# karankhatavkar/predictive-maintenance-bearing-tool-wear architecture

> Architecture diagram of the GitHub repository karankhatavkar/predictive-maintenance-bearing-tool-wear, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-27). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/karankhatavkar/predictive-maintenance-bearing-tool-wear
- Repository: https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear

## Overview

This repository is a notebook-based predictive-maintenance study, not a deployed application. Its two documented workflows analyze NASA IMS bearing degradation and PHM 2010 CNC tool wear, then compare traditional and deep-learning predictions across held-out hardware. The graph reflects capabilities and data relationships documented in the README; notebook internals were not available, so it does not infer code-level calls or transfers between notebooks.

Main entry points: [`phm2010/03_deep_learning_tcn_xai.ipynb`](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/03_deep_learning_tcn_xai.ipynb) (Deep architectures), [`nasa-ims/02_traditional_ml.ipynb`](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/02_traditional_ml.ipynb) (Vibration features), [`nasa-ims/03_deep_learning_cnn_lstm.ipynb`](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/03_deep_learning_cnn_lstm.ipynb) (Raw-signal models), [`phm2010/02_feature_engineering_ml.ipynb`](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/02_feature_engineering_ml.ipynb) (Cut features)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_nasa["NASA IMS Bearings"]
  node_nasa_eda["Degradation EDA<br/>[01_eda.ipynb]"]
  node_nasa_features["Vibration features"]
  node_nasa_ml["Traditional ML"]
  node_nasa_eval["Held-out bearing test"]
  node_nasa_dl["Raw-signal models"]
  node_nasa_dl_eval["Held-out set test"]
end

subgraph group_phm["PHM Tool Wear"]
  node_phm_eda["Signal EDA<br/>[01_eda.ipynb]"]
  node_phm_features["Cut features"]
  node_phm_ml["Traditional ML"]
  node_phm_eval["Held-out cutter test"]
  node_phm_dl["Deep architectures"]
  node_phm_xai["Saliency analysis"]
  node_phm_dl_eval["Held-out cutter test"]
end

node_analyst(("Analyst"))
node_nasa_data[("NASA IMS data")]
node_phm_data[("PHM 2010 data")]

node_analyst -->|"analyzes"| node_nasa_eda
node_analyst -->|"models"| node_nasa_features
node_analyst -->|"models"| node_nasa_dl
node_nasa_data -->|"raw signals"| node_nasa_eda
node_nasa_data -->|"bearing signals"| node_nasa_features
node_nasa_features -->|"extracts features"| node_nasa_ml
node_nasa_data -->|"raw signals"| node_nasa_dl
node_nasa_ml -->|"compared on"| node_nasa_eval
node_nasa_dl -->|"tested on"| node_nasa_dl_eval
node_analyst -->|"analyzes"| node_phm_eda
node_analyst -->|"models"| node_phm_features
node_analyst -->|"models"| node_phm_dl
node_phm_data -->|"raw signals"| node_phm_eda
node_phm_data -->|"cut signals"| node_phm_features
node_phm_features -->|"extracts features"| node_phm_ml
node_phm_data -->|"signal data"| node_phm_dl
node_phm_ml -->|"compared on"| node_phm_eval
node_phm_dl -->|"tested on"| node_phm_dl_eval
node_phm_dl -->|"explained by"| node_phm_xai

click node_nasa_eda "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/01_eda.ipynb"
click node_nasa_features "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/02_traditional_ml.ipynb"
click node_nasa_ml "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/02_traditional_ml.ipynb"
click node_nasa_eval "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/02_traditional_ml.ipynb"
click node_nasa_dl "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/03_deep_learning_cnn_lstm.ipynb"
click node_nasa_dl_eval "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/03_deep_learning_cnn_lstm.ipynb"
click node_phm_eda "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/01_eda.ipynb"
click node_phm_features "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/02_feature_engineering_ml.ipynb"
click node_phm_ml "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/02_feature_engineering_ml.ipynb"
click node_phm_eval "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/02_feature_engineering_ml.ipynb"
click node_phm_dl "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/03_deep_learning_tcn_xai.ipynb"
click node_phm_xai "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/03_deep_learning_tcn_xai.ipynb"
click node_phm_dl_eval "https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/03_deep_learning_tcn_xai.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_nasa_eda,node_nasa_features,node_nasa_ml,node_nasa_eval,node_nasa_dl,node_nasa_dl_eval toneBlue
class node_phm_eda,node_phm_features,node_phm_ml,node_phm_eval,node_phm_dl,node_phm_xai,node_phm_dl_eval,node_nasa_data,node_phm_data toneAmber
class node_analyst toneIndigo
```

## Components

### NASA IMS Bearings

- [Degradation EDA](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/01_eda.ipynb): `nasa-ims/01_eda.ipynb`
- [Vibration features](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/02_traditional_ml.ipynb): `nasa-ims/02_traditional_ml.ipynb`
- [Traditional ML](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/02_traditional_ml.ipynb): `nasa-ims/02_traditional_ml.ipynb`
- [Held-out bearing test](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/02_traditional_ml.ipynb): `nasa-ims/02_traditional_ml.ipynb`
- [Raw-signal models](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/03_deep_learning_cnn_lstm.ipynb): `nasa-ims/03_deep_learning_cnn_lstm.ipynb`
- [Held-out set test](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/nasa-ims/03_deep_learning_cnn_lstm.ipynb): `nasa-ims/03_deep_learning_cnn_lstm.ipynb`

### PHM Tool Wear

- [Signal EDA](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/01_eda.ipynb): `phm2010/01_eda.ipynb`
- [Cut features](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/02_feature_engineering_ml.ipynb): `phm2010/02_feature_engineering_ml.ipynb`
- [Traditional ML](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/02_feature_engineering_ml.ipynb): `phm2010/02_feature_engineering_ml.ipynb`
- [Held-out cutter test](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/02_feature_engineering_ml.ipynb): `phm2010/02_feature_engineering_ml.ipynb`
- [Deep architectures](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/03_deep_learning_tcn_xai.ipynb): `phm2010/03_deep_learning_tcn_xai.ipynb`
- [Saliency analysis](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/03_deep_learning_tcn_xai.ipynb): `phm2010/03_deep_learning_tcn_xai.ipynb`
- [Held-out cutter test](https://github.com/karankhatavkar/predictive-maintenance-bearing-tool-wear/blob/main/phm2010/03_deep_learning_tcn_xai.ipynb): `phm2010/03_deep_learning_tcn_xai.ipynb`

### Other components

- **Analyst**
- **NASA IMS data**
- **PHM 2010 data**

## Connections

- Analyst → Degradation EDA: analyzes
- Analyst → Vibration features: models
- Analyst → Raw-signal models: models
- NASA IMS data → Degradation EDA: raw signals
- NASA IMS data → Vibration features: bearing signals
- Vibration features → Traditional ML: extracts features
- NASA IMS data → Raw-signal models: raw signals
- Traditional ML → Held-out bearing test: compared on
- Raw-signal models → Held-out set test: tested on
- Analyst → Signal EDA: analyzes
- Analyst → Cut features: models
- Analyst → Deep architectures: models
- PHM 2010 data → Signal EDA: raw signals
- PHM 2010 data → Cut features: cut signals
- Cut features → Traditional ML: extracts features
- PHM 2010 data → Deep architectures: signal data
- Traditional ML → Held-out cutter test: compared on
- Deep architectures → Held-out cutter test: tested on
- Deep architectures → Saliency analysis: explained by