Jewon Lee

Demagnetization fault diagnosis method for PMSM of electric vehicle

Permanent magnet synchronous motor (PMSM) is attractive candidate for traction motor of electric vehicle (EV). But several kinds of faults can occur in PMSM system: bearing faults, electrically short/open faults and demagnetization fault, etc. Demagnetization fault means that strengths of permanent magnets (PM) in PMSM significantly decrease. The magnet flux linkage, which is proportional to the strengths of the PMs, can be estimated by means of conventional parameter identification methods and the estimation results can be used for detection of the fault. However, variations of q-d axis inductances and stator resistance can cause inaccurate estimation and failure of demagnetization fault detection . In this paper, demagnetization fault diagnosis method for PMSM subject to the parameter variations has been proposed. This method consists of the following serial processes: estimation of parameters variations, data extraction, the least square method, calculation of upper bounds of estimation error rate and magnet flux linkage, fault decision. This method is robust against estimation errors becuase of the upper bounds. It will be demonstrated in chapter IV.

Demagnetization Fault Diagnosis of a PMSM Based on Structure Analysis of Motor Inductance

A demagnetization fault in a permanent-magnet synchronous machine (PMSM) degrades performance and energy efficiency. The demagnetization fault causes flux-density difference and changes the PMSM inductances. Such varying parameters make it difficult to detect and diagnose the exact severity of a demagnetization fault. In this paper, we suggest a use of structural analysis of the PMSMs to diagnose the demagnetization fault. We can calculate varying inductances and consider the magnetic saturation effect with varying flux linkages of the machine. Combination of the proposed technique with the least-square (LS) method shows better results than the conventional methods in the estimation of flux linkages in the PMSMs with the demagnetization fault. Experimental results demonstrate that the proposed method works well both in the tracking of varying inductances and the diagnosis of the demagnetization fault.

Robust Diagnosis Method Based on Parameter Estimation for an Interturn Short-Circuit Fault in Multipole PMSM under High-Speed Operation

This paper proposes a diagnosis method for a multipole permanent magnet synchronous motor (PMSM) under an interturn short circuit fault. Previous works in this area have suffered from the uncertainties of the PMSM parameters, which can lead to misdiagnosis. The proposed method estimates the q-axis inductance (Lq) of the faulty PMSM to solve this problem. The proposed method also estimates the faulty phase and the value of G, which serves as an index of the severity of the fault. The q-axis current is used to estimate the faulty phase, the values of G and Lq. For this reason, two open-loop observers and an optimization method based on a particle-swarm are implemented. The q-axis current of a healthy PMSM is estimated by the open-loop observer with the parameters of a healthy PMSM. The Lq estimation significantly compensates for the estimation errors in high-speed operation. The experimental results demonstrate that the proposed method can estimate the faulty phase, G, and Lq besides exhibiting robustness against parameter uncertainties.

Localization for a Mobile Node Based on Chrip Spread Spectrum Ranging

In this paper, we propose a localization algorithm for a mobile node based on the IEEE 802.15.4a chirp spread spectrum (CSS). IEEE 802.15.4a is an alternative PHY for IEEE 802.15.4, which is used for wireless sensor networks, and it can be applied to ranging applications. Through CSS ranging, the coordinate of a CSS node can be calculated by using trilateration. This approach is usually used for mobile robot localization. In the case of mobile robot localization, the dynamics of a mobile robot can be modeled as a state-space equation because a robot is an artificial object manufactured by designers. Therefore, the coordinate of a mobile robot can be estimated by using the Kalman filter. However, in the case of localization of a mobile node whose movement cannot be predicted, the dynamics of the mobile node are difficult to model. Therefore, we propose a strategy that regards the un-modeled dynamics as the process noise of the state-space equation. Further, we propose a method for determining the statics of the process noise based on some reasonable assumptions. Through the localization experiment, we verify the proposed method and discuss the results.

