On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor

Bienefeld, Christoph; Kirchner, Eckhard; Vogt, Andreas; Kacmar, Marian

doi:10.3390/lubricants10040067

Cited by 19 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predictions of each tree are used for the final prediction; that is, when working in regression mode (quantitative data), these are averaged, and when working in classification mode (qualitative data) a voting process is carried out [18,19,24]. RF is considered a robust method that can provide good results compared to different regression algorithms [25].…”

Section: Random Forestmentioning

confidence: 99%

Machine Learning to Predict the Adsorption Capacity for Microplastics

Astray¹,

Soria²,

Barreiro³

et al. 2023

Preprint

View full text Add to dashboard Cite

Nowadays, there is extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through degradation processes of the plastics themselves, can contaminate the ecosystem with micro and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) to predict different microplastic/water partition coefficients (log Kd) were developed using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients upper than 0.92 in the query phase, which indicate that these type of models could be used for a rapid estimation of the absorption of organic contaminants on microplastics.

show abstract

Section: Random Forestmentioning

confidence: 99%

Machine Learning to Predict the Adsorption Capacity for Microplastics

Astray¹,

Soria²,

Barreiro³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A particularly computationally efficient processing method has already been proposed in [ 26 ] and further investigated in [ 27 ]. The core of this method is to separate the signal into frequency bands.…”

Section: Fundamentalsmentioning

confidence: 99%

Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis

Bienefeld,

Becker-Dombrowsky,

Shatri

et al. 2023

Entropy

Self Cite

View full text Add to dashboard Cite

The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research, purely data-driven deep learning methods are becoming increasingly popular, aiming for accurate predictions of bearing faults without requiring bearing-specific domain knowledge. Opposing this trend in popularity, the present paper takes a more traditional approach, incorporating domain knowledge by evaluating a variety of feature engineering methods in combination with a random forest classifier. For a comprehensive feature engineering study, a total of 42 mathematical feature formulas are combined with the preprocessing methods of envelope analysis, empirical mode decomposition, wavelet transforms, and frequency band separations. While each single processing method and feature formula is known from the literature, the presented paper contributes to the body of knowledge by investigating novel series connections of processing methods and feature formulas. Using the CWRU bearing fault data for performance evaluation, feature calculation based on the processing method of frequency band separation leads to particularly high prediction accuracies, while at the same time being very efficient in terms of low computational effort. Additionally, in comparison with deep learning approaches, the proposed feature engineering method provides excellent accuracies and enables explainability.

show abstract

“…For regression tasks with quantitative data, the predictions are averaged, whereas for classification tasks with qualitative data, a voting process is carried out [ 20 , 21 , 26 ]. RF is generally considered a robust method that can achieve good results compared to different regression algorithms [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Predict the Adsorption Capacity of Microplastics

et al. 2023

View full text Add to dashboard Cite

Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.

show abstract

On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor

Cited by 19 publications

References 27 publications

Machine Learning to Predict the Adsorption Capacity for Microplastics

Machine Learning to Predict the Adsorption Capacity for Microplastics

Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis

Machine Learning to Predict the Adsorption Capacity of Microplastics

Contact Info

Product

Resources

About