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The purpose of this paper is to respond to a call for research focusing on how and by how much new Product‐Service System (PSS) development methodologies and tools can help companies moving towards product‐oriented PSS, in the oil and gas equipment manufacturers industry.

This study has been conducted using a single case study method. The single case study enabled the authors to analyze the implementation of PSS in a complex project environment, where the unit of analysis is an organization that designs, builds and delivers integrated product‐service offers. The selected case study is Nuovo Pignone S.p.a., a brand of the “GE Oil & Gas” company, that produce products and services for the oil and gas industry.

The case study research confirms most of the statement reported in the state of the art, i.e. the oil and gas industries do not use a methodology to develop new PSS. Moreover, a new methodology for this specific sector seems not to be necessary, while a correct approach in the new PSS development process definition and the application of some tools of the existing methods could improve the servitization process performances of such companies.

The paper focuses on the product‐oriented PSS, where the ownership of the product is transferred to customer, and where manufacturer offers additional services directly related to the product through the use of contractual services.

This work adds a contribution in the use of a methodology for the development of a new PSS in the oil and gas industry servitization 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.