A Novel Strong Tracking Fault Prognosis Algorithm

Mathematical Problems in Engineering, Jun 2015

Improving the ability to track abruptly changing states and resolving the degeneracy are two difficult problems to particle filter applied to fault prognosis. In this paper, a novel strong tracking fault prognosis algorithm is proposed to settle the above problems. In the proposed algorithm, the artificial immunity algorithm is first introduced to resolve the degeneracy problem, and then the strong tracking filter is introduced to enhance the ability to track abruptly changing states. The particles are updated by strong tracking filter, and better particles are selected by utilizing the artificial immune algorithm to estimate states. As a result, the degeneracy problem is resolved and the accuracy of the proposed fault prognosis algorithm is improved accordingly. The feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the DTS200 system.

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A Novel Strong Tracking Fault Prognosis Algorithm

A Novel Strong Tracking Fault Prognosis Algorithm Qi Zhang, Wei Jiang, Tian-Mei Li, and Jian-Fei Zheng Unit 302, Xi’an Research Institute of High-Tech, Xi’an 710025, China Received 27 November 2014; Revised 21 December 2014; Accepted 21 December 2014 Academic Editor: Gang Li Copyright © 2015 Qi Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Improving the ability to track abruptly changing states and resolving the degeneracy are two difficult problems to particle filter applied to fault prognosis. In this paper, a novel strong tracking fault prognosis algorithm is proposed to settle the above problems. In the proposed algorithm, the artificial immunity algorithm is first introduced to resolve the degeneracy problem, and then the strong tracking filter is introduced to enhance the ability to track abruptly changing states. The particles are updated by strong tracking filter, and better particles are selected by utilizing the artificial immune algorithm to estimate states. As a result, the degeneracy problem is resolved and the accuracy of the proposed fault prognosis algorithm is improved accordingly. The feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the DTS200 system. 1. Introduction Particle filter (PF) is a leading and powerful algorithm for estimating the states of nonlinear or non-Gaussian systems. The past decades have witnessed a wide range of applications, including target tracking [1–4], data detection [5], modeling [6, 7], price forecasting, and fault detection [8–11]. On the other hand, a great number of investigators are interested in enriching particle filtering framework, and many new particle filters are proposed in recent years [12–15]. In these studies, it is found that resolving the degeneracy problem and improving the ability to track abruptly changing states are two difficult problems to particle filter applied to fault prognosis [16–20]. The degeneracy problem means that most particles are assigned to zero weights. As a result, the performance of the particle filter deteriorates because most computational resource is wasted. It is noted that, however, degeneracy can be reduced by resampling or choosing good importance sampling functions. Along this line of research, many resampling algorithms have been proposed for reducing the degeneracy. In these resampling algorithms, sequential importance resampling (SIR) is the representation which largely copies the particles with larger weights to replace the particles with smaller weights [21, 22]. The degeneracy problem is partially addressed, but the sample impoverishment is not fully concerned. Sample impoverishment means that most particles are the same in the set of particles since the particles with larger weights are largely copied. In this circumstance, choosing good importance sampling functions deserves further studies, and many investigators are interested in this question. For example, extended Kalman filter (EKF) was introduced to propose extended particle filter (EPF) by De Freitas et al. [6], and unscented Kalman filter (UKF) was introduced to propose unscented particle filter (UPF) by van der Merwe et al. [23]. Both EPF and UPF can resolve the degeneracy problem, but they cannot track abruptly changing states due to the disadvantages of EKF and UKF. Strong tracking filter (STF) has good performance for tracking abruptly changing states, and thus it can be used to update particles. In addition, it is known that artificial immunity (AI) can search for the best one from all the range, and thus it can be used to clone and vary particles. Therefore, in this paper STF and AI algorithms are utilized jointly to improve particle filter algorithm. As a result, a novel fault prognosis algorithm based on strong tracking artificial immunity particle filter (STAIPF) is proposed to settle the above discussed problems. In the proposed algorithm, the artificial immunity algorithm is first introduced to resolve the degeneracy problem, and then the strong tracking filter is introduced to enhance the ability to track abruptly changing states. More specifically, the particles are updated by strong tracking filter, and better particles for states estimation are selected by utilizing the artificial immune algorithm to enhance the diversity of samples. Therefore, the degeneracy problem and sample impoverishment are resolved simultaneously, and the accuracy of the proposed fault prognosis algorithm is improved as well. Finally, the feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the DTS200 system. The remainder of this paper is structured as follows. In Section 2, the particle filter is intr (...truncated)


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Qi Zhang, Wei Jiang, Tian-Mei Li, Jian-Fei Zheng. A Novel Strong Tracking Fault Prognosis Algorithm, Mathematical Problems in Engineering, 2015, 2015, DOI: 10.1155/2015/676289