A semi-supervised diagnostic framework based on the surface estimation of faulty distributions
| Autor: | Maryam Farajzadeh-Zanjani, Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif |
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| Rok vydání: | 2019 |
| Předmět: |
Surface (mathematics)
induction motors (IMs) Computer science Feature extraction 02 engineering and technology Session (web analytics) law.invention Reduction (complexity) law 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Cluster analysis Fault diagnosis Bearing (mechanical) business.industry 020208 electrical & electronic engineering Pattern recognition Manifold Computer Science Applications semi-supervised learning (SSL) Control and Systems Engineering Feature (computer vision) Artificial intelligence business surface estimation approach Information Systems |
| Zdroj: | Electrical and Computer Engineering Publications |
| Popis: | Design of the data-driven diagnostic systems usually requires to have labeled data during the training session. This paper aims to design a hybrid data-driven framework for diagnosing faults, where the data labels are not available to a large extent. This hybrid framework has five steps for transforming raw vibration signals to informative sets of samples for decision making. It uses several state-of-the-art approaches for feature extraction and semi-supervised feature reduction. The decision-making step uses a number of state-of-the-art semi-supervised learners. This step also comprises a novel surface estimation approach that is developed for SSL. The proposed hybrid framework is applied for diagnosing bearing defects in induction motors and validated based on four scenarios, each of which is experimented with different amounts of labeled samples. The attained diagnostic accuracies show the efficiency of the proposed hybrid framework, including the novel semi-supervised learner in classifying bearing defects, regardless of the number of labeled samples. |
| Databáze: | OpenAIRE |
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