Introduction to Survival Analysis for Predictive Maintenance using sksurv

Wartungen für Feuerwehrfahrzeuge frühzeitig vorhersagen

Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data

Find out how to measure motor vibrations using the Sensor Featherwing and create a Machine Learning algorithm on the Portenta board to detect anomalies.

Predictive maintenance for pump sensor data using a simple LSTM model

Fault explanation based on autoencoder model for predictive maintenance purposes

Esse repositório faz parte do trabalho de conclusão de curso "Aplicação de Algoritmos de Machine Learning para Apoio a Manutenção Preditiva" a ser apresentado como pré-requisito para conclusão do curso de graduação em engenharia elétrica na UFF.

Specific analysis of the data collected by machine sensors for the development of a Machine Learning application, which exploits the correlations discovered in the dataset to provide a sufficiently valid and correct prediction regarding a probable failure of a machine.

Demonstration of using Fast Fourier Transformation (FFT) to obtain timeseries signatures for use in predicting performance.

Multi-Instance based incremental Decision Trees for Time-series classification in industrial equipment.

Reverse engineering EGT Margin for P&W engines (ECM)

TFM UOC Jonathan Zambrano - Predictive Maintenance

Using a dataset of remote sensed features of an air production unit on a metro transit train to predict machinery high stress | failure.

LSTM network is used to predict the RUL of turbofan engines.

This repository presents a project that explores the realm of Predictive Maintenance, focusing on assessing the reliability of a hybrid Markov model compared to traditional models. The objective is to gain insights into optimal strategies for industrial maintenance scheduling.

Device Lifetime Estimation using Conv1D-based model.