Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques

In this paper, we consider a model for the dynamic multiple-fault diagnosis (DMFD) problem arising in online monitoring of complex systems and present a solution. This problem involves real-time inference of the most likely set of faults and their time-evolution based on blocks of unreliable test outcomes over time. In the DMFD problem, there is a finite set of mutually independent fault states, and a finite set of sensors (tests) is used to monitor their status. We model the dependence of test outcomes on the fault states via the traditional D-matrix (fault dictionary). The tests are imperfect in the sense that they can have missed detections, false alarms, or may be available asynchronously. Based on the imperfect observations over time, the problem is to identify the most likely evolution of fault states over time. The DMFD problem is an intractable NP-hard combinatorial optimization problem. Consequently, we decompose the DMFD problem into a series of decoupled subproblems, one for each sample epoch. For a single-epoch MFD, we develop a fast and high-quality deterministic simulated annealing method. Based on the sequential inferences, a local search-and-update scheme is applied to further improve the solution. Finally, we discuss how the method can be extended to dependent faults.

SUMMARYIn wireless communication, Viterbi decoding algorithm (VDA) is the one of most popular channel decoding algorithms, which is widely used in WLAN, WiMAX, or 3G communications. However, the throughput of Viterbi decoder is constrained by the convolutional characteristic. Recently, the three‐point VDA (TVDA) was proposed to solve this problem. In TVDA, the whole procedure can be divided into three phases, the forward, trace‐back, and decoding phases. In this paper, we analyze the parallelism of TVDA and propose parallel TVDA on the multi‐core CPU, graphics processing unit (GPU), and field programmable gate array (FPGA). We demonstrate approaches that fully exploit its performance potential on CPU, GPU, and FPGA computing platforms. For CPU platforms, we perform two optimization methods, single instruction multiple data and multithreading to gain over 145 × speedup over the naive CPU version on a quad‐core CPU platform. For GPU platforms, we propose the combination of cached memory optimization, coalesced global memory accesses, codeword packing scheme, and asynchronous data transition, achieving the throughput of 404.65 Mbps and 12 × speedup over initial GPU versions on an NVIDIA GeForce GTX580 card and 7 × speedup over Intel quad‐core CPU i5‐2300, under the same manufacturing year and both with fully optimized schemes. In addition, for FPGA platforms, we customize a radix‐4 pipelined architecture for the TVDA in a 45‐nm FPGA chip from Xilinx (XC6VLX760). Under 209.15‐MHz clock rate, it achieves a throughput of 418.30 Mbps. Finally, we also discuss the performance evaluation and efficiency comparison of different flexible architectures for real‐time Viterbi decoding in terms of the decoding throughput, power consumption, optimization schemes, programming costs, and price costs.Copyright © 2013 John Wiley & Sons, Ltd.

Underground Electro Magnetic Interference (EMI) has become so serious that there were false alarms in monitoring system, which induced troubles of coal mine safety in production. In order to overcome difficulties caused by the explosion-proof enclosure of the equipments and the limitation of multiple startup and stop in transient process during EMI measurement, a novel technique was proposed to measure underground EMI distri- bution indirectly and enhance Electromagnetic Campatibility(EMC) of the monitoring sys- tem. The wavelet time-frequency analysis was introduced to underground monitoring sys- tem. Therefore, the sources, the startup time, duration and waveform of EMI could be as- certained correctly based on running records of underground electric equipments. The electrical fast transient/burst (EFT/B) was studied to verify the validity of wavelet analysis. EMI filter was improved in accordance of the EMI distribution gotten from wavelet analysis. Power port immunity was developed obviously. In addition, the method of setting wavelet thresholds was amended based upon conventional thresholds in the wavelet filter design. Therefore the EFT/B of data port was restrained markedly with the wavelet filtering. Coor- dinative effect of EMI power and wavelet filter makes false alarms of monitoring system reduce evidently. It is concluded that wavelet analysis and the improved EMI filter have enhanced the EMC of monitoring system obviously.

Multiscalets in the multiwavelet family are used as the basis and testing functions in Galerkin's method. Since the multiscalets are orthogonal to their translations under the Sobolev inner product, the resulting Galerkin's method behaves like a collocation method but possesses the ability of derivative tracking for unknown functions in solving integral equations. The former makes the method simple in implementation and the latter allows to use coarse meshes in discretization. These robust features have been demonstrated in solving two‐dimensional (2D) electromagnetic (EM) problems, but have not been exploited in three‐dimensional (3D) scenarios. For 3D problems, the unknown functions in the integral equations are dependent on two coordinate variables. In order to preserve the use of coarse meshes for 3D cases, we realize the omnidirectional derivative tracking by tracking the directional derivatives along two orthogonal directions, or equivalently tracking the gradient. This process yields a nonsquare matrix equation and we use the least‐squares method (LSM) to solve it. Numerical examples show that the multiscalet‐based Galerkin's method is also robust in solving for 3D EM integral equations with a minor cost increase from LSM. Copyright © 2007 John Wiley & Sons, Ltd.

