| 1. |
- Akin, Bilal, et al.
(author)
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A Simple Real-Time Fault Signature Monitoring Tool for Motor-Drive-Embedded Fault Diagnosis Systems
- 2011
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In: IEEE Transactions on Industrial Electronics. - 0278-0046 .- 1557-9948. ; 58:5, s. 1990-2001
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Journal article (peer-reviewed)abstract
- The reference frame theory constitutes an essential aspect of electric machine analysis and control. In this study, apart from the conventional applications, it is reported that the reference frame theory approach can successfully be applied to real-time fault diagnosis of electric machinery systems as a powerful toolbox to find the magnitude and phase quantities of fault signatures with good precision as well. The basic idea is to convert the associated fault signature to a dc quantity, followed by the computation of the signals average in the fault reference frame to filter out the rest of the signal harmonics, i.e., its ac components. As a natural consequence of this, neither a notch filter nor a low-pass filter is required to eliminate fundamental component or noise content. Since the incipient fault mechanisms have been studied for a long time, the motor fault signature frequencies and fault models are very well-known. Therefore, ignoring all other components, the proposed method focuses only on certain fault signatures in the current spectrum depending on the examined motor fault. Broken rotor bar and eccentricity faults are experimentally tested online using a TMS320F2812 digital signal processor (DSP) to prove the effectiveness of the proposed method. In this application, only the readily available drive hardware is used without employing additional components such as analog filters, signal conditioning board, external sensors, etc. As the motor drive processing unit, the DSP is utilized both for motor control and fault detection purposes, providing instantaneous fault information. The proposed algorithm processes the measured data in real time to avoid buffering and large-size memory needed in order to enhance the practicability of this method. Due to the short-time convergence capability of the algorithm, the fault status is updated in each second. The immunity of the algorithm against non-ideal cases such as measurement offset errors and phase unbalance is theoretically and experimentally verified. Being a model-independent fault analyzer, this method can be applied to all multiphase and single-phase motors.
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| 2. |
- Ardeshiri, Tohid, et al.
(author)
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Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
- 2015
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In: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 22:12, s. 2450-2454
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Journal article (peer-reviewed)abstract
- We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensional linear systems. The performance of the algorithm is illustrated on a target tracking example.
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| 3. |
- Ardeshiri, Tohid, et al.
(author)
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Bayesian Inference via Approximation of Log-likelihood for Priors in Exponential Family
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Other publication (other academic/artistic)abstract
- In this paper, a Bayesian inference technique based on Taylor series approximation of the logarithm of the likelihood function is presented. The proposed approximation is devised for the case where the prior distribution belongs to the exponential family of distributions. The logarithm of the likelihood function is linearized with respect to the sufficient statistic of the prior distribution in exponential family such that the posterior obtains the same exponential family form as the prior. Similarities between the proposed method and the extended Kalman filter for nonlinear filtering are illustrated. Further, an extended target measurement update for target models where the target extent is represented by a random matrix having an inverse Wishart distribution is derived. The approximate update covers the important case where the spread of measurement is due to the target extent as well as the measurement noise in the sensor.
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| 4. |
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| 5. |
- Ardeshiri, Tohid, et al.
(author)
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Greedy Reduction Algorithms for Mixtures of Exponential Family
- 2015
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In: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 22:6, s. 676-680
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Journal article (peer-reviewed)abstract
- In this letter, we propose a general framework for greedy reduction of mixture densities of exponential family. The performances of the generalized algorithms are illustrated both on an artificial example where randomly generated mixture densities are reduced and on a target tracking scenario where the reduction is carried out in the recursion of a Gaussian inverse Wishart probability hypothesis density (PHD) filter.
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| 6. |
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| 7. |
- Ardeshiri, Tohid, 1980-, et al.
(author)
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On Reduction of Mixtures of the Exponential Family Distributions
- 2013
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Reports (other academic/artistic)abstract
- Many estimation problems require a mixture reduction algorithm with which an increasing number of mixture components are reduced to a tractable level. In this technical report a discussion on dierent aspects of mixture reduction is given followed by a presentation of numerical simulation on reduction of mixture densities where the component density belongs to the exponential family of distributions.
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| 8. |
- Ardeshiri, Tohid, et al.
(author)
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Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances
- 2015
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Reports (other academic/artistic)abstract
- In this technical report, some derivations for the smoother proposed in [1] are presented. More specifically, the derivations for the cyclic iteration needed to solve the variational Bayes smoother for linear state-space models with unknownprocess and measurement noise covariances in [1] are presented. Further, the variational iterations are compared with iterations of the Expectation Maximization (EM) algorithm for smoothing linear state-space models with unknown noise covariances.[1] T. Ardeshiri, E. Özkan, U. Orguner, and F. Gustafsson, ApproximateBayesian smoothing with unknown process and measurement noise covariances, submitted to Signal Processing Letters, 2015.
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| 9. |
- Axelsson, Patrik, 1985-, et al.
(author)
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ML Estimation of Process Noise Variance in Dynamic Systems
- 2010
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Reports (other academic/artistic)abstract
- The performance of a non-linear filter hinges in the end on the accuracy of the assumed non-linear model of the process. In particular, the process noise covariance $Q$ is hard to get by physical modeling and dedicated system identification experiments. We propose a variant of the expectation maximization (EM) algorithm which iteratively estimates the unobserved state sequence and $Q$ based on the observations of the process. The extended Kalman smoother (EKS) is the instrument to find the unobserved state sequence. Our contribution fills a gap in literature, where previously only the linear Kalman smoother and particle smoother have been applied. The algorithm will be important for future industrial robots with more flexible structures, where the particle smoother cannot be applied due to the high state dimension. The proposed method is compared to two alternative methods on a simulated robot.
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| 10. |
- Axelsson, Patrik, 1985-, et al.
(author)
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ML Estimation of Process Noise Variance in Dynamic Systems
- 2011
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In: Proceedings of the 18th IFAC World Congress. - 9783902661937 ; , s. 5609-5614
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Conference paper (peer-reviewed)abstract
- The performance of a non-linear filter hinges in the end on the accuracy of the assumed non-linear model of the process. In particular, the process noise covariance Q is hard to get by physical modeling and dedicated system identification experiments. We propose a variant of the expectation maximization (EM) algorithm which iteratively estimates the unobserved state sequence and Q based on the observations of the process. The extended Kalman smoother (EKS) is the instrument to find the unobserved state sequence. Our contribution fills a gap in literature, where previously only the linear Kalman smoother and particle smoother have been applied. The algorithm will be important for future industrial robots with more flexible structures, where the particle smoother cannot be applied due to the high state dimension. The proposed method is compared to two alternative methods on a simulated robot.
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