---
library_name: onnx
tags:
  - onnx
  - predictive-maintenance
  - perceptron
  - tabular-classification
---

# Predictive Maintenance — Pocket Perceptron

A binary classifier trained in C++ using the Pocket Perceptron algorithm on the AI4I 2020 Predictive Maintenance Dataset and exported to ONNX.

## Model Interface

- **Input:** `X`, FP64 tensor of shape `[N, 8]`
- **Output:** `Y`, INT64 tensor of shape `[N, 1]`
- **Labels:** `+1` = machine failure, `-1` = normal operation

The eight features are three one-hot encoded machine-type values (L, M, H), followed by air temperature, process temperature, rotational speed, torque, and tool wear.

Numerical features must be standardized using the included `scaler.json` before inference.

## Evaluation

On 2,000 held-out observations:

| Metric | Result |
|---|---:|
| Accuracy | 97.15% |
| Precision | 70.37% |
| Recall | 27.94% |
| F1 score | 40.00% |

The model is intended as an educational deployment demonstration, not a production-ready predictive maintenance system.

## Deployment

`config.pbtxt` provides the Datasance ONNX inference engine configuration.

Source project: C++ Pocket PLA → ONNX Runtime → Datasance REST inference.