Four-Dimensional 8PSK Trellis-Coded Modulation (4D-8PSK-TCM) is recommended as an efficient coded modulation standard for the Earth Exploration Satellite Service (EESS) 8025-8400 MHz band by the Consultative Committee for Space Data System (CCSDS). The advantage of 4D-8PSK-TCM is that coding gains can be achieved without expanding the bandwidth relative to the un-coded signal set. In this paper, the 4D-8PSK-TCM system with the modulation efficiency of 2.5 bit/symbol and its Viterbi decoding are presented and the Viterbi decoding algorithm is implemented on Matlab Simulink. During the algorithm implementation, an auxiliary trellis is introduced to reduce greatly the number of computations of metrics of each path. In this way, there are only 64 comparisons to compute the metrics in comparing with the 2032 comparisons required in a directly computing approach.

The European Space Agency (ESA) successfully launched the Soil Moisture and Ocean Salinity (SMOS) mission in November 2, 2009. SMOS uses a new type of instrument, a synthetic aperture radiometer named MIRAS that provides full-polarimetric multi-angular L-band brightness temperatures, from which regular and global maps of Sea Surface Salinity (SSS) and Soil Moisture (SM) are generated. Although SMOS operates in a restricted band (1400–1427 MHz), radio-frequency interference (RFI) appears in SMOS imagery in many areas of the world, and it is an important issue to be addressed for quality SSS and SM retrievals. The impact on SMOS imagery of a sinusoidal RFI source is reviewed, and the problem is illustrated with actual RFI encountered by SMOS. Two RFI detection and mitigation algorithms are developed (dual-polarization and full-polarimetric modes), the performance of the second one has been quantitatively evaluated in terms of probability of detection and false alarm (using a synthetic test scene), and results presented using real dual-polarization and full-polarimetric SMOS imagery. Finally, a statistical analysis of more than 13,000 L1b snap-shots is presented and discussed.

In this paper we present and compare different fault diagnosis algorithms using space space models for nonlinear dynamic systems. Most fault diagnosis and isolation algorithms for dynamic systems, which can be modeled using a set of state space equations, have relied on the system being linear and the noise and disturbances being Gaussian. In such cases, optimal filtering ideas based on Kalman filtering are utilized in estimation followed by a residual analysis, for which whiteness tests are typically carried out. Linearized approximations (e.g., Extended Kalman filters) have been used in the nonlinear dynamic systems case. However, linearization techniques, being approximate, tend to suffer from poor detection or high false alarm rates. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distribution of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution. The particle filter is combined with the innovation-based fault detection techniques to develop a fault detection and isolation scheme. The advantage of particle filters is that they are capable of handling any functional nonlinearity and system or measurement noise of any distribution. An improvement on using a single Extended Kalman filter matched to a particular model is to use the the Interacting Multiple Model estimator, which consists of a number of EKFs running in parallel. Such a multiple model estimator can handle the abrupt changes in the system dynamics, which is essential for fault diagnosis. Here, we compare the fault detection performances of these algorithms on different nonlinear systems.

Distributed self fault diagnosis algorithm for large scale wireless sensor networks using modified three sigma edit test

Automatic patient monitoring is an essential resource in hospitals for a good health care management. While alarms due to abnormal physiological conditions are important to deliver fast treatment, it can be also a source of unnecessary noise due to false alarms caused by electromagnetic interference or motion artifacts. This condition leads to stress, sleep disorders in patients and desensitization in staff. One significant source of false alarms are those related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. Other types of cardiac arrhythmia alarms also relies on a good detection of the HR. In order to avoid false alarms, it is important to create systems for reliable heart rate calculation. In this paper, the fusion of different physiological sensors is used o create a robust heart rate estimation. The algorithm uses a bayesian approach to fuse information from electrocardiogram, arterial blood pressure and photoplethysmogram. To validate this system, it was used twenty selected recordings from MIMIC database. A white gaussian noise was added in each waveform to simulate the worst case scenario. The proposed algorithm was compared with two other techniques (HRV index and majority voter) in addition to the individual analysis of each source. Results show that this bayesian fusion presents the lower error of 23%, while the other evaluated techniques presents an error rate of 35% (ECG Only), 40% (ABP Only), 41% (PPG Only), 37% (HRV Index) and 31% (Majority Voter). Therefore, the system shows good performance and requires simple computations, so it is very useful for real-time applications.

Modelling of the cooperative spectrum sensing in cognitive radio networks in presence of correlated multipath-fading is an important issue. It deals with complex dependencies of large number of random variables. Earlier developments on decision fusion consider correlated local decisions, correlated shadowing, spatial correlation and independent fading channels. This paper presents likelihood ratio test (LRT) based new fusion rule using normal factor graph (NFG) and Sum-Product-Algorithm (SPA) model for cooperative spectrum sensing in presence of correlated multipath-fading channels. The proposed approach requires exact channel statistics instead of instantaneous channel state information (CSI). It leads to Neyman-Pearson (N-P) criteria based optimal sensing. We assume SUs are aligned in a linear fashion and each channel is correlated only with its adjacent channels with identical fading correlation coefficients. In this respect, we derive a new tri-variate Rayleigh probability density function using Miller's [1] approach. Moreover, closed form solutions for local probabilities of detection and false alarm are also derived by assuming that all secondary users employ energy detection. The decision fusion is performed using SPA as message passing strategy over the NFG. Simulation results are provided to validate the performance of the proposed approach.

