or grease dumped by restaurants along several streets, combined with poor or nonexistent maintenance of the lines. Sewer lines in streets with higher risk levels due to multiple food establishments along those streets experienced most of the failures. Sustainability of the proposed maintenance was evidenced since it reduces costs and exposure to harmful substances and hazardous conditions as well as minimizing environmental impacts.

With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain sustainable operational management, Predictive Maintenance (PdM) has become important in industries. The principle of PdM is forecasting the next failure; thus, the respective maintenance is scheduled before the predicted failure occurs. In the construction of maintenance management, facility managers generally employ reactive or preventive maintenance mechanisms. However, reactive maintenance does not have the ability to prevent failure and preventive maintenance does not have the ability to predict the future condition of mechanical, electrical, or plumbing components. Therefore, to improve the facilities’ lifespans, such components are repaired in advance. In this paper, a PdM planning model is developed using intelligent methods. The developed method involves five main phases: (a) data cleaning, (b) data normalization, (c) optimal feature selection, (d) prediction network decision-making, and (e) prediction. Initially, the data pertaining to PdM are subjected to data cleaning and normalization in order to arrange the data within a particular limit. Optimal feature selection is performed next, to reduce redundant information. Optimal feature selection is performed using a hybrid of the Jaya algorithm and Sea Lion Optimization (SLnO). As the prediction values differ in range, it is difficult for machine learning or deep learning face to provide accurate results. Thus, a support vector machine (SVM) is used to make decisions regarding the prediction network. The SVM identifies the network in which prediction can be performed for the concerned range. Finally, the prediction is accomplished using a Recurrent Neural Network (RNN). In the RNN, the weight is optimized using the hybrid J-SLnO. A comparative analysis demonstrates that the proposed model can efficiently predict the future condition of components for maintenance planning by using two datasets—aircraft engine and lithium-ion battery datasets.

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

Purpose The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths. Design/methodology/approach Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities. Findings This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions. Research limitations/implications It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities. Social implications Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities. Originality/value This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

The smart sustainable city (SSC) is a concept created in response to problems and challenges arising from rapid urbanization. This is a relatively new term that is developing dynamically, which is confirmed by the growing number of publications over recent years. For this reason, this article presented an up-to-date comprehensive bibliometric analysis to describe and assess the scientific landscape of smart and sustainable cities literature. The analysis was based on two bibliographic sources—the Web of Science Core Collection and the Scopus database. It covers publications on the SSC, as well as documents describing the smart city (SC) and the sustainable city (SuC) concepts separately. VOSviewer and Biblioshiny were selected as software tools for the bibliometric analysis. Based on the descriptive bibliometric analysis, quantity and quality indicators were determined separately for the SC, SuC, and SSC concepts, while the network analysis mapped and covered the level of multi-faceted scientific cooperation in the field of the SSC research. The analysis results were intended to familiarize scholars and practitioners with the most prolific authors, sources, institutions, and countries in the analyzed scientific field, to identify the most influential research channels and impact from authors, sources, countries, and research topics, to determine major clusters of the SSC research and also to provide valuable information for further investigation.

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

To resolve the problem of the low management capacity of highway preventive maintenance (HPM), this paper identified and evaluated the major HPM management factors to improve management effectiveness and achieve sustainable highway development. The study conducted a literature review and exploratory factor analysis (EFA) to identify the major HPM management factors. Social network analysis (SNA) was used to distinguish the degree of importance of these factors. A system dynamics (SD) model was developed to explore their patterns of influence. The research identified six dimensions of HPM management, including the management system, management resources, management cognition, management decisions, management technology, and external conditions, along with 26 major management factors. Moreover, information acquisition, system perfection, etc., are key factors; system execution, manager capability, etc., are hub factors; and route selection, machinery allocation, etc., are non-key factors. These factors have a positive impact on HPM management, leading to an upward trend in management effectiveness. The main innovation provided a hybrid and comprehensive approach to identify and evaluate the major management factors for effective HPM. This study can guide managers in developing effective HPM plans, allocating resources more efficiently, improving the overall quality of highway maintenance and forming a sustainable transportation system.

