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The increase in available data from sensors embedded in industrial equipment has led to a recent rise in the use of industrial predictive maintenance. In the aircraft industry, predictive maintenance has become an essential tool for optimizing maintenance schedules, reducing aircraft downtime, and identifying unexpected faults. Despite this, there is currently no comprehensive survey of predictive maintenance applications and techniques solely devoted to the aircraft manufacturing industry. This article is an in‐depth state‐of‐the‐art systematic literature review of the different data types, applications, projects, and opportunities for predictive maintenance in this industry. The goal of this review is to identify, and highlight the challenges and opportunities for future research in this field. This review found that the current focus of research is too biased towards aircraft engines due to a lack of publicly available data sets, and that greater automation is an important step to optimize aircraft maintenance to its full potential. [ABSTRACT FROM AUTHOR]

The ability to process large amounts of data and to extract useful insights from data has revolutionised society. This phenomenon—dubbed as Big Data—has applications for a wide assortment of industries, including the construction industry. The construction industry already deals with large volumes of heterogeneous data; which is expected to increase exponentially as technologies such as sensor networks and the Internet of Things are commoditised. In this paper, we present a detailed survey of the literature, investigating the application of Big Data techniques in the construction industry. We reviewed related works published in the databases of American Association of Civil Engineers (ASCE), Institute of Electrical and Electronics Engineers (IEEE), Association of Computing Machinery (ACM), and Elsevier Science Direct Digital Library. While the application of data analytics in the construction industry is not new, the adoption of Big Data technologies in this industry remains at a nascent stage and lags the broad uptake of these technologies in other fields. To the best of our knowledge, there is currently no comprehensive survey of Big Data techniques in the context of the construction industry. This paper fills the void and presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data mining and warehousing, machine learning, and Big Data Analytics in the context of the construction industry. We discuss the current state of adoption of Big Data in the construction industry and discuss the future potential of such technologies across the multiple domain-specific sub-areas of the construction industry. We also propose open issues and directions for future work along with potential pitfalls associated with Big Data adoption in the industry. [ABSTRACT FROM AUTHOR]