This paper focuses on the time-variant radio frequency interference (RFI) issue that ultra-wide band (UWB) through-wall radar (TWR) is faced with, and presents an iterative dual sparse recovery (IDSR) framework to combat it. The framework consists of two stages: 1) RFI estimation and detection and 2) IDSR of scattered echoes from objects and RFI signals. In the first stage, an overlapped short time Fourier transform is employed to construct and update the discrete frequency Doppler spectrum (DFDS). Then, a minimum statistic operation is conducted on the DFDS to estimate RFI signals, followed by detection via a 1-D cell-averaging constant false alarm rate detector to determinate whether RFI signals exist or not. In the second stage, a dual sparse model of the collected signals is set up, based on the fast-time frequency sparsity of RFI signals because of their narrow bands and the Doppler frequency sparsity of scattered echoes from objects because of their limited moving velocities. The alternating direction method of multipliers (ADMM) is introduced to iteratively and alternately recover RFI signals and scattered echoes from objects. Specifically, the iterative hard thresholding (IHT) method is used to complete the two sparse recovery operations. The involved two sparse dictionaries are simple and only made up of inverse discrete Fourier transform basis and independent of the received signals. The IDSR framework is able to relieve the influences of RFI signals and scattered echoes from objects on each other, and thus can reconstruct the two type signals to the maximum limit. Field experiments using a UWB TWR were carried out to verify the proposed method.

There is an increasing demand for converged solution for multi-standard radio processors to support existing and future standards. In this work, heterogeneous multi-processor platform is proposed for multi standard wireless communication system which is programmable and scalable in adapting to future standards. Channel decoding algorithms form important constituent of wireless communication system because of their computational complexity. A programmable radio processor is proposed for channel decoding with application specific instruction accelerators. Viterbi and Turbo channel decoding algorithms are analyzed for computational parallelism in the algorithms and for hardware reusability across the algorithms. Application specific instruction accelerator is designed by exploiting similar characteristics and computational parallelism across the algorithms. The analysis shows that the throughput of 54Mbps for UWB Viterbi Decoder and 12 Mbps for UMTS Turbo Decoder at 91.7MHz can be achieved using the proposed design.

This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the multipath components (MPCs) for the estimation of the agent position without using any prior information such as floorplan information or training data. Using simulated and real measurements in different channel conditions, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB), facilitating the additional information contained in the presented FA model. The algorithm is shown to provide robust estimates in both, dense multipath channels as well as channels showing specular, resolved MPCs, significantly outperforming state-of-the-art radio-based localization methods.

The constant false alarm rate (CFAR) detectors are well studied for ship detection in SAR images, which suffer performance degradation due to the capture effect from interfering outliers, such as nearby targets, sidelobes and ghosts in multi-target environments. To address this issue, the clutter truncation scheme is adopted to reduce the outlier contamination in clutter samples such that the accuracy of clutter modeling can be improved. However, the selection of clutter truncation depth is difficult, which often resorts to sensitivity study. In this paper, the complex signal kurtosis (CSK) is first utilized as a statistical indicator for the decision of truncation depth to guarantee that the true clutter samples are maintained. Besides, a coarse-to-fine detection process is designed, including global superpixel proposal with the CSK and local identification of target pixels with the superpixel-level CFAR detector based on truncated statistics. During the local CFAR detection stage, the segmented superpixels provide convenient sample indexing for the iterative clutter truncation processing. The elevated performance achieved by the proposed method mainly benefits from the schemes of two-stage detection and automatic clutter truncation, yielding the increased detection efficiency and accuracy at the same time. Besides, false alarms caused by radio frequency interference can be reduced. In the experiment, the comparative results with state-of-the-art methods based on the Sentinel-1 and Gaofen-3 SAR data validate the performance of the proposed method.

Recent studies have shown that complex information in single-channel synthetic aperture radar (SAR) imagery has practically always been underrated. This improves the perception of their potential for ocean monitoring. Based on the in-depth interpretation of complex signal kurtosis (CSK), this paper proposes a new ship detection method based on CSK in single-channel SAR imagery. The proposed method consists of two main parts, i.e., region proposal and target identification. The basic idea is to first detect potential ship locations based on the region proposal. Then, the final ship target is acquired based on the target identification. Compared to conventional methods based on detected products, e.g., the constant false alarm rate (CFAR), the proposed method has three advantages. First, CSK can take advantage of both non-Gaussianity and noncircularity, which is the fundamental concept distinguishing complex signal analysis from the real case. Second, the proposed method can be intrinsically free of false alarms caused by radio frequency interference (RFI). Finally, the proposed method can avoid missing detection in dense target situations. This methodology has been demonstrated over significant data sets acquired from Sentinel-1, TerraSAR-X, and Gaofen-3. These results validate that CSK is a vital indicator of ship detection. Complex information is expected to play a more important role in single-channel SAR imagery.