Quick Learning Mechanism with Cross-Domain Adaptation for Intelligent Fault Diagnosis

• URL: http://arxiv.org/abs/2103.08889v1
• Date: Tue, 16 Mar 2021 07:24:37 GMT
• Title: Quick Learning Mechanism with Cross-Domain Adaptation for Intelligent Fault Diagnosis
• Authors: Arun K. Sharma, Nishchal K. Verma
• Abstract summary: This paper presents a quick learning mechanism for intelligent fault diagnosis of rotating machines operating under changeable working conditions. We propose a quick learning method with Net2Net transformation followed by a fine-tuning method to cancel/minimize the maximum mean discrepancy of the new data to the previous one. The effectiveness of the proposed fault diagnosis method has been demonstrated on the CWRU dataset, IMS bearing dataset, and Paderborn university dataset.
• Score: 11.427019313283997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a quick learning mechanism for intelligent fault diagnosis of rotating machines operating under changeable working conditions. Since real case machines in industries run under different operating conditions, the deep learning model trained for a laboratory case machine fails to perform well for the fault diagnosis using recorded data from real case machines. It poses the need of training a new diagnostic model for the fault diagnosis of the real case machine under every new working condition. Therefore, there is a need for a mechanism that can quickly transform the existing diagnostic model for machines operating under different conditions. we propose a quick learning method with Net2Net transformation followed by a fine-tuning method to cancel/minimize the maximum mean discrepancy of the new data to the previous one. This transformation enables us to create a new network with any architecture almost ready to be used for the new dataset. The effectiveness of the proposed fault diagnosis method has been demonstrated on the CWRU dataset, IMS bearing dataset, and Paderborn university dataset. We have shown that the diagnostic model trained for CWRU data at zero load can be used to quickly train another diagnostic model for the CWRU data at different loads and also for the IMS dataset. Using the dataset provided by Paderborn university, it has been validated that the diagnostic model trained on artificially damaged fault dataset can be used for quickly training another model for real damage dataset.

Related papers

• AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing [64.79967583649407]
  Knowledge Tracing (KT) monitors students' knowledge states and simulates their responses to question sequences. Existing KT models typically follow a single-step training paradigm, which leads to significant error accumulation. We propose a novel Adversarial Multi-Step Training Framework for Knowledge Tracing (AdvKT) which focuses on the multi-step KT task.
  arXiv  Detail & Related papers  (2025-04-07T03:31:57Z)
• DDxT: Deep Generative Transformer Models for Differential Diagnosis [51.25660111437394]
  We show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network.
  arXiv  Detail & Related papers  (2023-12-02T22:57:25Z)
• Active Foundational Models for Fault Diagnosis of Electrical Motors [0.5999777817331317]
  Fault detection and diagnosis of electrical motors is of utmost importance in ensuring the safe and reliable operation of industrial systems. The existing data-driven deep learning approaches for machine fault diagnosis rely extensively on huge amounts of labeled samples. We propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples.
  arXiv  Detail & Related papers  (2023-11-27T03:25:12Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data [0.0]
  FaultD-XAI is a generic and interpretable approach for classifying faults in rotating machinery based on transfer learning. To provide scalability using transfer learning, synthetic vibration signals are created mimicking the characteristic behavior of failures in operation. The proposed approach not only obtained promising diagnostic performance, but was also able to learn characteristics used by experts to identify conditions.
  arXiv  Detail & Related papers  (2022-10-06T15:02:35Z)
• Deep Learning and Handheld Augmented Reality Based System for Optimal Data Collection in Fault Diagnostics Domain [0.0]
  This paper presents a novel human-machine interaction framework to perform fault diagnostics with minimal data. Minimizing the required data will increase the practicability of data-driven models in diagnosing faults. The proposed framework has provided above 100% precision and recall on a novel dataset with only one instance of each fault condition.
  arXiv  Detail & Related papers  (2022-06-15T19:15:26Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving [80.6725125503521]
  Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest. We present a use case of using ML models for detecting faults during automated screwdriving operations.
  arXiv  Detail & Related papers  (2021-07-05T11:46:00Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Residual Generation Using Physically-Based Grey-Box Recurrent Neural Networks For Engine Fault Diagnosis [1.0152838128195467]
  Hybrid fault diagnosis methods combining physically-based models and available training data have shown promising results. An automated residual design is developed using a bipartite graph representation of the system model to design grey-box recurrent neural networks. Data from an internal combustion engine test bench is used to illustrate the potentials of combining machine learning and model-based fault diagnosis techniques.
  arXiv  Detail & Related papers  (2020-08-11T11:59:48Z)
• Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning [3.8015092217142223]
  We propose a few-shot learning framework for bearing fault diagnosis based on model-agnostic meta-learning (MAML) Case studies show that the proposed framework achieves an overall accuracy up to 25% higher than a Siamese network-based benchmark study.
  arXiv  Detail & Related papers  (2020-07-25T04:03:18Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.