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ScienceDirect Procedia Engineering 178 (2017) 419 – 426

16thConference on Reliability and Statistics in Transportation and Communication, RelStat’2016, 19-22 October, 2016, Riga, Latvia

Design of Embedded Architecture for Integrated Diagnostics in Avionics Domain Igor Kabashkin* Transport and Telecommunication Institute, Lomonosova 1, Riga, LV 1019, Latvia

Abstract The resent paper introduces a multi-level decision-making approach for design of optimal embedded integrated diagnostic architecture that combines maintenance decisions at the k-levels of system architecture and integration with health and usage monitoring systems (HUMS) mechanisms for achieving efficient system for level maintenance and lowering life-cycle cost of Integrated Modular Avionics (IMA). HUMS can be implemented in software or directly on an integrated circuit. The effectiveness of such an approach is investigated through the optimization of embedded HUMS architecture for known reliability and economic dependence during life cycle of IMA. © Published by Elsevier Ltd. This © 2017 2017The TheAuthors. Authors. Published by Elsevier Ltd. is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 16th International Conference on Reliability and Statistics in Peer-review underand responsibility of the scientific committee of the International Conference on Reliability and Statistics in Transportation Communication. Transportation and Communication Keywords: Embedded architecture, health and usage monitoring systems, integrated modular avionics

1. Introduction Embedded systems are an essential component of safety-critical applications, such as avionics of airplanes and air traffic management systems (ATM). The future ATM system infrastructure will consist of a mix of access technologies, each with its own avionics elements at the aircraft side (airborne embedded cloud) and its own ground infrastructure (ground embedded clouds). The high level of system integration in avionics is made possible through

* Corresponding author. E-mail address: [email protected]

1877-7058 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the International Conference on Reliability and Statistics in Transportation and Communication

doi:10.1016/j.proeng.2017.01.081

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Igor Kabashkin / Procedia Engineering 178 (2017) 419 – 426

the use of integrated avionics architectures or Integrated Modular Avionics (IMA) (Garside and Pighetti, 2007). The IMA employs extensive use of Surface Mount Technology, Very Large Scale Integrated Circuits, and Application Specific Integrated Circuits. IMA has been developed to create a modular, open, and highly-flexible architecture for digital avionics. IMA may also have some special advantages for space applications, where power, weight, and volume are of particular concern. By hosting many applications on the same platform, some of which run at different times than others, the total amount of hardware needed can be reduced and consequently there will be cost, weight and volume savings and perhaps some power savings as well (Butler, 2008). One of the important attribute of the embedded systems for such safety-critical applications as IMA is diagnosability – ability of system to support the identification of information related to its real or potential faults (Rajan and Wahl, 2013). Traditional maintenance decisions in the framework of condition-based maintenance applied to multi-component systems are performed either at the system level or at the component level. These decisions however cannot always assure the best maintenance performance for IMA. Use of health and usage monitoring systems (HUMS) is new approach to activities that utilize data collection and analysis techniques to help ensure availability, reliability and safety of vehicles (Spitzer, 2006). The present paper introduces a multi-level decision-making approach for design of optimal embedded integrated diagnostic architecture that combines maintenance decisions at the k-levels of system and integration with HUMS mechanisms for achieving efficient system level maintenance and lowering life-cycle cost of IMA. HUMS can be implemented in software or directly on an integrated circuit. The effectiveness of such an approach is investigated through the optimization of embedded HUMS architecture for known reliability and economic dependence during life cycle of IMA. The rest of this paper is organized as follows. In Section II some important works in the area of Built-in-Test embedded system are reviewed. In Section III the main definitions and assumptions are presented. In Section IV a models of multi-level decision-making approach for design of optimal embedded integrated diagnostic architecture are proposed and optimal solution is designed. In Section V the conclusions are presented. 2. Related works Integrated avionics architectures or Integrated Modular Avionics have been developed to create a modular, open, and highly-flexible architecture for digital avionics. Built-in-Test (BIT) is an invaluable component of modular, embedded systems that are used for critical applications such as avionics, mission systems, sensors, and others. BIT provides a level of confidence in the correct operation of each module at both power-up and during normal operation. As more of these critical embedded systems are assembled from off-the-shelf hardware and software components, it is increasingly important to evaluate BIT’s performance and its potential for interaction with software. The avionics systems in commercial aircraft are organized into sub-systems for functional areas such as flight control, engine control, navigation, communication etc. Typical avionics bay consists of specials cabinets with avionics modules (Itier, 2007). The paper (Ott, 2007) has discussed the limitations of the current avionic architectures when dealing with the high level of functionality required by advanced civil aircraft and has formulated the main tasks for avionics designers: • a reduction in overall cost of ownership through reduced spares requirement and equipment removal rate; • a reduction in weight and volume of wiring leading to increased range and payload; • improved built-in-test coverage to provide better maintenance diagnostics, improved fault detection, and reduced unconfirmed removals; • maintenance-free dispatch to achieve quick turnaround times; • resource sharing to reduce line replaceable units count; • standardization at the functional interface to provide hardware and software interoperability (that is, vendor/product independence).


