Embedded holonic fault diagnosis of complex transportation systems

doi:10.1016/j.engappai.2012.09.008

Engineering Applications of Artificial Intelligence

Volume 26, Issue 1, January 2013, Pages 227-240

https://doi.org/10.1016/j.engappai.2012.09.008 Get rights and content

Abstract

The use of electronic equipment and embedded computing technologies in modern complex transportation systems continues to grow in a highly competitive market, in which product maintainability and availability is vital. These technological advances also make fault diagnosis and maintenance interventions much more challenging, since these operations require a deep understanding of the entire system. This paper proposes a holonic cooperative fault diagnosis approach, along with a generic architecture, to increase the embedded diagnosis capabilities of complex transportation systems. This concept is applied to the fault diagnosis of door systems of a railway transportation system.

Introduction

To deal with the complexity of modern transportation systems (e.g., commercial aircrafts, trains, ships), an efficient maintenance strategy is essential for maintaining and improving the availability and reliability of assets, while minimizing maintenance and total life-cycle ownership costs (Discenzo et al., 2002). Normatively, maintenance is classified as preventive or corrective maintenance (DIN EN 13306, 2010). While preventive maintenance focuses on reducing the probability of failures by replacing components before they fail, corrective maintenance has the objective of returning an item back to service after a failure occurrence. Before any corrective actions can be taken, one of the most time-consuming step of corrective maintenance is the fault diagnosis procedure, which consists of identifying the faulty components to be repaired (Feldman, 2010, Khol and Bauer, 2010).

Unlike diagnosing complex industrial systems and static machines, several considerations must be taken into account when diagnosing complex transportation systems. In this paper, we assume that complex transportation systems are characterized by the following properties:

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System complexity—a complex transportation system is assumed to be decomposable into a set of interacting sub-systems, composed of a control part and a controlled part. These sub-systems are designed by several suppliers, using computational and physical components, and heterogeneous technologies (e.g., electrical, mechanical, hydraulic, pneumatic, hardware and software parts) (Dievart et al., 2010).
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System variability—the sub-systems may differ from one system to another (e.g., change of suppliers, design changes, product evolutions) (Azarian et al., 2011).
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System environmental context—each sub-system is assumed to have its own context, which can be either physical (e.g., climate impact, vibrations, electromagnetic disturbance) or informational (e.g., functioning mode, component states) (Monnin et al., 2011).
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System maintainability—a complex transportation system is usually linked to a stationary maintenance center and needs to communicate with it (Jianjun et al., 2007). In addition, maintenance operations cannot be executed immediately in the system (Umiliacchi et al., 2011).

To allow the diagnosis of this kind of complex transportation systems, a diagnosis system must be defined. This diagnosis system must not interfere with the normal operation of any sub-system. This no-intrusiveness constraint is mandatory. In this paper, a diagnosis system is assumed to fulfill the following requirements:

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Accuracy—the diagnosis system must be adapted for isolating uniquely the faulty components among various interconnected sub-systems.
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Ease of explanations—the diagnosis system must allow a user to understand how the diagnosis procedure came to the results.
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Adaptability—the diagnosis system must be adapted when changes in components and sub-systems design occur.
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Reactivity—diagnosis results must be delivered in a timely manner to the maintenance center in order to improve the maintenance management.
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Confidence—the diagnosis system must avoid producing false alarms.

Through the continued advances in infotronics and in communication technologies (Clarhaut et al., 2011), intelligent diagnosis systems based in particular on artificial intelligence (AI) and multi-agents (MAS) allow the diagnosis procedure to be automated on-line, observing continuously the system (Campos, 2009, Ng and Srinivasan, 2010). In this context, the main contribution of this paper is to propose a fault diagnosis approach that supports all the previously introduced assumptions. This approach is applied within a joint research-industry project, called SURFER (SURveillance active FERroviaire, translated as “active train monitoring”), led by Bombardier-Transport. The aim of the SURFER project is to provide a more advanced solution for the on-line diagnosis of incipient failures and faults that can occur during the train service, besides the existing ORBITA system developed by Bombardier-Transport in 2006 (Orbita-BT, 2006).

