Philippe Dague

Modeling and Diagnosing OrchestratedWeb Service Processes

Web service orchestration languages describe executable business processes composed of Web services. A business process can fail for many reasons, such as faulty Web services or mismatching messages. It is important to find out which Web services are responsible for a failed business process because we could penalize these Web services and exclude them from the business process in the future. In this paper, we propose a model-based approach to diagnose orchestrated Web service process. We convert the Web service orchestration language, BPEL4WS, into synchronized automata, so that we have a formal description of the topology and variable dependency of the business process. After an exception is thrown, the diagnoser can calculate the business process execution trajectory based on the formal model and the observed evolution of the business process. The faulty Web services are deduced from the variable dependency on the execution trajectory. We demonstrate our diagnosis technique with an example.

A Decentralized Model-Based Diagnosis for BPEL Services

The paper proposes a decentralized diagnosis approach for a set of choreographed BPEL Web services, where a local diagnoser is associated to each BPEL service and cooperates with a coordinator. The local diagnosis is based on a Colored Petri Nets model enriched with I/O data dependency relations represented with color propagation functions (A preliminary version of centralized local diagnosis has been presented in DX09). By applying the multiset marking calculation equation, a diagnosis inequations system is constructed and solved to retrieve a local diagnosis. The coordinator updates the global diagnosis until reaching a final consistency.

Spacecraft actuator diagnosis with principal component analysis: application to the rendez-vous phase of the mars sample return mission

This paper presents a fault detection and isolation (FDI) approach in order to detect and isolate actuators (thrusters and reaction wheels) faults of an autonomous spacecraft involved in the rendez-vous phase of the Mars Sample Return (MSR) mission. The principal component analysis (PCA) has been adopted to estimate the relationships between the various variables of the process. To ensure the feasibility of the proposed FDI approach, a set of data provided by the industrial high-fidelity simulator of the MSR and representing the opening (resp., the rotation) rates of the spacecraft thrusters (resp., reaction wheels) has been considered. The test results demonstrate that the fault detection and isolation are successfully accomplished.

An Incremental Approach for Pattern Diagnosability in Distributed Discrete Event Systems

Diagnosability is a crucial property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system. Recent work on diagnosability has generalized fault event case to pattern case, which can describe more general objectives for diagnosis problem, but based on global model and global twin plant construction. In this paper, we propose an original framework to solve pattern diagnosability in a distributed way to avoid calculating global objects. We first show how to incrementally accomplish pattern recognition without building global model by propagating only diagnosability relative information between components. Then an efficient way to construct pattern verifier is proposed, which is inspired from the classical twin plant method but with smaller state space, to search for partial critical paths, whose global consistency is subsequently checked. Meanwhile we prove that the result obtained from our distributed approach is on an equality with that from the centralized one but the evaluation result shows that our search state space exploited is only a small subpart of the global twin plant, whose construction is unavoidable in the centralized approach.

A General Algorithm for Pattern Diagnosability of Distributed Discrete Event Systems

Diagnosability is an important system property that determines at design stage how accurate any diagnostic reasoning can be on a partially observed system. A fault in a discrete-event system is diagnosable iff its occurrence can always be deduced from enough observations. It is well known that centralized diagnosability approaches lead to combinatorial explosion of the search space since they assume the existence of a monolithic model of the system. This is why very recently the distributed approaches for diagnosability began to be investigated, relying on local objects. On the other hand, diagnosis objectives are generalized from fault event to fault pattern that can represent multiple faults, repeating fault, sequences of significant events, repair of faults, etc. For pattern case, most existing approaches are centralized. In this paper, we propose a new distributed framework for pattern diagnosability. We first show how to recognize patterns by incrementally constructing local pattern recognizers through extended subsystems. Then we propose a structure called regional pattern verifier that is constructed from the subsystem where the pattern is completely recognized before showing how to abstract just the necessary and sufficient diagnosability information to further save the search space. Then the global consistency checking is based on another local structure called abstracted local twin checker to analyze pattern diagnosability. In this way, we avoid constructing global objects both for pattern recognition and for pattern diagnosability. The correctness of our distributed algorithm is theoretically proved and its efficiency experimentally demonstrated by the results of the implementation.