For decision of above mentioned problems the paper (Romain et al., 2012) exposes design process dedicated to facilitate the integration of fault-detection and diagnosis functions in sensor acquisition system in order to improve avionics critical system efficiency. The paper (Zhang et al., 2013) proposes a model-driven design methodology, which assists system engineers to simulate and validate non-functional requirements such as scheduling or resources dimensioning and then propose a method of testing the IMA modules using different instrumentation technologies. The paper (Byington et al., 2003) identifies on-board information sources and automated reasoning techniques that build upon existing BIT results to improve fault isolation accuracy. HUMS is new generation of diagnostics approach in aviation. The (Spitzer, 2006) gives a brief overview of future HUMS technologies and applications and describes three modern HUMS under development for modular avionics systems. The similar Integrated Intelligent Vehicle Management systems (Paris and others, 2005) will provide the framework for manageable vehicle operations and quick response to system failures and space environmental events. Traditionally models of condition-based maintenance (CBM) strategies have been widely developed and successfully applied to single-component systems (Jardine, Lin and Banjevic, 2006; Noortwijk, 2009; Prajapati et al., 2012). However, a multicomponent system in practice is always subject to inherent interactions among the components (i.e., economic, structural, and stochastic dependence) (Nicolai and Dekker, 2008). Therefore, strategies for single-component systems cannot be properly applied to multicomponent ones. Faced with this situation, a few efforts have been recently developed using CBM strategies considering component interactions. One can cite, for example, (Bouvard et al., 2011; Tian and Liao, 2011; Tian et al., 2011) that study strategies with economic dependence, and (Barros et al., 2003; Camci, 2009; Bian and Gebraeel, 2013; Rasmekomen and Parlikad, 2013) that consider strategies with integrating stochastic dependence. All the existing CBM strategies applied to multicomponent systems share the common weak point that maintenance decisions made only at the component level. To remedy this drawback, the paper (Huynh et al., 2015) introduces a two-level decision-making approach that combines maintenance decisions at the system level and the component level. The effectiveness of such an approach is investigated through an n-component deteriorating system with a k-out-of-n: F structure, and economic dependence. Maximizing the effectiveness of HUMS requires that it be designed into early stage of life cycle, rather than added on at some later date. Keeping the proper focus on HUMS is achieved by health management integration, whose ultimate goal is to ensure an optimal HUMS balance across the system, resulting in improved system safety, improved reliability and reduced life cycle costs. One approach to the development of above mentioned HUMS for k-level architecture is described in this article. 3. Definitions and assumptions The following symbols have been used to develop equations for the models: ൌ ͳǡ −Levels of structural modularity of system architecture – Number of modular subsystems ൌ ௜ ǡ ൌ Ͳǡ – Architecture of HUMS with ௜ channels for diagnosis ௣ ሺሻ− cost for the development and production of HUMS with architecture ௢ ሺǡ ሻ− cost for the identification of failures in system with architecture of HUMS during time of system operation ଴ – Channel for diagnosis of system in general − Channels for diagnosis of subsystems ௜ ǡ ൌ ͳǡ

௜ ǡ ൌ Ͳǡ – Cost of channel development and production for diagnosis of -th element – Diagnostic system cost, if it has a channel for the diagnosis of -th element ௜ ǡ ൌ Ͳǡ - The average cost of diagnosis of one failure by HUMS with architecture – Number of failures in the system during life cycle ௜ − Failure Rate of element of system ൌ ∑௡௜ୀଵ ௜ – Failure rate of subsystem with ൌ ͳǡ ǥ elements In this work, we apply multi-level CBM decision-making to a system with modular hardware and software structure of identical or non-identical deteriorating components. The system has multilevel structure submitted by several levels of k division reflecting the correspondent depth of the search of the defects and failures: for example,

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level k = 0 – level of system in general; k = 1 – level of subsystems; k = 2 – levels of modules; k = 3 – levels of submodules, etc. (Fig. 1).

