Deep digital maintenance

Abstract

With the emergence of Industry 4.0, maintenance is considered to be a specific area of action that is needed to successfully sustain a competitive advantage. For instance, predictive maintenance will be central for asset utilization, service, and after-sales in realizing Industry 4.0. Moreover, artificial intelligence (AI) is also central for Industry 4.0, and offers data-driven methods. The aim of this article is to develop a new maintenance model called deep digital maintenance (DDM). With the support of theoretical foundations in cyber-physical systems (CPS) and maintenance, a concept for DDM is proposed. In this paper, the planning module of DDM is investigated in more detail with realistic industrial data from earlier case studies. It is expected that this planning module will enable integrated planning (IPL) where maintenance and production planning can be more integrated. The result of the testing shows that both the remaining useful life (RUL) and the expected profit loss indicator (PLI) of ignoring the failure can be calculated for the planning module. The article concludes that further research is needed in testing the accuracy of RUL, classifying PLI for different failure modes, and testing of other DDM modules with industrial case studies.

Assessing relations between Circular Economy and Industry 4.0: a systematic literature review

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“…Wang, Ong, and Nee 2018) x Total 5 4 3 13 (Barbosa et al 2016) x (Bassi 2017) x (Deschamps et al 2018) x (Gürdür and Gradin 2017) x (Hehenberger et al 2016) x (Holligan, Hargaden, and Papakostas 2017) x (Isaksson, Hallstedt, and Öhrwall Rönnbäck 2018) x (Jensen and Remmen 2017) x (Kusiak 2018) x (J. Li et al 2015) x (Ma et al 2018) x (Mashhadi and Behdad 2018) x (Menon, Kärkkäinen, and Gupta 2016) x (Menon et al 2018) x (Minetola and Eyers 2018) x (Miranda et al 2017) x (Müller et al 2018) x (Pacis, Subido Jr., and Bugtai 2017) x (Rødseth, Schjølberg, and Marhaug 2017) x (Sauerwein, Bakker, and Balkenende 2017) x (Schmidt et al 2017) x (Schroeder et al 2016a) x (Schroeder et al 2016b) x (Sinclair et al 2018) x (Sharpe et al 2018) x (Sheng Yang et al 2017) x (Syed-Khaja, Perez, and Franke 2016)…”

Proposal for requirements on industrial AI solutions

“…[3,4]. More recently, research focused on the application of AI techniques in fault diagnosis and predictive maintenance [5][6][7][8][9][10][11][12] as well as decision support systems [5,13]. Current academic investigations aim at the coordination of the introduction of AI into all layers of production systems [14] as well as production-wide maintenance processes [15].…”

Reliability-based cyber plant

“…Thus it is in this article of interest to investigate how FMECA can be balanced with big data analytics such as machine learning. The maintenance model called deep digital maintenance (DDM) comprise an artificial intelligence module that tested remaining useful life (RUL) prediction based on dataset of degradation simulation run-to-failure data of jet engines (Rødseth et al, 2017). The output of the prediction model is the probability that RUL is more than 10 cycles in a specific point in time, denoted as P r (RUL > 10).…”