Feature Engineering
Feature engineering is the process of transforming raw sensor and process data into informative inputs — features — that help a machine-learning model perform well. Good features often matter more than the choice of algorithm.
Raw industrial signals are rarely useful to a model as-is. Feature engineering derives quantities that capture the underlying physics or fault signatures: statistical summaries, frequency-domain components from vibration, rolling averages, rates of change, or ratios between measurements. Domain knowledge is central, because an engineer who understands the asset can craft features that expose the patterns a model needs. Strong features improve accuracy, reduce data requirements and make models easier to interpret.