Dataflow specification in the DSS layers

This subsection gives a detailed description of the three layers of the DSS identified in the previous subsection, namely, the data layer, the model layer and the presentation layer as shown in Figure II-5.

II.3.4.1 DATA LAYER

This layer consists of the data from the internal and external data sources ([178], [280]). The external data sources constitute the raw acquired data by the embedded sensors in the CPSs subsystems, the health status indicators (obtained as the results of the raw acquisition data processing) showing the current health status of the CPSs (ref. to diagnosis [281], [282], [237]). Moreover, due to the presence of CBM indicators in the CPSs, the external data sources consist of the data indicating the future pos-sible health status of the respective CPSs (ref. to prognosis [236], [266], [283], [17], [284], [234], [285]).

Furthermore, the external data sources constitute the information from the fleet operator on the num-ber of CPSs needed to satisfy the fleet operations as well as the information from the maintenance depots on the availability of the maintenance resources such as the maintenance teams, maintenance infrastructure and replacement parts. As far as the internal data sources are concerned, this layer con-sists of the data from the model layer and the presentation layers as detailed in the coming subsec-tions.

II.3.4.2 MODEL LAYER

This layer consists of the reactive FMSP model. The reactive FMSP model in the model layer carries out the following operations:

➢ The computation of the CPSs groups (no maintenance required group, CBM group and cor-rective maintenance group). These groups are calculated using acquired raw variables, health indicators and CBM indicators of the CPSs.

➢ The verification of the fleet’s availability level. This is computed using the number of CPSs required for fleet operations (ϵ, indicated by the fleet operator), the number of CPSs which are mission ready (in terms of CPSs’ health status) and the fleet’s availability threshold (μ) as indicated by the fleet supervisor.

➢ Verifications of the maintenance resources availability. The verifications have to be made vis-à-vis the maintenance depots information data handled in the data layer. The mainte-nance resources in this context considers the maintemainte-nance teams, the maintemainte-nance infra-structure and the replacement parts.

➢ Reactive maintenance planning of the CPSs in terms of their health, fleet availability and the availability of the maintenance resources.

Chapter III will present a detail description of the reactive FMSP model integrated in this layer, which constitutes the core scientific contribution of our research.

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II.3.4.3 PRESENTATION LAYER

This layer consists of a UI delaying information between other layers (data layer and model layer) and the fleet supervisor. In this sense, the fleet supervisor becomes an important component in the design of the DSS ([286], [287]). In the context of this work, the presentation layer brings about the following requirements:

➢ Fleet information

o Current and future CPSs’ health status o CPSs’ geolocations

➢ Maintenance depots information o Maintenance teams o Replacement parts o Infrastructure o Geolocations

➢ Fleet operator information o Required fleet availability

➢ Optimized maintenance planning

➢ Interactions between the fleet supervisor and the DSS. This is discussed in detail by Sprague [178] and Keen [288]. The approach to these interactions can be for example by Natural language processing (NLP) technique ([289], [290], [291] and [292]).

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FIGURE II-5:DESIGN LAYERS IN THE DSS

II.4 SUMMARY

This chapter has first provided a set of specifications regarding the design of a reactive CPSs FMSP system with an objective of satisfying the fleet’s availability and reliability expectations in a dynamic environment (i.e. presence of perturbations). For that purpose, several assumptions had to be set to

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narrow the scope of the work. These assumptions reduced the FMSP framework defined in chapter 1 as shown in Table II-1 as follows:

TABLE II-1:REDUCED CONTEXT IN FMSP FRAMEWORK

FMSP framework aspects (Chapter I) Reduced aspects Objectives: Sustainability

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• ANSI TAPPI TIP 0305-34:2008

• Industrial Internet Consortium Ref-erence Architecture

Maintenance policies: Reactive and preven-tive techniques. (Further classification in chapter 1)

• CBM

• Corrective

• E-maintenance

Subsequently, after having defined the boundaries of the specification context, the FMSP problem modelling assumptions and data requirements were laid out. Through these assumptions and require-ments, the FMSP problem was bounded (subsections II.2.2 and II.2.3 respectively). Moreover, to aid the human decision-maker (referred to as the fleet supervisor), in attaining the specified objectives (availability, reliability and reactivity), a decision support approach was adopted. A DSS that could in-tegrate our contribution was therefore specified (section II.3). In this sense, a reactive FMSP model is to be integrated in the model layer of the specified DSS.

Following the CPSs FMSP system specifications provided in this chapter, the principle interest that fol-lows is the design of the reactive FMSP model integrated in the model layer of the DSS presented in this chapter. In the coming chapter, this reactive FMSP model is formulated using a multi-agent system (MAS) approach.

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A MULTI-AGENT SYSTEM FOR THE REACTIVE CPSs FLEET MAINTENANCE SUPPORT PLANNING

Chapter II specified the design of a reactive CPSs FMSP system. The objective of this chapter is to de-velop a possible reactive CPSs FMSP decision-making model integrated in the model layer of the DSS specified in the previous chapter. The remainder of this chapter is organized as follows, section III.1 will present the choice of the modelling approach. In this section, a multi-agent system (MAS) approach is chosen. The justifications for the MAS modelling approach as well the limitations and drawbacks of the latter are provided in this section. In section III.2, the reactive MAS for CPSs FMSP decision-making is proposed, using the ANEMONA MAS design methodology. The section III.3 will conclude the chapter by giving the summary and the perspectives for the next chapter.