Knowledge Transfer based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis

• URL: http://arxiv.org/abs/2109.13479v3
• Date: Thu, 05 Dec 2024 05:50:39 GMT
• Title: Knowledge Transfer based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis
• Authors: Arun K. Sharma, Nishchal K. Verma,
• Abstract summary: We propose an evolutionary Net2Net transformation (EvoN2N) that finds the best suitable DNN architecture with limited availability of labeled data samples. The proposed framework can obtain the best model for intelligent fault diagnosis without a long and time-consuming search process. The best models obtained are capable of demonstrating an excellent diagnostic performance and classification accuracy of almost up to 100% for most of the operating conditions.
• Score: 6.167830237917662
• License:
• Abstract: A fault diagnosis with commendable accuracy is essential for the reliability of industrial machines. Two main challenges affect the design of high-performing intelligent systems: (i) the selection of a suitable model and (ii) domain adaptation if there is a continuous change in operating conditions. Therefore, we propose an evolutionary Net2Net transformation (EvoN2N) that finds the best suitable DNN architecture with limited availability of labeled data samples. Net2Net transformation-based quick learning algorithm has been used in the evolutionary framework of Non-dominated sorting genetic algorithm II to obtain the best DNN architecture. Net2Net transformation-based quick learning algorithm uses the concept of knowledge transfer from one generation to the next for faster fitness evaluation. The proposed framework can obtain the best model for intelligent fault diagnosis without a long and time-consuming search process. The proposed framework has been validated on the Case Western Reserve University dataset, the Paderborn University dataset, and the gearbox fault detection dataset under different operating conditions. The best models obtained are capable of demonstrating an excellent diagnostic performance and classification accuracy of almost up to 100\% for most of the operating conditions.

Related papers

• BiDense: Binarization for Dense Prediction [62.70804353158387]
  BiDense is a generalized binary neural network (BNN) designed for efficient and accurate dense prediction tasks. BiDense incorporates two key techniques: the Distribution-adaptive Binarizer (DAB) and the Channel-adaptive Full-precision Bypass (CFB)
  arXiv  Detail & Related papers  (2024-11-15T16:46:04Z)
• RLEEGNet: Integrating Brain-Computer Interfaces with Adaptive AI for Intuitive Responsiveness and High-Accuracy Motor Imagery Classification [0.0]
  We introduce a framework that leverages Reinforcement Learning with Deep Q-Networks (DQN) for classification tasks. We present a preprocessing technique for multiclass motor imagery (MI) classification in a One-Versus-The-Rest (OVR) manner. The integration of DQN with a 1D-CNN-LSTM architecture optimize the decision-making process in real-time.
  arXiv  Detail & Related papers  (2024-02-09T02:03:13Z)
• A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks [65.34977803841007]
  Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. We show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one.
  arXiv  Detail & Related papers  (2022-11-16T00:11:04Z)
• Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis [8.92307560991779]
  We have proposed a novel framework of evolutionary deep neural network which uses policy gradient to guide the evolution of model architecture. The effectiveness of the proposed framework has been validated on three datasets.
  arXiv  Detail & Related papers  (2021-11-12T18:59:45Z)
• 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)
• Non-Gradient Manifold Neural Network [79.44066256794187]
  Deep neural network (DNN) generally takes thousands of iterations to optimize via gradient descent. We propose a novel manifold neural network based on non-gradient optimization.
  arXiv  Detail & Related papers  (2021-06-15T06:39:13Z)
• Genetic Algorithm based hyper-parameters optimization for transfer Convolutional Neural Network [10.144772866486914]
  Decision of transfer layers and trainable layers is a major task for design of convolutional neural networks. In this paper, a genetic algorithm is applied to select trainable layers of the transfer model. The system will converge with a precision of 97% in the classification of Cats and Dogs datasets.
  arXiv  Detail & Related papers  (2021-02-26T07:38:01Z)
• 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)
• Binarizing MobileNet via Evolution-based Searching [66.94247681870125]
  We propose a use of evolutionary search to facilitate the construction and training scheme when binarizing MobileNet. Inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs) Our objective is to come up with a tiny yet efficient binary neural architecture by exploring the best candidates of the group convolution.
  arXiv  Detail & Related papers  (2020-05-13T13:25:51Z)
• Neural Architecture Search For Fault Diagnosis [6.226564415963648]
  Deep learning is suitable for processing big data, and has a strong feature extraction ability to realize end-to-end fault diagnosis systems. Neural architecture search (NAS) is developing rapidly, and is becoming one of the next directions for deep learning. In this paper, we proposed a NAS method for fault diagnosis using reinforcement learning.
  arXiv  Detail & Related papers  (2020-02-19T04:03:51Z)
• Evolving Neural Networks through a Reverse Encoding Tree [9.235550900581764]
  This paper advances a method which incorporates a type of topological edge coding, named Reverse HANG Tree (RET), for evolving scalable neural networks efficiently. Using RET, two types of approaches -- NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) -- have been designed to solve problems in benchmark continuous learning environments.
  arXiv  Detail & Related papers  (2020-02-03T02:29:51Z)

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.