arXiv (All)
open access
Deep Learning Approach to Bearing and Induction Motor Fault Diagnosis via Data Fusion
Convolutional Neural Networks (CNNs) are used to evaluate accelerometer and microphone data for bearing and induction motor diagnosis. A Long Short-Term Memory (LSTM) recurrent neural network is used to combine sensor information effectively, highlighting the benefits of data fusion. This approach e
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Vibration, acoustic, temperature, and motor current dataset of rotating machine under varying operating conditions for fault diagnosis#442723
- A Harmonic-Regression ARMA model for rotating machinery dynamics identification and fault detection under varying conditions#1063647
- Multisource neural network feature map fusion: An efficient strategy to detect plant diseases#367427
- Methodological challenges in machine learning and deep learning applied to food analysis: A critical review#297023
- Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype#779321
Record · ID 972379
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive