Artificial Intelligence
Digital filter design
Fault diagnosis in dynamic systems: theory and application
Fault diagnosis in dynamic systems: theory and application
Kalman filtering: theory and practice
Kalman filtering: theory and practice
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Modelling, State Observation, and Diagnosis of Quantised Systems
Modelling, State Observation, and Diagnosis of Quantised Systems
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
On the specification of multiple models for diagnosis of dynamic systems
AI Communications
HYDES: A Web-based hydro turbine fault diagnosis system
Expert Systems with Applications: An International Journal
Fault detection on robot manipulators using artificial neural networks
Robotics and Computer-Integrated Manufacturing
A hybrid intelligent system for generic decision for PID controllers design in open-loop
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
A hierarchical multiple-model approach for detection and isolation of robotic actuator faults
Robotics and Autonomous Systems
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
IMS 10-Validation of a co-evolving diagnostic algorithm for evolvable production systems
Engineering Applications of Artificial Intelligence
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This paper presents a model-based approach to online robotic fault diagnosis: First Priority Diagnostic Engine (FPDE). The first principle of FPDE is that a robot is assumed to work well as long as its key variables are within an acceptable range. FPDE consists of four modules: the bounds generator, interval filter, component-based fault reasoner (core of FPDE) and fault reaction. The bounds generator calculates bounds of robot parameters based on interval computation and manufacturing standards. The interval filter provides characteristic values in each predetermined interval to denote corresponding faults. The core of FPDE carries out a two-stage diagnostic process: first it detects whether a robot is faulty by checking the relevant parameters of its end-effector, if a fault is detected it then narrows down the fault at the component level. FPDE can identify single and multiple faults by the introduction of characteristic values. Fault reaction provides an interface to invoke emergency operation or tolerant control, even possibly system reconfiguration. The paper ends with a presentation of simulation results and discussion of a case study.