US-20260126335-A1 - METHOD AND SYSTEM FOR MONITORING AND PREDICTING LEAK IN LIQUID COOLING SYSTEM OF ELECTRONIC DEVICE

Abstract

A leak monitoring technology is provided. In some embodiments, a real-time audio signal of fluid flow in a pipeline is received in real time. The real-time audio signal is converted into a time-frequency spectrum. Image recognition is performed on the time-frequency spectrum, to determine whether there is a leak in the pipeline.

Inventors

• Tung Yang Tang
• Liping Pan
• Meng Chao KAO
• Chu Chia Tsai
• Wen Hua Liu

Assignees

• WISTRON CORPORATION

Dates

Publication Date

20260507

Application Date

20250103

Priority Date

20241101

Claims (20)

1. 1 . A method for monitoring and predicting a leak in a liquid cooling system of an electronic device, the method comprising: receiving a real-time audio signal of fluid flow within a pipeline; converting the real-time audio signal into a time-frequency spectrum; and performing image recognition on the time-frequency spectrum, to determine whether there is a leak in the pipeline.
2. 2 . The method for monitoring and predicting a leak according to claim 1 , wherein the step of converting the audio signal into the time-frequency spectrum comprises: performing fast Fourier transform on the real-time audio signal, to generate the time-frequency spectrum, wherein the time-frequency spectrum is a three-dimensional spectrum, and the three-dimensional spectrum comprises time information, frequency information, and sound intensity information.
3. 3 . The method for monitoring and predicting a leak according to claim 1 , wherein the step of determining whether there is the leak in the pipeline based on the time-frequency spectrum comprises: inputting the time-frequency spectrum into a classification model, to generate a leak determining result of the pipeline.
4. 4 . The method for monitoring and predicting a leak according to claim 3 , wherein the classification model is a neural network (NN) model.
5. 5 . The method for monitoring and predicting a leak according to claim 4 , wherein the classification model comprises a bidirectional long short-term memory (BLSTM) model, comprising an input layer, a forward long short-term memory layer, a backward long short-term memory layer, a fully connected layer, and an output layer, wherein the input layer is configured to receive the time-frequency spectrum, the forward long short-term memory layer is configured to process forward sequence data in the time-frequency spectrum, the backward long short-term memory layer is configured to process backward sequence data in the time-frequency spectrum, the fully connected layer is configured to integrate outputs of the forward long short-term memory layer and the backward long short-term memory layer, and the output layer is configured to output the corresponding leak determining result.
6. 6 . The method for monitoring and predicting a leak according to claim 5 , wherein after obtaining the leak determining result, the method further comprises: determining whether the leak determining result matches a result of an actual current condition; and inputting the time-frequency spectrum into a training dataset to retrain the classification model through the training dataset if the leak determining result does not match the result of the actual current condition.
7. 7 . The method for monitoring and predicting a leak according to claim 6 , wherein the training dataset comprising the following steps: initializing a weight and a bias of the classification model; inputting training data into the classification model, and obtaining a prediction result through forward propagation; calculating a loss function, and evaluating a difference between the prediction result and an actual result; calculating gradients of the weight and the bias by using a backpropagation algorithm, and updating the weight and the bias by using an optimization algorithm, to minimize the loss function; and repeating the above steps until the model converges or reaches a set number of training iterations.
8. 8 . The method for monitoring and predicting a leak according to claim 7 , wherein after the training of the classification model is completed, the method comprises the following steps: using test data to perform predictions with the classification model, and evaluating performance of the model; and adjusting a model architecture and a hyperparameter or retraining the model based on an evaluation result.
9. 9 . The method for monitoring and predicting a leak according to claim 3 , wherein the classification model comprises a support vector machine classification model.
10. 10 . The method for monitoring and predicting a leak according to claim 9 , wherein establishing the classification model comprises the following steps: providing a plurality of time-frequency spectra corresponding to leakage and non-leakage conditions, performing image pyramid processing and feature extraction processing on the time-frequency spectra in sequence, and then outputting the processed data to the support vector machine classification model for classification.
11. 11 . The method for monitoring and predicting a leak according to claim 9 , wherein the inputting the time-frequency spectrum into a classification model, to generate a leak determining result of the pipeline comprises the following steps: performing image pyramid processing at different scales on time-frequency spectra, to generate a plurality of audio region images with varying scales, and segmenting the audio region images of different scales to generate a plurality of sub-blocks; calculating at least one feature value of each of the sub-blocks, and determining a texture correlation between each sub-block and a reference model associated with non-leakage conditions; and generating the leak determining result of the pipeline based on the feature value and the texture correlation of each sub-block by using the support vector machine classification model.
12. 12 . The method for monitoring and predicting a leak according to claim 11 , wherein establishing the reference model comprises the following steps: averaging pixel values of sub-time-frequency spectra obtained from performing the image pyramid processing on the time-frequency spectra corresponding to non-leakage conditions, to generate the reference model.
13. 13 . The method for monitoring and predicting a leak according to claim 11 , wherein the at least one feature value of each sub-block comprises at least one of a standard deviation and histogram kurtosis of a plurality of pixel values of the sub-block.
14. 14 . The method for monitoring and predicting a leak according to claim 11 , wherein the texture correlation between each sub-block and the reference model is a correlation coefficient of a local binary pattern (LBP) between the sub-block and the reference model.
15. 15 . A system for monitoring and predicting a leak in a liquid cooling system of an electronic device, the system comprising: a receiving module, configured to receive a real-time audio signal of fluid flow within a pipeline; and a processor, coupled to the receiving module, and configured to convert the real-time audio signal into a time-frequency spectrum and perform image recognition on the time-frequency spectrum, to determine whether there is a leak in the pipeline.
16. 16 . The system for monitoring and predicting a leak according to claim 15 , wherein the receiving module comprises an automotive audio bus (A2B) and a plurality of miniature microphone units, and the miniature microphone units are arranged in a daisy chain configuration and are electrically connected to the automotive audio bus.
17. 17 . The system for monitoring and predicting a leak according to claim 15 , wherein the receiving module is arranged on a side of a fluid cooling pipeline of an electronic device, and is adapted to continuously receive the real-time audio signal generated by cooling fluid flow in the fluid cooling pipeline.
18. 18 . A system for monitoring and predicting a leak in a liquid cooling system of an electronic device, the system comprising: a receiving module, configured to receive a real-time audio signal of fluid flow within a pipeline; and a processor, coupled to the receiving module, and configured to perform fast Fourier transform on the real-time audio signal, to generate a time-frequency spectrum, and input the time-frequency spectrum into a classification model to perform image recognition, so as to determine whether there is a leak in the pipeline.
19. 19 . The system for monitoring and predicting a leak according to claim 18 , wherein the classification model comprises a bidirectional long short-term memory (BLSTM) model, comprising an input layer, a forward long short-term memory layer, a backward long short-term memory layer, a fully connected layer, and an output layer, wherein the input layer is configured to receive the time-frequency spectrum, the forward long short-term memory layer is configured to process forward sequence data in the time-frequency spectrum, the backward long short-term memory layer is configured to process backward sequence data in the time-frequency spectrum, the fully connected layer is configured to integrate outputs of the forward long short-term memory layer and the backward long short-term memory layer, and the output layer is configured to output a corresponding leak determining result.
20. 20 . The system for monitoring and predicting a leak according to claim 18 , wherein the classification model comprises a support vector machine classification model, and the processor is configured to: perform image pyramid processing at different scales on time-frequency spectra, to generate a plurality of audio region images with varying scales, and segment the audio region images of different scales, to generate a plurality of sub-blocks; calculate at least one feature value of each of the sub-blocks, and calculate a texture correlation between each sub-block and a reference model associated with non-leakage conditions; and generate a leak determining result of the pipeline based on the feature value and the texture correlation of each sub-block by using the support vector machine classification model.