Vision-based AGV Parking System

This paper proposes an efficient method to locate the automated guided vehicle (AGV) into a specific parking position using artificial visual landmark and vision-based algorithm. The landmark has comer features and a HSI color arrangement for robustness against illuminant variation. The landmark is attached to left of a parking spot under a crane. For parking, an AGV detects the landmark with CCD camera fixed to the AGV using Harris comer detector and matching descriptors of the comer features. After detecting the landmark, the AGV tracks the landmark using pyramidal Lucas-Kanade feature tracker and a refinement process. Then, the AGV decreases its speed and aligns its longitudinal position with the center of the landmark. The experiments showed the AGV parked accurately at the parking spot with small standard deviation of error under bright illumination and dark illumination.

Observability-Based Selection Criterion for Anchor Nodes in Multiple-Cell Localization

The coverage of localization systems for a mobile robot can be expanded by using a multiple-cell structure. In such a structure, there are a number of anchor nodes installed at several positions whose coordinates are known in advance. Therefore, the mobile robot must select some anchor nodes that are suitable for its localization. As a selection criterion for these anchor nodes, the strength of the received ranging signal has been used in the conventional manner, but this approach is highly dependent on the particular environment. In this paper, we focus on the recently proposed localization model for biased chirp spread spectrum ranging. To apply the model to multiple-cell localization, we propose a selection criterion for anchor nodes that is based upon the observability of the model. The proposed criterion assures that the estimated coordinates by the extended Kalman filter can track the actual position of the mobile robot when it is located at the boundary of two adjacent cells.

Demagnetization fault diagnosis of PMSM based on analytic inductance calculation

Demagnetization fault of Permanent magnet synchronous machine (PMSM) degrades motors performance. As the strength of permanent magnet (PM) decreases, inductance of the PMSM changes since flux density affects electrical steels nonlinear characteristics. It makes difficult to estimate exact flux linkages of the PMSM to diagnose demagnetization fault. In this paper, we suggested analytic inductance prediction considering magnetic saturation effect and varying flux linkages of PMSM, which shows better results in estimation of flux linkages. Finite Element Analysis (FEA) results demonstrate that the proposed method works well.

Localization based on two-stage treatment for dealing with noisy and biased distance measurements

Localization can be performed by trilateration in which the coordinates of a target are calculated by using the coordinates of reference points and the distances between each reference point and the target. Because the distances are measured on the basis of the time-of-flight of various kinds of signals, they contain errors which are the noise and bias. The presence of bias can become a major problem because its magnitude is generally unknown. In this article, we propose an algorithm that combines the Kalman filter (KF) and the least square (LS) algorithm to treat noisy and biased distances measured by chirp spread spectrum ranging defined in IEEE 802.15.4a. By using the KF, we remove the noise in the measured distance; hence, the noise-eliminated distance, which still contains bias, is obtained. The next step consists of the calculation of the target coordinates by using the weighted LS algorithm. This algorithm uses the noise-eliminated distance obtained by using the KF, and the weighting parameters of the algorithm are determined to reduce the effects of bias. To confirm the accuracy of the proposed algorithm, we present the results of indoor localization experiments.

Two-dimensional partial response maximum likelihood at rear for bit-patterned media

Compared with conventional partial response maximum likelihood (PRML), we propose new structure of two-dimensional (2D) PRML for bit-patterned media storage. The proposed PRML at rear shows superior bit error rate performance despite no iteration in 2D soft output Viterbi algorithm.

An algorithm to arbitrate multiple chirp-spread-spectrum nodes for ranging: The three node case

Chirp-spread-spectrum (CSS) defined in IEEE 802.15.4a is available for ranging based on the time-of-flight of electromagnetic signal and it can be applied to localization systems. One of problems of the CSS-based localization system is the collision of signals transmitted from multiple CSS nodes. In this paper, a probabilistic arbitration algorithm is proposed to prevent the signal collision for ranging. By using the algorithm, we can adjust the probability that each node will succeed in ranging. Therefore, the proposed algorithm can assign higher priority to a node which requires frequent coordinate calculations such as a rapidly moving mobile robot. Through simulations and experimental results, we evaluate and discuss the performance of the proposed algorithm.