An Optimization-Based Method for Dynamic Multiple Fault Diagnosis Problem

IEEE Trans. Syst., Man, Cybern. A

et al. 2009

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Dynamic multiple fault diagnosis (DMFD) is a challenging and difficult problem due to coupling effects of the states of components and imperfect test outcomes that manifest themselves as missed detections and false alarms. The objective of the DMFD problem is to determine the most likely temporal evolution of fault states, the one that best explains the observed test outcomes over time.Here, we discuss four formulations of the DMFD problem. These include the deterministic situation corresponding to a perfectlyobserved coupled Markov decision processes, to several partiallyobserved factorial hidden Markov models ranging from the case where the imperfect test outcomes are functions of tests only to the case where the test outcomes are functions of faults and tests, as well as the case where the false alarms are associated with the nominal (fault-free) case only. All these formulations are intractable NP-hard combinatorial optimization problems. We solve each of the DMFD problems by decomposing them into separable subproblems, one for each component state sequence.Our solution scheme can be viewed as a two-level coordinated solution framework for the DMFD problem. At the top (coordination) level, we update the Lagrange multipliers (coordination variables, dual variables) using the subgradient method. The top level facilitates coordination among each of the subproblems, and can thus reside in a vehicle-level diagnostic control unit. At the bottom level, we use a dynamic programming technique (specifically, the Viterbi decoding or Max-sum algorithm) to solve each of the subproblems. The key advantage of our approach is that it provides an approximate duality gap, which is a measure of suboptimality of the DMFD solution. Interestingly, the perfectly-observed DMFD problem leads to a dynamic set covering problem, which can be approximately solved via Lagrangian relaxation and Viterbi decoding. Computational results on real-world problems are presented.

Section: Introductionmentioning

confidence: 99%

Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques

IEEE Trans. Syst., Man, Cybern. A

et al. 2009

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“…The procedure of minimizing the upper bound via a subgradient or surrogate subgradient optimization produces a sequence of dual feasible and the concomitant primal feasible solutions to the dynamic fusion problem. Details of the DMFD algorithm, subgradient method and dynamic programming are provided in our previous papers [2], [3].…”

Section: Dynamic Multiple Fault Diagnosis (Dmfd) Problemmentioning

confidence: 99%

“…The dynamic fusion problem is a specific formulation of the dynamic multiple fault diagnosis problem (DMFD) [1]- [3]. In the DMFD problem, the objective is to isolate multiple faults based on test (classifier) outcomes observed over time.…”

Section: Dynamic Multiple Fault Diagnosis (Dmfd) Problemmentioning

confidence: 99%

Dynamic fusion of classifiers for fault diagnosis

2007 IEEE International Conference on Systems, Man and Cybernetics

et al. 2007

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Abstract-This paper considers the problem of temporally fusing classifier outputs to improve the overall diagnostic classification accuracy in safety-critical systems. Here, we discuss dynamic fusion of classifiers which is a special case of the dynamic multiple fault diagnosis (DMFD) problem [1]-[3]. The DMFD problem is formulated as a maximum a posteriori (MAP) configuration problem in tri-partite graphical models, which is NP-hard. A primal-dual optimization framework is applied to solve the MAP problem. Our process for dynamic fusion consists of four key steps: (1) data preprocessing such as noise suppression, data reduction and feature selection using data-driven techniques, (2) error correcting codes to transform the multiclass data into binary classification, (3) fault detection using pattern recognition techniques (support vector machines in this paper), and (4) dynamic fusion of classifiers output labels over time using the DMFD algorithm. An automobile engine data set, simulated under various fault conditions [4], was used to illustrate the fusion process. The results demonstrate that an ensemble of classifiers, when fused over time, reduces the diagnostic error as compared to a single classifier and static fusion of classifiers trained over the entire batch of data. The results for sliding window dynamic fusion are also provided.

“…A near optimal polynomial time algorithm based on Lagrangian relaxation and Viterbi decoding was developed. The details of this technique, termed a primal-dual optimization framework, may be found in [12][13][14].…”

Section: Formally We Represent the Dynamic Fusion Problem As Df={s mentioning

confidence: 99%

“…These were motivated by our previous work on multi-target tracking and distributed M -ary hypothesis testing [10][11] and on dynamic multiple fault diagnosis (DMFD) [12][13], respectively. Our primary focus in this paper is on evaluating how effectively our proposed classifier fusion approaches can reduce the diagnostic errors, as compared to traditional fusion methods and the individual classifiers.…”

mentioning

confidence: 99%

Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems

IEEE Trans. Instrum. Meas.

et al. 2009

Faulty automotive systems significantly degrade the performance and efficiency of vehicles, and oftentimes are the major contributors of vehicle breakdown; they result in large expenditures for repair and maintenance. Therefore, intelligent vehicle health-monitoring schemes are needed for effective fault diagnosis in automotive systems. Previously, we developed a data-driven approach using a data reduction technique, coupled with a variety of classifiers, for fault diagnosis in automotive systems. In this paper, we consider the problem of fusing classifier decisions to reduce diagnostic errors. Specifically, we develop three novel classifier fusion approaches: class-specific Bayesian fusion, joint optimization of fusion center and of individual classifiers, and dynamic fusion. We evaluate the efficacies of these fusion approaches on an automotive engine data. The results demonstrate that the proposed fusion techniques outperform traditional fusion approaches. We also show that learning the parameters of individual classifiers as part of the fusion architecture can provide better classification performance.