Description

CROSS-REFERENCE TO RELATED APPLICATION This non-provisional application claims priority under 35 U.S.C. § 119(a) to Patent Application No. 113142004 filed in Taiwan, R.O.C. on Nov. 1, 2024, the entire contents of which are hereby incorporated by reference. BACKGROUND Technical Field The disclosure relates to a method and system for monitoring and predicting a leak in a liquid cooling system of an electronic device. Related Art With the advent of the high-computing power era driven by Artificial Intelligence (AI), the demand for high-performance computing and high-frequency, high-speed data transmission has been steadily increasing. This trend has led to a continuous rise in power consumption by servers, driving the advancement of cooling technologies. Currently, air cooling is gradually becoming insufficient to meet thermal management requirements, bringing liquid cooling technology to the forefront. However, liquid cooling still presents a potential risk of leakage, which can result in failures of electronic components. In conventional technologies, leakage monitoring in liquid cooling systems is predominantly carried out using leak detection cables. These cables are typically deployed beneath the raised floors of data centers or within servers. However, due to their large size and high cost, leak detection cables can only be installed in critical areas. Moreover, they are incapable of detecting minor leaks and generally only trigger detection when leakage has reached a significant level, by which time damage to electronic components or circuits has often already occurred. SUMMARY In view of the above, the disclosure provides a method for monitoring and predicting a leak in a liquid cooling system of an electronic device. The method includes: receiving a real-time audio signal of fluid flow within a pipeline; converting the real-time audio signal into a time-frequency spectrum; and performing image recognition on the time-frequency spectrum, to determine whether there is a leak in the pipeline. In some embodiments, the step of converting the real-time audio signal into the time-frequency spectrum includes: performing fast Fourier transform on the real-time audio signal, to generate the time-frequency spectrum, where the time-frequency spectrum is a three-dimensional spectrum, and the three-dimensional spectrum includes time information, frequency information, and sound intensity information. In some embodiments, the step of determining whether there is the leak in the pipeline based on the time-frequency spectrum includes: inputting the time-frequency spectrum into a classification model, to generate a leak determining result of the pipeline. In some embodiments, the classification model is a neural network (NN) model. In some embodiments, the classification model includes a bidirectional long short-term memory (BLSTM) model, which includes an input layer, a forward long short-term memory layer, a backward long short-term memory layer, a fully connected layer, and an output layer. The input layer is configured to receive the time-frequency spectrum, the forward long short-term memory layer is configured to process forward sequence data in the time-frequency spectrum, the backward long short-term memory layer is configured to process backward sequence data in the time-frequency spectrum, the fully connected layer is configured to integrate outputs of the forward long short-term memory layer and the backward long short-term memory layer, and the output layer is configured to output the corresponding leak determining result. In some embodiments, after obtaining the leak determining result, the method further includes: determining whether the leak determining result matches a result of an actual current condition; and inputting the time-frequency spectrum into a training dataset, to retrain the classification model through the training dataset if the leak determining result does not match the result of the actual current condition. In some embodiments, the training dataset includes the following steps: initializing a weight and a bias of the classification model; inputting training data into the classification model, and obtaining a prediction result through forward propagation; calculating a loss function, and evaluating a difference between the prediction result and an actual result; calculating gradients of the weight and the bias by using a backpropagation algorithm, and updating the weight and the bias by using an optimization algorithm, to minimize the loss function; and repeating the above steps until the model converges or reaches a set number of training iterations. In some embodiments, after the training of the classification model is completed, the method includes the following steps: using test data to perform predictions with the classification model, and evaluating performance of the model; and adjusting a model architecture and a hyperparameter, or retraining the model based on an evaluation res