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Article
Peer-Review Record

Saliency-Based Rotor Spatial Position Displacement Self-Sensing for Self-Bearing Machines

Sensors 2022, 22(24), 9663; https://doi.org/10.3390/s22249663
by Ye gu Kang 1,*, Daniel Fernandez 2 and David Diaz Reigosa 2
Reviewer 1:
Reviewer 2:
Sensors 2022, 22(24), 9663; https://doi.org/10.3390/s22249663
Submission received: 31 October 2022 / Revised: 30 November 2022 / Accepted: 1 December 2022 / Published: 9 December 2022
(This article belongs to the Special Issue Advances and Applications of Magnetic Sensors)

Round 1

Reviewer 1 Report

The paper presents a very interesting spatial position estimation technique which is intended to be used with bearingless PM synchronous machine.

The estimation method is based on the machine model so the robustness of the estimation technique towards parameters variation should be evaluated.

Is there any condition required about the machine saliency limits for the developed method to be effective?

As authors state, fHF and VHF should be chosen carefully to minimize the interference between HF injection and the fundamental frequency voltage that produce the necessary torque. However, there is not enough details about this issue in the paper.

The impact of the PWM on the estimation procedure and results should be explained as well.

I suggest you to add the position estimation error graph for both x and y axis.

The paper presents stationary tests only, and the Fig. 13 shows 1ms estimation dynamic for fixed x, and y positions. I suggest you to add experimental results concerning complete test with rotating motor or at least explain how mature is the presented technique to be integrated in sensorless bearingless synchronous machine Drives.

Finally, the conclusion should be improved.

Author Response

Please, see the attached PDF file.

Thank you for your help.

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper presents a xy-position estimation self-sensing technique based on both main- and cross-inductance variation. In my opinion, the content of this article is interesting, but the authors should address the following issues:

1. The existing literature shows that a built neural network can estimate faster and better. In fact, the full text is the idea of soft measurement. Now the popular one is neural network estimation. Why does the author still use traditional methods without considering advanced means such as neural network.

2. How the estimated performance is verified by the author when the load changes. That is to say, more experimental conditions need to be tested to verify the performance.

Author Response

Please, see the attachment.

Thank you.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

None

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