Deep Learning-Based Rolling Bearing Fault Diagnosis Part 5: Why a Random Forest Hands-On Is Also Worth Doing

In Part 5 of the rolling bearing fault diagnosis series, a random forest is used to build a traditional machine learning control group: each 1000×2 sample is flattened into 2000-dimensional features and trained with parameters such as n_estimators=50 and max_depth=5, achieving an overall accuracy of about 0.73. The author points out that tree models cannot see the waveform structure, and 73% is already the upper limit of their expressive capability, which perfectly highlights the value of the subsequent deep network's 95%.