I've been working on Machine Learning Projects for industrial predictive maintenance, but my R-squared value is stuck at 0.65. I've tried scaling my features and using XGBoost, but the sensor data is just so noisy. Are there specific feature engineering tricks, like rolling averages or Fourier transforms, that generally help in these types of time-series heavy projects?
3 answers
Noisy sensor data is the biggest hurdle in predictive maintenance Machine Learning Projects. Standard scaling isn't enough; you need to look at the temporal features. Creating "Lag features" (values from 5-10 minutes ago) and "Rolling statistics" (moving average and standard deviation) helps the model understand the trend rather than just the instant snapshot. Also, check for "Change Point Detection"—if a sensor suddenly shifts its baseline, that’s often a better predictor than the actual value itself. Try a Kalman filter to smooth out the noise before feeding the data into XGBoost.
Are you dealing with a lot of missing values in your sensor logs for these Machine Learning Projects?
Try using Random Forests instead of XGBoost. Sometimes they are more resilient to the noise found in industrial Machine Learning Projects.
That's a solid tip, Kelly. Random Forest’s bagging approach can definitely help prevent the model from over-tuning to the outliers in the noisy sensor data.
Yes, Gregory, the data has huge gaps because the sensors lose connection frequently. I’ve been using simple mean imputation, but I suspect that’s diluting the signal. For these Machine Learning Projects, would you recommend something more advanced like MICE (Multivariate Imputation by Chained Equations) or perhaps just using an algorithm that handles missing values natively? I want to make sure I’m not introducing bias that isn't actually there in the hardware.