This paper is organized as follows. Section 2 presents a literature review about condition monitoring and diagnosis standards, along with the main diagnosis methods. This section highlights the limits of currently-used diagnosis approaches, emphasizing the need of a robust embedded diagnosis. Section 3 proposes an embedded decentralized cooperative fault diagnosis approach, based on a generic holonic model. Section 4 applies the proposed embedded diagnosis model for advanced fault diagnosis of train door systems, within the context of the SURFER project. Section 5 presents the experimental platform used for the implementation of the holonic diagnosis architecture. Section 6 exhibits the first results obtained in the implementation in a real train. Finally, Section 7 offers conclusions and perspectives for future research.

Section snippets

Condition monitoring for diagnosing transportation systems

A condition monitoring system involves raw data acquisition, processing, analysis and interpretation of faults to provide useful maintenance information (Campos, 2009). In this section, we refer to condition monitoring as a means of applying on-line fault diagnosis procedure to a complex transportation system, focusing on diagnosing abrupt faults rather than diagnosing incipient faults or performance degradation. Then, the literature is surveyed on condition monitoring, diagnosis standards, and

Proposed approach for the embedded fault diagnosis of complex transportation systems

This section describes the proposed generic approach for diagnosing complex transportation systems, by combining the EDCD architecture and the FPR method. The holonic view clarifies and expands the usual systemic framework (Koestler, 1967). Its major advantages are:

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a holon is capable of autonomous decision-making in relation to its objectives and cooperation with other holons (this is one way towards an autonomous, cooperative diagnosis).
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a complete holon is composed of physical and

Applying the holonic approach to a railway transportation system

The following sections illustrate the corresponding instantiation in the passenger access function, which is a safety critical function (Umiliacchi et al., 2011).

Experimentation platform

The EMAPS platform will allow the holonic diagnosis approach to be validated before the final embedding in the trains. This platform is intended to:

•
develop the various configurations depending on the architectures proposed (i.e., mapping of monitoring and diagnosis functions to hardware systems),
•
validate the internal algorithms of the embedded holons, which compose the train diagnosis system,
•
assess the appropriateness and reliability of fault diagnosis processes obtained from the cooperation of

The implementation in a real train, first results and lessons learned

Low-level diagnosis functions (e.g., monitoring layers) were implemented and embedded in real trains to monitor train signals continuously. Along with information about the system's operating context, this monitoring system stores the raw data measurements on-board as a collection of log files. These log files provide useful support for understanding the faults that occurred while the train was in service, especially for the door sub-systems. The log files analysis demonstrated that

Conclusion and future works

In this paper, a holonic cooperative approach for diagnosing complex transportation systems was proposed. The main contribution of this proposal is the combination of a holonic cooperative diagnosis architecture with a FPR method for the embedded fault diagnosis. The proposed holonic architecture is based on the decomposition of a complex system into sub-systems and is composed of recursive diagnosis holons. At each level in the decomposition, each holon is characterized by its system to be

Acknowledgments

The project presented in this paper is led by Bombardier-Transport in collaboration with PROSYST, HIOLLE Industries Group, Université de Valenciennes et du Hainaut-Cambrésis (UVHC) and Institut français des sciences et technologies des transports, de l'aménagement et des réseaux (IFSTTAR). This research is supported financially by French Inter-ministerial Funds (FUI) and the Nord/Pas-de-Calais Region, and sponsored by the i-Trans and Advancity competitiveness clusters. The authors gratefully

References (55)

A. Azarian et al.
A new strategy for automotive off-board diagnosis based on a meta-heuristic engine
Eng. Appl. Artif. Intell.
(2011)
L. Chittaro et al.
Diagnosis of multiple faults with flow-based functional models: the functional diagnosis with efforts and flows approach
Reliab. Eng. Syst. Saf.
(1999)
J. De Kleer et al.
Diagnosing multiple faults
Artif. Intell.
(1987)
S. Ferreiro et al.
Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept
Expert Syst. Appl.: Int. J. Arch.
(2012)
L.V. Ganyun et al.
Fault diagnosis of power transformer based on multi-layer SVM classifier
Electr. Power Syst. Res.
(2005)
C. Jianjun et al.
Decentralized and overall condition monitoring system for large-scale mobile and complex equipment
J. Syst. Eng. Electron.
(2007)
J. Li et al.
Research on the algorithm of avionic device fault diagnosis based on fuzzy expert system
Chin. J. Aeronaut.
(2007)
M. Rao et al.
Integrated distributed intelligent system architecture for incidents monitoring and diagnosis
Comput. Ind.
(1998)
L. Ribeiro et al.
Re-thinking diagnosis for future automation systems: an analysis of current diagnostic practices and their applicability in emerging IT based production paradigms
Comput. Ind.
(2011)
Z. Simeu-Abazi et al.
Fault diagnosis for discrete event systems: modelling and verification
Reliab. Syst. Saf.
(2010)

