Advanced technologies based on Internet of Things (IOT) are blazing a trail to effective and efficient management of an overall plant. In this context, manufacturing companies require an innovative strategy to survive in a competitive business environment, utilizing those technologies. Guided by these requirements, the so-called predictive maintenance is of paramount importance and offers a significant potential for innovation to overcome the limitations of traditional maintenance policies. However, real shop-floors often have obstacles in providing insights to facilitate the effective management of assets in smart factories. Even if a significant amount of machine and process data is available, one of the common problems of these data is the lack of annotations describing the machine status or maintenance history. For this reason, companies have limited options to analyse manufacturing data, despite the capability of advanced machine learning techniques in supporting the identification of failure symptoms in order to optimize scheduling of maintenance operations. Moreover, each machine generates highly heterogeneous data, making it difficult to integrate all the information to provide data-driven decision support for predictive maintenance. Inspired by these challenges, this research provides a hybrid machine learning approach combining unsupervised learning and semi-supervised learning. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled BOOST 4.0 i.e. Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories.

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Bowing to the burgeoning needs of online consumers, exploitation of social media content for extrapolating buyer-centric information is gaining increasing attention of researchers and practitioners from service science, data analytics, machine learning and associated domains. The current paper aims to identify the structural relationship between product attributes and subsequently prioritize customer preferences with respect to these attributes while exploiting textual social media data derived from fashion blogs in Germany. A Bayesian Network Structure Learning (BNSL) model with K2-score maximization objective is formulated and solved. A selftailored metaheuristic approach that combines Self-Learning Particle Swarm Optimization (SLPSO) with K2 algorithm (SLPSOK2) is employed to decipher the highest scored structures. The proposed approach is implemented on small, medium and large size instances consisting of nine fashion attributes and 18 problem sets. The results obtained by SLPSOK2 are compared with Particle Swarm Optimization/K2 score (PSOK2), Genetic Algorithm/K2 score (GAK2), and Ant Colony Optimization/K2 score (ACOK2). Results verify that SLPSOK2 outperforms its hybrid counterparts for the tested cases in terms of computational time and solution quality. Furthermore, the study reveals that psychological satisfaction, historical revival, seasonal information and facts and figure based reviews are major components of information in fashion blogs that influence the customers.

The offshore plant equipment usually has a long life cycle. During its O&M (Operation and Maintenance) phase, since the accidental occurrence of offshore plant equipment causes catastrophic damage, it is necessary to make more efforts for managing critical offshore equipment. Nowadays, due to the emerging ICTs (Information Communication Technologies), it is possible to send health monitoring information to administrator of an offshore plant, which leads to much concern on CBM (Condition-Based Maintenance). This study introduces three approaches for predicting the next failure time of offshore plant equipment (gas compressor) with case studies, which are based on finite state continuous time Markov model, linear regression method, and their hybrid model.

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