Abstract

The safety of an Internet Data Center (IDC) is directly determined by the reliability and stability of its chiller system. Thus, combined with deep learning technology, an innovative hybrid fault diagnosis approach (1D-CNN_GRU) based on the time-series sequences is proposed in this study for the chiller system using 1-Dimensional Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU). Firstly, 1D-CNN is applied to automatically extract the local abstract features of the sensor sequence data. Secondly, GRU with long and short term memory characteristics is applied to capture the global features, as well as the dynamic information of the sequence. Moreover, batch normalization and dropout are introduced to accelerate network training and address the overfitting issue. The effectiveness and reliability of the proposed hybrid algorithm are assessed on the RP-1043 dataset; based on the experimental results, 1D-CNN_GRU displays the best performance compared with the other state-of-the-art algorithms. Further, the experimental results reveal that 1D-CNN_GRU has a superior identification rate for minor faults.

Highlights

  • With the rapid development of computer and sensor technology, modern industrial systems present a tendency towards complexity and integration, and the data reflecting the operation mechanism and state of the system presents all the characteristics of “big data”

  • In the process of feature extraction, the local features of the sequence are extracted by 1-Dimensional Convolutional Neural Network (1D-convolutional neural networks (CNN)) firstly, the output of 1D-CNN is used as the input of Gated Recurrent Unit (GRU) to further extract the long-term dependent features of the sequence, and achieve accurate fault diagnosis

  • In order to evaluate the effectiveness of the proposed approach, comparative experiments were conducted on the same dataset, including GRU, LSTM, 1D-CNN, BPNN, PCA_BPNN, as well as

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Summary

Introduction

With the rapid development of computer and sensor technology, modern industrial systems present a tendency towards complexity and integration, and the data reflecting the operation mechanism and state of the system presents all the characteristics of “big data”. Circulation pump constructing the mathematical model thatthe is sensitive faults and achieves diagnosis to extract historical data features during operationtoofspecific the equipment and realizefault fault diagnosis through the deviation between estimates and measurements [3]. This method is not scalable, by judging the consistency of Figure the current and those historical.

Experimental
Research
Structure
Data Analysis
Model Preparation
Long Short‐Term Memory
Gated Recurrent Unit
Convolution Neural Network
Proposed Fault Diagnosis Approach
Diagnosis
Evaluation
Evaluation Index
Parameter Optimization
Sensitivity Evaluation
Effectiveness Evaluation
5.5.Conclusions
Future
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