D. Trentesaux

Distributed control of production systems

Eng. Appl. Artif. Intell.

(2009)

V. Venkatasubramanian et al.

A review of process fault detection and diagnosis part II: qualitative models and search strategies

Comput. Chem. Eng.

(2003)

V. Venkatasubramanian et al.

A review of process fault detection and diagnosis part III: Process history based methods

Comput. Chem. Eng.

(2003)

C. Vong et al.

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Eng. Appl. Artif. Intell.

(2011)

A. Widodo et al.

Support vector machine in machine condition monitoring and fault diagnosis

Mech. Syst. Signal Proc.

(2007)

O. Zaki et al.

Detecting faults in heterogeneous and dynamic systems using DSP and an agent-based architecture

Eng. App. Artif. Intell.

(2007)

J. Alanen et al.

Diagnostics of mobile work machines

VTT Res. Notes

(2006)

F.L. Bellifemine et al.

Developing Multi-Agent Systems with JADE

(2007)

O. Benedettini et al.

State-of-the-art in integrated vehicle health management

Proc. IMechE, Part G: J. Aerosp. Eng.

(2009)

Bengtsson, M., 2003. Condition Based Maintenance On Rail Vehicles, Possibilities for a more effective maintenance...

Berenji, H., Wang, Y., Saxena, A., 2005. Dynamic Case-Based Reasoning in Fault Diagnosis and Prognosis. IEEE...

A. Brooks

A robust control system for a mobile robot

IEEE J. Robotics Autom.

(1986)

Byington, C., Kalgren, P., Johns, R., Beers, R., 2003. Embedded diagnostic/prognostic reasoning and information...

J. Campos

Development in the application of ICT in condition monitoring and maintenance

Comput. Ind.

(2009)

Clarhaut, J., Hayat, S., Tahiri, H., 2011. Integration of communication systems for a railroad smart wagon, In...

Cordier, M.O., Dague, P., Dumas, M., Lévy, F., Montmain, J., 2000. A comparative analysis of AI and control theory...

Dievart, M., Charbonneau, P., Desforges, X., 2010. An embedded distributed tool for transportation systems health...

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^☆: This paper was selected from the proceedings of the 4th International Conference on Industrial Engineering and Systems Management IESM 2011.

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Embedded holonic fault diagnosis of complex transportation systems☆

Abstract

Introduction

Section snippets

Condition monitoring for diagnosing transportation systems

Proposed approach for the embedded fault diagnosis of complex transportation systems

Applying the holonic approach to a railway transportation system

Experimentation platform

The implementation in a real train, first results and lessons learned

Conclusion and future works

Acknowledgments

Eng. Appl. Artif. Intell.

Reliab. Eng. Syst. Saf.

Artif. Intell.

Expert Syst. Appl.: Int. J. Arch.

Electr. Power Syst. Res.

J. Syst. Eng. Electron.

Chin. J. Aeronaut.

Comput. Ind.

Comput. Ind.

Reliab. Syst. Saf.

Eng. Appl. Artif. Intell.

Comput. Chem. Eng.

Comput. Chem. Eng.

Eng. Appl. Artif. Intell.

Mech. Syst. Signal Proc.

Eng. App. Artif. Intell.

Diagnostics of mobile work machines

VTT Res. Notes

Developing Multi-Agent Systems with JADE

State-of-the-art in integrated vehicle health management

Proc. IMechE, Part G: J. Aerosp. Eng.

A robust control system for a mobile robot

IEEE J. Robotics Autom.

Development in the application of ICT in condition monitoring and maintenance

Comput. Ind.