This paper presents a real-time approach to the detection, isolation, and prediction of componentfailures in large-scale systems through the combination of two modules. The modules themselves are then used in conjunc...
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ISBN:
(纸本)0780381556
This paper presents a real-time approach to the detection, isolation, and prediction of componentfailures in large-scale systems through the combination of two modules. The modules themselves are then used in conjunction with an inference engine, TEAMS-RT, which is part of Qualtech Systems Integrated Diagnostic Toolset, to provide the end user with accurate diagnostic and prognostic information about the state of the system. The first module is a filter used to "clean" observed test results from multiple sensors from system noise. The sensors have false alarm and missed detection probabilities that are not known a-priori, and must be estimated - ideally along with the accuracies of these estimates - online, within the inference engine. Further, recognizing a practical concern in most real systems, a sparsely instantiated observation vector must not be problematic. Multiple Hypothesis Tracking (MBT) is at the heart of the filtering algorithm and Beta prior distributions are applied to the sensor errors. The second module is a prognostic engine that uses an interacting multiple model (IMM) approach to track the "trajectory" of degrading sensors. Kalman filters estimate the movement in each dimension of the sensors. The current state and trajectory of each sensor is then used to predict the time to failure value, i.e., when the component corresponding to the sensor is no longer usable. The modules are integrated together and as part of the TEAMS-RT suite;logic is presented for the cases that they disagree.
This paper investigates Fault-Tolerant Control for closed-loop systems where only coarse models are available and there is lack of actuator and sensor redundancies. The problem is approached in the form of a typical s...
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ISBN:
(纸本)9781479977871
This paper investigates Fault-Tolerant Control for closed-loop systems where only coarse models are available and there is lack of actuator and sensor redundancies. The problem is approached in the form of a typical servomotor in closed-loop. A linear model is extracted from input/output data to describe the system over a frequency range. Two methods based on the Kalman Filter and Statistical Change detection techniques are proposed for detecting degradation faults and componentfailures, respectively. Finally, a reference correction setup is used to compensate for degradation faults.
This paper presents an approach of using adaptive observers based on orthogonal series representations for the detection of component faults in dynamic systems. By applying the technique of the orthonogal expansion ap...
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This paper presents an approach of using adaptive observers based on orthogonal series representations for the detection of component faults in dynamic systems. By applying the technique of the orthonogal expansion approximation a dynamic system is represented in terms of a set of linear algebraic equations. Based on these equations a recursive algorithm is derived, which estimates the coefficients of the orthogonal expansion of the system states and delivers the information about the behaviours of the components. The proposed algorithm is computationally simple and especially suitable for the on-line component fault detection. A numerical example is included to illustrate the application of the approach
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