Fig. 1. Multilevel modular architecture of system.

The efficiency of HUMS for above mentioned system is assessed by the total cost of life cycle , , .

(1)

The problem of embedded architecture design for integrated diagnostics we formulate as development of HUMS with optimal architecture Uopt with minimal total cost during life cycleT0: , , | .

(2)

4. Model formulation and solution Let us examine the subsystem on the lowest structural level of the system, which consist of elements. The diagnosis of the technical condition of the examined subsystem and its elements could be performed with HUMS, which can have ∈ , 0,1, … diagnostics channels (DC). Diagnostics with DC to determine the defects arising in any of n elements of the subsystem without indicating certain defect element, and the diagnostics with the , 1, DC can determine the defect arising only in element of the subsystem. The architecture of such HUMS is shown in Fig.2. There are three alternative HUMS architecture for the examined subsystem: 1. HUMS with general control and diagnostic system (GCDS), which use only one DC for failures detecting of all elements of subsystem. The HUMS architecture is ∈ . 2. HUMS with separate control and diagnostic system (SCDS), which use individual DC for all 1, … elements of subsystem. The HUMS architecture is ∈ , 1, … . 3. HUMS with mixed control and diagnostic system (MCDS), which use 1 DC, where individual DC are used for the identification of the technical condition of elements, and the DC is used for the identification of the technical condition of the rest elements. The HUMS architecture is ∈ , 0,1, … , . For any of above mentioned HUMS structures we can use common expressions for the cost of development and production of HUMS ∑∈ and cost for the identification of failures in system with architecture of HUMS during time of operation , . Cost for the diagnostics of one failure of the system depends on HUMS architecture. The detection of the failure on the level of element allows avoiding considerably more cost during the detection of the failure on the level

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of the subsystem in general. Practical experience of HUMS design shows that for modular constructions ! ! for all individual DC 1, … , and ! "! for DC for failures detecting of all elements of subsystem. Empiric coefficient " characterizes the increase in the cost of failure diagnosis in the implementation of the HUMS at a higher level of structural hierarchy. For microelectronics this coefficient is " # 10 (Freise, 2003).

Fig. 2. Architecture of HUMS diagnostics channels.

With taking into account the made remarks the expression for the determination of costs for the diagnostics of one failure in the process of operation of the examined subsystem could be submitted in the following form: !$% 1 %"&, ∑೘

where % ೔సభ ೔ − is a coefficient of completeness of subsystem diagnostics with the individual diagnostics channels. In particular case for the structure of HUMS with general control and diagnostic system % 0 the value of "!, and for the structure of HUMS with separate control and diagnostic system % 1 the value of !. For the exponential model of failures '. In this case the expression (1) takes a form , ( ∑ '!$% 1 %"&,

(3)

where ( 1 for and ( 0 for . The graphs of , form a family of lines with an initial value equal to the cost of development and production of SKD chosen architecture: , – GCDS, , – SCDS, , – MCDS. The slope of the lines is determined by specific operating costs (Fig. 4). If only GCDS and SCDS architectures of HUMS are used, their respective feasible application domains are determined by coordinates of the point F. Point F is a crossing point of the straight lines , and , . These lines coorespond to the cost of GSDS and SCDS functioning, respectively. Such coordinates may be determined solving the equation , , , incerting into this equation expressions (3) for GCDS 0, % 0 and for SCDS , % 1 structures of HUMS:

∑೙ ೔సభ ೔ బ

,

(4)

In this case the interval corresponds to the more efficient application of GCDS architecture of HUMS, whereas the interval ) describes the more efficient application of SCDS structure.

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In terms of cost-saving, MCDS structure application may become more efficient only at the condition that the straight line C3 (U,t) reflecting the cost of MCDS functioning, will be passing below the point F; otherwise diapason will be completely missing the intervals where MCDS structure would be seen as more efficient compared to GCDS or SCDS structures. The use of MCDS could be economically more expedient only in case if the straight line ଷ ሺǡ ሻ, reflecting the cost of MCDS functioning, will be passing below the point F. In the opposite case in all range of the area where MCDS structure would be more efficient than GCDS or SCDS will be absent.

