machinery-condition-monitoring

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biswajitsahoo1111 / rul_codes_open

This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.

machine-learning deep-learning prediction remaining-useful-life prognosis rul-prediction machinery-condition-monitoring cmapss data-driven-prediction

Updated Dec 27, 2024
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biswajitsahoo1111 / cbm_codes_open

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This repository contains data and code that implement common machine learning algorithms for machinery condition monitoring task.

machine-learning deep-learning cwru condition-monitoring bearing-fault-diagnosis fault-diagnosis machinery-condition-monitoring

Updated Jul 16, 2023
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tvhahn / ml-tool-wear

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Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"

deep-learning recall trend beta-vae advanced-manufacturing precision milling anomaly-detection machinery-condition-monitoring masc-thesis latent-spaces tool-wear-monitoring tool-condition-monitoring

Updated Jun 1, 2021
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mo26-web / Induction-Motor-Faults-Detection-with-Stacking-Ensemble-Method-and-Deep-Learning

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This is a induction motor faults detection project implemented with Tensorflow. We use Stacking Ensembles method (with Random Forest, Support Vector Machine, Deep Neural Network and Logistic Regression) and Machinery Fault Dataset dataset available on kaggle.