Generation of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-based Input-Output Decoupled Network
- URL: http://arxiv.org/abs/2404.18528v1
- Date: Mon, 29 Apr 2024 09:12:53 GMT
- Title: Generation of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-based Input-Output Decoupled Network
- Authors: Zhuofu Pan, Qingkai Sui, Yalin Wang, Jiang Luo, Jie Chen, Hongtian Chen,
- Abstract summary: This paper proposes a transfer learning-based input-output decoupled network (TDN) for diagnostic purposes.
It consists of an input-output decoupled network (IDN) and a pre-trained variational autocoder (VAE)
After training, IDN learns the mapping from faulty to normal, thereby serving as the fault detection index and the estimated fault signal simultaneously.
- Score: 8.611299411558045
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Structural decoupling has played an essential role in model-based fault isolation and estimation in past decades, which facilitates accurate fault localization and reconstruction thanks to the diagonal transfer matrix design. However, traditional methods exhibit limited effectiveness in modeling high-dimensional nonlinearity and big data, and the decoupling idea has not been well-valued in data-driven frameworks. Known for big data and complex feature extraction capabilities, deep learning has recently been used to develop residual generation models. Nevertheless, it lacks decoupling-related diagnostic designs. To this end, this paper proposes a transfer learning-based input-output decoupled network (TDN) for diagnostic purposes, which consists of an input-output decoupled network (IDN) and a pre-trained variational autocoder (VAE). In IDN, uncorrelated residual variables are generated by diagonalization and parallel computing operations. During the transfer learning phase, knowledge of normal status is provided according to VAE's loss and maximum mean discrepancy loss to guide the training of IDN. After training, IDN learns the mapping from faulty to normal, thereby serving as the fault detection index and the estimated fault signal simultaneously. At last, the effectiveness of the developed TDN is verified by a numerical example and a chemical simulation.
Related papers
- DeltaPhi: Learning Physical Trajectory Residual for PDE Solving [54.13671100638092]
We propose and formulate the Physical Trajectory Residual Learning (DeltaPhi)
We learn the surrogate model for the residual operator mapping based on existing neural operator networks.
We conclude that, compared to direct learning, physical residual learning is preferred for PDE solving.
arXiv Detail & Related papers (2024-06-14T07:45:07Z) - TDANet: A Novel Temporal Denoise Convolutional Neural Network With Attention for Fault Diagnosis [0.5277756703318045]
This paper proposes the Temporal Denoise Convolutional Neural Network With Attention (TDANet) to improve fault diagnosis performance in noise environments.
The TDANet model transforms one-dimensional signals into two-dimensional tensors based on their periodic properties, employing multi-scale 2D convolution kernels to extract signal information both within and across periods.
Evaluation on two datasets, CWRU (single sensor) and Real aircraft sensor fault (multiple sensors), demonstrates that the TDANet model significantly outperforms existing deep learning approaches in terms of diagnostic accuracy under noisy environments.
arXiv Detail & Related papers (2024-03-29T02:54:41Z) - DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data [0.0]
We propose an anomaly detection and diagnosis model, DTAAD, based on Transformer and Dual Temporal Convolutional Network (TCN)
scaling methods and feedback mechanisms are introduced to improve prediction accuracy and expand correlation differences.
Our experiments on seven public datasets validate that DTAAD exceeds the majority of currently advanced baseline methods in both detection and diagnostic performance.
arXiv Detail & Related papers (2023-02-17T06:59:45Z) - Hybridization of Capsule and LSTM Networks for unsupervised anomaly
detection on multivariate data [0.0]
This paper introduces a novel NN architecture which hybridises the Long-Short-Term-Memory (LSTM) and Capsule Networks into a single network.
The proposed method uses an unsupervised learning technique to overcome the issues with finding large volumes of labelled training data.
arXiv Detail & Related papers (2022-02-11T10:33:53Z) - Transfer Learning for Fault Diagnosis of Transmission Lines [55.971052290285485]
A novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed.
It is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source neural network to predict a dissimilar target dataset.
arXiv Detail & Related papers (2022-01-20T06:36:35Z) - Convolutional generative adversarial imputation networks for
spatio-temporal missing data in storm surge simulations [86.5302150777089]
Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods.
We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
arXiv Detail & Related papers (2021-11-03T03:50:48Z) - Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge
Computing: A Contextual-Bandit Approach [81.5261621619557]
We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC)
We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer.
Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
arXiv Detail & Related papers (2021-08-09T08:45:47Z) - SignalNet: A Low Resolution Sinusoid Decomposition and Estimation
Network [79.04274563889548]
We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples.
We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions.
In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
arXiv Detail & Related papers (2021-06-10T04:21:20Z) - Explainability: Relevance based Dynamic Deep Learning Algorithm for
Fault Detection and Diagnosis in Chemical Processes [0.0]
Two important applications of Statistical Process Control (SPC) in industrial settings are fault detection and diagnosis (FDD)
In this work a deep learning (DL) based methodology is proposed for FDD.
We investigate the application of an explainability concept to enhance the FDD accuracy of a deep neural network model trained with a data set of relatively small number of samples.
arXiv Detail & Related papers (2021-03-22T23:10:05Z) - TELESTO: A Graph Neural Network Model for Anomaly Classification in
Cloud Services [77.454688257702]
Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance.
One direction aims at the recognition of re-occurring anomaly types to enable remediation automation.
We propose a method that is invariant to dimensionality changes of given data.
arXiv Detail & Related papers (2021-02-25T14:24:49Z) - High-level Modeling of Manufacturing Faults in Deep Neural Network
Accelerators [2.6258269516366557]
Google's Unit Processing (TPU) is a neural network accelerator that uses systolic array-based matrix multiplication hardware for computation in its crux.
Manufacturing faults at any state element of the matrix multiplication unit can cause unexpected errors in these inference networks.
We propose a formal model of permanent faults and their propagation in a TPU using the Discrete-Time Markov Chain (DTMC) formalism.
arXiv Detail & Related papers (2020-06-05T18:11:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.