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From Sensors to Smart Decisions: Using ChatGPT to Build an IIoT Overheating Detection Model (with ONNX Export for TwinSight)

TL;DR: This guide shows how to use ChatGPT to create a Python-based machine learning pipeline for detecting overheating from IIoT time series data, and export the model to ONNX format for deployment in TwinSight ML Workbench.

The Industrial Data Opportunity

Industrial IoT (IIoT) systems generate a massive stream of time series data: temperatures, pressures, vibrations — all timestamped and sensor-tagged. But the real value isn’t in collecting the data — it’s in acting on it.

Machine learning can help detect anomalies like overheating before they cause damage or downtime. The challenge? Building and deploying these models often requires data science and coding skills that aren’t common in the field.

That’s where ChatGPT comes in.

Why Use ChatGPT for IIoT Machine Learning?

ChatGPT acts as a co-pilot for Python development. With the right prompts, it can:

  • Generate custom scripts for data ingestion, processing, and modeling
  • Help non-experts apply best practices from data science
  • Convert models to ONNX, a portable format supported by TwinSight

No need to start from scratch — just describe your use case and let ChatGPT assist.

Use Case: Detecting Overheating from Sensor Data

Let’s say you want to monitor a machine component using a temperature sensor. Here’s a sample of what the data looks like:

timestampsensor_namevalue (°C)
2025-05-01 00:00:00temp_sensor72.1
2025-05-01 00:01:00temp_sensor72.5
2025-05-01 00:02:00temp_sensor79.0

For this demo, we’ll define anomalies as temperature readings above 78°C.

Step-by-Step: From CSV to ONNX Model with ChatGPT

Here’s how you can build this pipeline using ChatGPT-generated code.

1. Load and Label the Data

ChatGPT Prompt:

“Write a Python script that loads a CSV with timestamp, sensor_name, and value, and creates a new column is_overheating that is 1 when value > 78, else 0.”

Generated code:

python

import pandas as pd

# Load the data
df = pd.read_csv(“sensor_data.csv”, parse_dates=[“timestamp”])

# Add overheating flag
df[‘is_overheating’] = (df[‘value’] > 78).astype(int)

2. Create Features for Machine Learning

ChatGPT Prompt:

“Add rolling features (mean, std, delta) to detect changes over time.”

python

# Sort by time
df = df.sort_values("timestamp")

# Rolling window features
df[‘rolling_mean’] = df[‘value’].rolling(window=5).mean()
df[‘rolling_std’] = df[‘value’].rolling(window=5).std()
df[‘delta’] = df[‘value’].diff()

# Drop rows with NaN values
df = df.dropna()

3. Train an Anomaly Detection Model

ChatGPT Prompt:

“Train a RandomForestClassifier to predict is_overheating using the rolling features, and print accuracy and F1 score.”

python

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score

# Feature columns
features = [‘rolling_mean’, ‘rolling_std’, ‘delta’]
X = df[features]
y = df[‘is_overheating’]

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(“Accuracy:”, accuracy_score(y_test, y_pred))
print(“F1 Score:”, f1_score(y_test, y_pred))

4. Export to ONNX for Deployment

ChatGPT Prompt:

“Convert the trained model to ONNX using skl2onnx.”

python

from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType

# Define input type
initial_type = [(‘float_input’, FloatTensorType([None, len(features)]))]

# Convert and save
onnx_model = convert_sklearn(model, initial_types=initial_type)
with open(“overheat_model.onnx”, “wb”) as f:
f.write(onnx_model.SerializeToString())

5. Deploy with TwinSight ML Workbench

Now that you have the overheat_model.onnx file, it’s ready to be used in TwinSight ML Workbench, which supports running ONNX models on real-time data.

Here’s how to deploy:

  1. Go to eot.ai/ml-workbench
  2. Upload your .onnx model
  3. Connect it to your live sensor stream
  4. Set triggers and alerts based on the model’s predictions

Why This Workflow Works So Well

BenefitDescription
 AI-assisted codingNo boilerplate or syntax stress
Iterative developmentYou can ask ChatGPT to tweak, refactor, or improve any step
ONNX portabilityModels are ready for industrial platforms like TwinSight
Fast prototypingFrom CSV to deployed model in minutes
Accessible to non-expertsEngineering teams can lead the process with minimal Python

Prompting Tips

  • Be specific: Mention your column names, use case, and model goals.
  • Use incremental requests: Build one step at a time and ask ChatGPT to improve or explain as needed.
  • Ask for alternatives: “What’s a faster model?”, “Can you add scaling?”, etc.

Common Pitfalls

  • Bad data: Ensure timestamps are consistent and values are clean.
  • Feature leakage: Be cautious when using rolling or lag features.
  • ONNX conversion errors: Stick with supported models (e.g., RandomForest, LogisticRegression) for a smoother export.

What’s Next?

You can now iterate further:

  • Add more sensors as input
  • Use unsupervised models like IsolationForest for anomaly detection without labels
  • Schedule retraining when more data becomes available

This approach isn’t just for overheating — you can use the same workflow to detect vibration anomalies, pressure surges, or even early signs of system failure.

Final Thoughts

ChatGPT and TwinSight ML Workbench together create a powerful bridge between raw IIoT data and actionable machine learning — even for teams without dedicated data scientists.

By turning plain language into working Python code and portable ONNX models, you can start deploying intelligence into your industrial systems today.

So next time you want to build an ML model for your plant or machine, don’t start in Jupyter — start in ChatGPT.

Want to try it? Head over to https://eot.ai/ml-workbench and start deploying your models today.