VPCA-based fault diagnosis of spacecraft reaction wheels

Fault detection and isolation methods based on the Principal Component Analysis (PCA) have been widely used for monitoring complex industrial processes with multiple variables and diagnosing process and sensor faults. This approach has been mainly developed for the analysis of single valued variables without considering any uncertainty in the systems. The main objective of this paper is then to develop a diagnosis process, of the chaser reaction wheels used during the rendezvous phase of the Mars Sample Return (MSR) mission, based on the Vertices Principal Component Analysis (VPCA) as an extension of the classical PCA for interval valued data. An industrial “high-fidelity” simulator of the MSR has provided a set of interval valued data describing the rotation rates of the spacecraft reaction wheels. The results have proven the efficiency of the proposed FDI approach in the diagnosing process.

Spacecraft thrusters diagnosis with vertices principal component analysis: Application to the rendez-vous phase of the mars sample return mission

This paper presents a fault diagnosis system of the chaser thrusters used during the rendez-vous phase of the Mars Sample Return (MSR) mission. The Vertices Principal Component Analysis (VPCA) has been adopted as an extension method of the classical principal component analysis for interval valued data. The VPCA has been used to estimate the relationships between the various variables of the process. To ensure the feasibility of the proposed FDI approach, a set of interval valued data provided by the industrial high-fidelity simulator of the MSR and representing the opening rates of the spacecraft thrusters has been considered. The test results demonstrate that the fault detection and isolation are successfully accomplished.

Smart Distance Keeping: Modeling and Perspectives for Embedded Diagnosis

This paper presents a detailed description of an advanced Adaptive Cruise Control (ACC) system implemented on a Renault-Volvo Trucks vehicle. One of the main differences between this new system, which is called the Smart Distance Keeping (SDK), and the classical ACC is the choice of the safe distance. This later is the distance between the vehicle (with the ACC or the SDK system) and the front obstacle (which may be a vehicle). It is supposed fixe in the case of the ACC, while variable in the case of the SDK. The variation of this distance (in the case of SDK) depends essentially on the relative velocity between the vehicle and the front obstacle. The main contribution of this work is on the SDK system architecture, the design of its environment model, and the proposition of a detection and isolation strategy for some of the possible faults that may be produced on the system.

A Predictability Algorithm for Distributed Discrete Event Systems

Predictability is considered as a crucial system property that determines with certainty the future occurrence of a fault based on a sequence of observations on system model. There are very few works done on the predictability problem for discrete event systems, which is however extremely important for developing critical complex systems. In this paper, we propose a formal sufficient and necessary condition for this property before presenting a new algorithm based on it, which is extendible from a centralized framework to a distributed one. Both are formally presented, as well as experimental results that show the efficiency of our approach.

Model-based decentralized embedded diagnosis inside vehicles: Application to Smart Distance Keeping function

In this paper, the deployment of a fault diagnosis strategy in the Smart Distance Keeping (SDK) system with a decentralized architecture is presented. The SDK system is an advanced version of the Adaptive Cruise Control (ACC) system, implemented in a Renault-Volvo Trucks vehicle. The main goal of this work is to analyze measurements, issued from the SDK elements, in order to detect, to localize and to identify some faults that may be produced. Our main contribution is the proposition of a decentralized approach permitting to carry out an on-line diagnosis without computing the global model and to deploy it on several control units. This paper explains the model-based decentralized solution and its application to the embedded diagnosis of the SDK system inside truck with five control units connected via a CAN-bus using ”Hardware In the Loop” (HIL) technique. We also discuss the constraints that must be fulfilled.