Intelliquench–Real Time detection of magnet quenches in superconducting accelerator ... · 2020. 8. 5. · ØSuperconducting accelerator magnets must operate at very low temperaturesto

Duc Hoang (Rhodes College); Sujay Kazi (MIT); Nhan Tran, Cristian Boffo, Steve Krave, Vittorio Marinozzi, Stoyan Stoynev (Fermilab). SIST/GEM Final Presentation.8/5/2020

Intelliquench – Real Time detection of magnet quenches in superconducting accelerator magnets.

FERMILAB-SLIDES-20-069-CMS-SCD-TD

I. Overview of magnet quenchesII. Deep neural network for anomaly detectionIII. ResultsIV. Summary & Outlook

Outline

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Ø Superconducting accelerator magnets must operate at very low temperatures to maintain superconductivity (no resistance).

Ø Due to several reasons (mechanical imperfections, conductor motion, …), a specific spot in the magnet heats up.

Ø This causes the magnet to become resistive, and with huge amount of current pumping through, it can be catastrophic.

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Magnet quenches

Wilson et al. Superconduc2ng magnets for accelerators.

🔥

r/CatastrophicFailure

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In 2008, magnet quench occurred in 100 magnets at the LHC at CERN, leading to a loss of approximately six tonnes of liquid helium.

The escaping vapour expanded with explosive force, damaging a total of 53 superconducting magnets (each costs several millions dollar.)

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• We placed 5 acoustic sensors around the magnet to detect abnormal sound signatures.

Acoustic sensors

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• We placed 5 acoustic sensors around the magnet to detect abnormal sound signatures.

Acoustic sensors

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Sensor

Deep Neural Network to detect anomaly in the signal.

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Deep Neural Network

Abnormal sound signals?

• Each input multiplied by a weight.

• Weighted values are summed, Bias is added.

• Non-linear activation function is applied

• Trained by varying the parameters to minimize a loss function (quantifies how many mistakes the network makes)

Deep Neural Networks

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Deep Neural Network Auto-encoder

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SignalsRoot mean square error (RMS)

Large error? Anomaly

Encoder Decoder

Compressed info

📚🙇

🧠

✍📝

Reconstruction loss visualization

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0 is quench timeAnomaly point

Results

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Zoomed in -25s near the quench

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Results – summary

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Ø Magnet quenches are expensive.

Ø We are using Deep Neural Network to detect anomaly sound signals, which hopefully enable us trigger before the quench happens.

Ø We’ve achieved some promising results and will be moving on to verification step on unseen data.

Ø Eventually, we want to have a real-time system deployed on FPGAs to process streaming acoustic data.

Summary & Outlook

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• My supervisor: Dr. Nhan Tran & other collaborators in the superconducting technology division: Sujay, Cristian, Steve, Vittorio, Stoyan.

• The SIST committee & my mentor group. • Other awesome interns:

Acknowledgements

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2019 2020

Back-ups

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Signals’ statistical features

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- From the main signals, we calculate two features, standard deviation and mean of the amplitude. s

- These features are calculated using a rolling window.

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• To adapt to increasing higher level of noise as we get to higher current, we also implement a dynamic learning algorithm.

Dynamic learning

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Training model

Evalua6on of error.

For each 10-second sec/on

Preprocessing

> 3.3 log loss median of previous distribu6on? trigger!

Base model

Reconstruction loss visualization

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0 is quench timeAnomaly point

Problems with static learning

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You generally see very clean signal when doing static learning (just learn on the firs few seconds)

However, the loss scale is different in each ramp and it’s hard to set a consistent threshold.

Dynamic threshold

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Training started

Reached previous quench’s max current

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Time/Current