… we derive a valid fault forest for each layer of analysis. Since a fault forest is only valid with
… from whence it came, we will now iteratively extend the model with each composition step. …

… 2, we show that the composition of two fault trees results in a valid fault tree. We will then
extend this to show that the composition of two fault forests results in a valid fault forest. …

… , fault diagnosis based on the use of random forests appears to be a promising approach to
process monitoring. The nonlinear nature of random forest … its ability to identify faults, indicate …

… forests scheme is proposed for fault … forests, the hierarchical clustering method is applied
for offline model selection in random forests, which can simultaneously reduce the online fault …

… of process monitoring and fault diagnosis. The dataset, which … machine learning algorithm
causal random forests (CRF) and … the impact of dataset composition on model interpretation, …

… Table 1 shows the composition of fault types and fault severity in the experimental sample
set, which includes 10 different rolling bearing health states. Each rolling bearing vibration …

… Fault classification plays a central role in process monitoring and fault diagnosis in complex
industrial processes. Plenty of fault classification methods have been proposed under the …

… for classification, random forests has been widely used in … selection (DES) in random
forests. In addition, a weighted … forests (RF) and the static selection based random forests. …

… after the fault … fault feature extraction is realized via a stacked autoencoder (SAE) model.
After that, an improved multi-grained cascade forest (IgcForest) is proposed to diagnose faults…

… that can achieve desired fault classification performance … forests model is proposed in this
paper, which uses XGBoost, random forests and extremely randomized trees as basic forests …