Over the life cycle of a civil infrastructure (a bridge as an example), 0.4–2% of the construction cost is spent annually on its maintenance. Utilising new technologies including the internet of things (IoT) and digital twin (DT) can significantly reduce the infrastructure maintenance costs. An infrastructure DT involves its digital replica and must include data on geometric, geospatial reference, performance, attributes (material, environment etc.) and management. Then, the acquired data need to be analysed and visualised to inform maintenance decision making. To develop this DT, the first step is the study of the infrastructure life cycle to design DT architecture. Using data semantics, this paper presents a novel DT architecture design for an intelligent infrastructure maintenance system. Semantic modelling is used as a powerful tool to structure and organize data. This approach provides an industry context through capturing knowledge about infrastructures in the structure of semantic model graph. Using new technologies, DT approach derives and presents meaningful data on infrastructure real-time performance and maintenance requirements, and in a more expressible and interpretable manner. The data semantic model will guide when and what data to collect for feeding into the infrastructure DT. The proposed DT concept was applied on one of the conveyors of Dalrymple Bay Coal Terminal in Queensland Australia to monitor the structural performance in real-time, which enables predictive maintenance to avoid breakdowns and disruptions in operation and consequential financial impacts.

PurposeTotal Productive Maintenance (TPM) could act as a practical approach to offer sustainability deliverables in manufacturing firms aligning with the natural resource-based view (NRBV) theory's strategic capabilities: pollution prevention, product stewardship and sustainable development. Also, the emergence of Blockchain Technology (BCT) and Circular Economy (CE) are proven to deliver sustainable outcomes in the past literature. Therefore, the present research examines the relationship between BCT and CE and TPM's direct and mediation effect through the lens of NRBV theory.Design/methodology/approachThe current study proposes a conceptual framework to examine the relationship between BCT, CE and TPM and validates the framework through the Partial Least Squares Structural Equation Modeling. Responses from 316 Indian manufacturing firms were collected to conduct the analysis.FindingsThe investigation outcomes indicate that BCT positively influences CE and TPM and that TPM has a significant positive impact on CE under the premises of NRBV theory. The results also suggest that TPM partially mediates the relationship between BCT and CE.Research limitations/implicationsThis research fills a gap in the literature by investigating the effect of BCT and TPM on CE within the framework of the NRBV theory. It explores the link between BCT, TPM and CE under the NRBV theory's strategic capabilities and TPM mediation.Practical implicationsThe positive influence of TPM and BCT on CE could initiate the amalgamation of BCT-TPM, improving the longevity of production equipment and products and speeding up the implementation of CE practices.Originality/valueThis research fills a gap in the literature by investigating the effect of BCT and TPM on CE within the framework of the NRBV theory. It explores the link between BCT, TPM and CE under the NRBV theory's strategic capabilities along with TPM mediation.

PurposeThe purpose of this research is to establish a conceptual model to understand the impact of Total Productive Maintenance (TPM) and Industry 4.0 (I4.0) on the transition of a Circular Economy (CE). Also, the paper explores the combined impact of TPM, I4.0 and CE on the sustainability performance (SP) of manufacturing firms.Design/methodology/approachThe conceptual model is proposed using the dynamic capability view (DCV) and empirically validated by partial least squares-structural equation modelling (PLS-SEM) using 304 responses from Indian manufacturing firms.FindingsThe results suggest that I4.0 positively impacts TPM, CE and SP, also showing TPM's positive impact on CE and SP. In addition, CE has a positive influence on the SP of manufacturing firms. Furthermore, CE partially mediates the relationship between I4.0 and SP with TPM and SP. The study also identifies TPM, I4.0 and CE as a new bundle of dynamic capabilities to deliver SP in manufacturing firms.Originality/valueThe present research adds to the knowledge and literature on DCV by identifying the importance of CE in the settings of I4.0 and TPM, especially in the context of sustainability. Also, the current study offers a new set of dynamic capabilities and provides some significant future recommendations for researchers and practitioners.