Predictive Maintenance with Machine Learning: A Hands-On Tutorial

01What predictive maintenance actually solves

Predictive maintenance forecasts when a machine will need attention so you can fix it before it breaks. It sits between reactive (fix on failure) and preventive (fix on a fixed schedule). Done right, it cuts downtime 30–50%.

02What data you need

Sensor readings (vibration, temperature, current, pressure), maintenance records (when, why, what failed), and operational context (load, shift, environment).

03Frame the problem correctly

You can frame predictive maintenance as classification (will it fail in the next N hours?), regression (how many hours until failure?), or anomaly detection (is the current state unusual?). Pick based on label quality.

04Feature engineering for time-series sensor data

import pandas as pd

def featurize(df, cols, windows=("1h","6h","24h")):
    for c in cols:
        for w in windows:
            df[f"{c}_mean_{w}"] = df[c].rolling(w).mean()
            df[f"{c}_std_{w}"]  = df[c].rolling(w).std()
            df[f"{c}_max_{w}"]  = df[c].rolling(w).max()
    df["hours_since_maint"] = (df.index - df["last_maint"]).dt.total_seconds()/3600
    return df.dropna()

05Pick a model your team can actually maintain

06Production reality

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