A robust sensorless fault diagnosis algorithm for low cost motor drives

Choi, Seungdeog; Akin, Bilal; Rahimian, Mina M.; Toliyat, H.A.

doi:10.1109/apec.2010.5433507

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…, N 2 . The signal averaging technique in [10] and [15] is modified and elaborately demonstrated for this purpose. The frequency analysis of the averaged signal in (8) can be limited from the Nyquist sampling theorem as follows:…”

Section: B Fine Error Adjustmentmentioning

confidence: 99%

“…The ML scheme is effectively used to blindly search potential errors without any additional information by utilizing the maximum of the periodogram as follows [10], [15], [27]:f…”

Section: ) Ml-based Blind Fine Error Adjustmentmentioning

confidence: 99%

“…As a result, by monitoring and analyzing the irregularities of fault harmonic modulations, the diagnostics have been efficiently performed to determine specific fault conditions. Among the various fault diagnostic methods proposed so far, some techniques keeping the track of the characteristic fault signatures in the spectrum have been suggested with reduced computational complexity and low memory occupancy on a microcontroller such as Park transform-based analysis (PTA) [14], [15], the phase sensitive detection (PSD) [9], the correlation-based detections (CD) [9], [15], and the matched filter (MF)-based techniques [10]. The MF-based theory has provided the fundamental background of those signature-based diagnosis techniques [9], [10], [14], [15] in terms of noise immunization and complexity minimization.…”

mentioning

confidence: 99%

“…However, the MF-based method cannot maintain its optimal effectiveness and its performance reduce inevitably by speed feedback error. The PSD, PTA, and CD techniques in [9] and [15], which utilize the optimal property of the MF theory, are inherently exposed to considerable performance degradation in practical vehicular system application.…”

mentioning

confidence: 99%

See 3 more Smart Citations

A Compact Error Management Algorithm to Minimize False-Alarm Rate of Motor/Generator Faults in (Hybrid) Electric Vehicles

Choi

Akin

Kwak

et al. 2014

IEEE J. Emerg. Sel. Topics Power Electron.

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This paper addresses an emerging reliability and safety requirement for more electric transportation systems by proposing an on-board diagnostics plug-in tool. For this purpose, a compact diagnostic algorithm is presented using an advanced speed feedback error management technique for traction motor and generator faults which is one of the most critical parts of the vehicle powertrain. The speed measurement accuracy can be degraded for various reasons such as: sensor issues, vehicle vibration, hardware tolerances, and environmental impacts like temperature, moisture, and electromagnetic interferences. Although the speed feedback errors within an acceptable level can be tolerated by most of the drive systems, the accuracy of speed measurement is critical for the reliability of fault diagnosis. The major reason behind this fact is the frequency of fault signature in the spectrum is primarily determined using the motor speed. It is shown how the reliability of a motor fault diagnosis decision can significantly be improved under erroneous speed feedback conditions by using the developed effective error localization and coarse-to-fine detection technique. The proposed scheme is implemented through effective strategies to minimize computational complexity and memory occupancy in every step of signal processing according to manufacturers' strict cost limitation.

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Section: B Fine Error Adjustmentmentioning

confidence: 99%

“…The ML scheme is effectively used to blindly search potential errors without any additional information by utilizing the maximum of the periodogram as follows [10], [15], [27]:f…”

Section: ) Ml-based Blind Fine Error Adjustmentmentioning

confidence: 99%