Intelligent decision support for maintenance: an overview and future trends

Abstract

The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision making systems. While e-maintenance practice provides a framework for internet connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of a comprehensive framework for its processing, analysis and use should be a valuable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data and the provision of comprehensive framework for its processing analysis and use, allowing future systems to enable 'Human in the loop' interactions.

Supply chain integration and Industry 4.0: a systematic literature review

“…Khakifirooz et al (2018) and Cardin (2019) identified that CPS focuses primarily on measuring information about talked about quality-controlled logistics for perishable goods that incorporates decisions from suppliers to issue replacement shipments and engineers to perform corrections. Some authors (Turner et al, 2019;Ben-Daya et al, 2019) discuss the importance of maintenance. Turner et al (2019) machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems.…”

“…(2019),Ahmi et al (2019),Raptis et al (2019),Hohmann and Posselt (2019),Rojas and Rauch (2019),Oztemel and Gursev (2020),Xu et al (2018),Factorachian and Kazemi (2020),Da Silva et al (2019),Muhuri et al (2019),Piccarozzi et al (2018),Turner et al (2019),Ben-Daya et al (2019),Winkelhaus and Grosse (2020),Cui et al (2020),Rossit et al (2019a, c),Kipper et al (2020),Nakayama et al (2020),Fatorachian and Kazemi (2018),Pinzone et al (2020),Manavalan and Jayakrishna (2019), Heged} us et al (2019), Cardin (2019), Schuh et al (2014), Yang et al Zhang et al (2018), Khakifirooz et al (2018), H€ ackel et al (2019), Rossit et al (2019b), Gru zauskas et al (2018), Romero-Silva and Hern andez-L opez (2020), Juh asz and B anyai (2018) 11.86 Model/Architecture Garrido-Hidalgo et al (2019), Monostori et al (2016), Bicocchi et al (2019), Singh et al (2019), Cimini et al (2020) 8.47 Hypothesis Salam (2019), Bag et al (2020), Buchi et al (2020) 5.08 Experimenting method O'Donovan et al (2019), Regal and Pereira (2019) 3.38 Case study Sicari et al (2019), Choi et al (2017) 3.38 Others (Less then 2 item Marinagi et al (2018), Li (2018), Qiu et al (2018), Thoben et al (2017), Klumpp et al (2019), Foidl and Felderer (2015), Tuptuk and Hailes (2018), Kang et al (2016), Chengula et al (2018), Da Silva et al (2019), Kusiak ((2018), Manavalan and Jayakrishna (2019), Marinagi et al (2018), H€ ackel et al (2019), Heged} us et al (2019), Raptis et al (2019), Bicocchi et al (2019), Qiu et al (2018), Factorachian and Kazemi (2020), Tuptuk and Hailes (2018), Kang et al (2016), Monostori et al (2016), Cohen et al (2019) M2: Human factors Pinzone et al (2020), Li (2018), Cardin (2019), Da Silva et al (2019), Piccarozzi et al (2018), Winkelhaus and Grosse (2020), Klumpp et al (2019), Buchi et al (2020), Qu et al (2019), Yang et al (2019), Singh et al (2019) M3: cyber-physical industrial system Cimini et al (2020), Monostori et al (2016), Thoben et al (2017), Rossit et al (2019a, b, c), Pinzone et al (2020), Khakifirooz et al (2018), Cardin (2019), Schuh et al (2014), Hohmann and Posselt (2019), Rojas and Rauch (2019), Muhuri et al (2019), Romero-Silva and Hern andez-L opez (2020), Kusiak (2018), Juh asz and B anyai (2018) M4: Quality management/ Maintenance Foidl and Felderer (2015), Ben-Daya et al (2019), Turner et al (2019), Singh et al (2019), Cohen et al (2019) Logistics(L) L1: Warehousing Singh et al (2019), Ben-Daya et al (2019), Winkelhaus and Grosse (2020). Gru zauskas et al (2018) L2: Procurement/ Purchases Factorachian and Kazemi (2020...…”

Determining the impact of 5G-technology on manufacturing performance using a modified TOPSIS method

“…Billions of sensors, machine-controlled robots, and autonomous logistics which can operate remotely in real-time are anticipated to contribute to productivity improvements in the manufacturing industry (Rao and Prasad 2018). Thanks to the development of 5G-technology, these smart systems have become more powerful for ubiquitous connectivity on the shop floor (Turner et al 2019). Therefore, 5G-technology will bring faster and more reliable communication between machines, sensors, and computing systems, resulting in a more flexible and productive manufacturing system (Rao and Prasad 2018).…”

Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review

“…The use of digital technologies in maintenance has given rise to the term eMaintenance (Iung et al, 2009), which is further advanced by the utilisation of Industry 4.0 technologies for this practice (Johansson et al, 2019;Turner et al, 2019;Jasiulewicz-Kaczmarek and Gola, 2019). Predictive maintenance in particular is enabled by digital technologies, and has much to offer towards the effective utilisation of maintenance for sustainability.…”