Fig. 3. Cost of life cycle with HUMS of different architectures.

The pre-condition of MCDS application’s cost-effectiveness can be estimated solving the inequality ଷ ሺǡ ଶሻ ൏ ଵ ሺǡ ଶ ሻ, incerting into it formulas (3) and (4): ∑௡௜ୀଵ ௜ െ ∑௠ ௜ୀଵ ௜ ൐ ଴ .

(5)

In the particular case of the identical DC with equal reliability of elements and with equal costሺ௜ ൌ ǡ ௜ ൌ ǡ

ൌ ͳǡ ǥ ሻ the condition (5) will be changed into ଴ ൏ Ͳ. The last inequality is impossible for any permitted values of its variables. It leads to the conclusion that MCDS architecture of HUMS is not economically feasible, in the case if the elements are equally reliable and their diagnostic costs are identical. While condition (5) is observed, the maximum economical feasibility will be demonstrated by GCDS for ଴ ൏ ଵ , MCDS for ଵ ൏ ଴ ൏ ଷ , and SCDS for ଴ ൐ ଷ . The values of ଵ and ଷ for the boundaries of economically feasible MCDS architecture area are be determined from the equation ଵ ǡ ଵ ൌ ଷ ሺǡ ଵ ሻ and ଶ ǡ ଷ ൌ ଷ ሺǡ ଷ ሻ: ௡

௡

ଵ ൌ ିଵ ௜ ǡ ଷ ൌ ିଵ ௜ െ ଴ ǡ ௜ୀ௠ାଵ

௜ୀଵ

where ௡

ൌ ሺ െ ͳሻ ௜ Ǥ ௜ୀ௠ାଵ

Using the expression (3) as an objective function (2), the problem of the synthesis of the optimal HUMS architecture ௢௣௧ on the examined k-level of structural representation for specified ଴ of life cycle could be solved

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by the known methods of integral optimization for ଴ ൏ ଶ in the class of GCDS and MCDS architectures, and for ଴ ൐ ଶ in the class of SCDS and MCDS architecture. The optimal structure of HUMS for other subsystems of (k-1)-level is performed in the same manner. For hierarchical structure of system in general (Fig. 1) the problem of synthesis of optimal architecture of HUMS is led to the stepwise recurrent procedure, on each step of which the algorithm of HUMS architecture is realized for each of the hierarchy levels, starting with the lowest one. Numerical example Let us define the optimal HUMS architecture of the system with four subsystems, failure rate and costs for the creation of DC of which are submitted in the Table 1. The term of operation ଴ ൌ ͳͲyears, ൌ ͳǡ ൌ ͳͲ. Table 1. The data for the numerical example. i

0

1

2

3

4

ߣ௜ , 1/year

1

0.1

0.2

0.4

0.3

‫ݖ‬௜ , conv. unit

10

20

30

40

20

Using formula (4), we can determine that ଶ ൌ ͳͳǡͳyears. Since ଶ ൐ ଴ , we should search for the optimal HUMS architecture within the GCDS and MCDS sctructures. For GCDS structure ൌ ሼ଴ ሽ, and in accordance with formula (3), ଵ ǡ ଴ ൌ ͳͳͲ conv. units. For MCDS architecture (using, for example, brute-force search method of optimization) it is possible to determine the optimal structure of diagnostic channels ௢௣௧ ൌ ଴ ǡ ସ , which supplies the smallest cost of system operation ௢௣௧ ǡ ଴ ൌ ͳͲ͵ conv. unit. Thus, the developed HUMS should contain DC for the control of the whole system in general and DC of the control of the forth subsystem that supplies the smallest costs for the fulfilment it functions during the life cycle. 5. Conclusions The present paper introduces a multi-level decision-making approach for design of optimal embedded integrated diagnostic architecture at the early stage of development that combines maintenance decisions at the k-levels of system and integration with built-in-test mechanisms for achieving efficient system level maintenance and lowering life-cycle cost of system operation. The proposed approach can be useful for the express analysis of the effectiveness of the decisions on the use of HUMS. This approach can be implemented in software or directly in hardware of integrated circuits. The effectiveness of such an approach is investigated through the optimization of embedded architecture of diagnostics for known reliability and economic dependence during life cycle of the system.

Acknowledgements This work was supported by Latvian state research programme project “The Next Generation of Information and Communication Technologies (Next IT)” (2014-2017).

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