Diagnosis with Incomplete Models: Diagnosing Hidden Interaction Faults
نویسندگان
چکیده
This paper extends model-based diagnosis (MBD) (de Kleer and Williams 1987; Reiter 1987) to systems with hidden interaction faults. An interaction fault is present if an interaction among a set of components leads to an observable failure, even though each individual component individually meets the specifications. A naive approach to address interaction faults is to simply account for all possible interaction faults in the system model. However, the naive approach presumes that all possible faults, both component and interaction faults, are known and addressed in the model. This assumption is violated by most real world systems, such as shorts in circuits (Davis 1984) or unmodeled connections (de Kleer 2007). That leads to incomplete system models, hence possibly hidden interaction faults. The problem of hidden interactions has been known for a long time (Davis 1984), but until now no general solution has been proposed. Instead of pushing for complete models (Preist and Welham 1990) or relying on additional structural information (Davis 1984; Bottcher 1995; de Kleer 2007) we approach the challenge differently. We allow system models to be incomplete and introduce a general, domain independent extension to model-based diagnosis to account for resulting hidden interaction faults. This extends model-based diagnosis to systems with incomplete models, in particular to models with incomplete structural information. In the paper, we demonstrate the proposed diagnosis framework on a logic circuit with a hidden interaction fault.
منابع مشابه
Diagnosing Component Interaction Errors from Abstract Event Traces
While discrete event systems have been widely applied for diagnosing distributed communicating systems, existing models may not completely satisfy the requirements for the application of fault identification and repair in software systems. This paper presents a model-based diagnosis approach that identifies possible faults based on generic fault models in abstract traces where events may be ass...
متن کاملHierarchical Reasoning about Faults in Cyber-Physical Energy Systems using Temporal Causal Diagrams
The resiliency and reliability of critical cyber physical systems like electrical power grids are of paramount importance. These systems are often equipped with specialized protection devices to detect anomalies and isolate faults in order to arrest failure propagation and protect the healthy parts of the system. However, due to the limited situational awareness and hidden failures the protecti...
متن کاملAdaptive Networks for Fault Diagnosis and Process Control
The use of adaptive (neural) networks for fault diagnosis and process control is explored. Adaptive networks can be used as fault recognition systems, adaptive non-linear process models, and as controllers. Connection strengths representing correlation between inputs (alarms and sensor measurements) and outputs (faults) are made to learn by the network using the Back Propagation Algorithm (BPA)...
متن کاملCombining HMM with A Genetic Algorithm for Fault Diagnosis of Photovoltaic Inverters
The traditional fault diagnosis method for photovoltaic (PV) inverters has had a difficult time meeting the requirements of the current complex systems. The main weakness lies in the study of nonlinear systems, but the diagnosis time is also long, and the accuracy is low. To solve these problems, we use a hidden Markov model (HMM) that has unique advantages in its training model and recognition...
متن کاملMulti-Layered Model Based Diagnosis in Robots
The problem of fault diagnosis in the domains of robotics and autonomous systems are unique and interesting. A hidden internal fault affects not only other hardware components, as in any other machine, but also the different layers of abstraction and control in the sensethink-act cycle that the robot carries. We propose to implement a model-based diagnosis approach by utilizing the robot's cont...
متن کامل