---
title: "Bearing Analytics"
id: "13640"
type: "portfolio-case-study"
slug: "bearing-analytics"
published_at: "2024-10-02T10:44:52+00:00"
modified_at: "2024-10-02T10:57:31+00:00"
url: "https://hblabgroup.com/case-study/bearing-analytics/"
markdown_url: "https://hblabgroup.com/case-study/bearing-analytics.md"
taxonomy_category_industries:
  - "Manufacture"
taxonomy_services:
  - "Al Solution"
---

# Bearing Analytics

A Japanese Heavy Industries

- [Al Solution](https://hblabgroup.com/services/al-solution/)

![Bearing Analytics](https://hblabgroup.com/wp-content/uploads/2024/10/Portfolio-HBLAB-GLOBAL-bearing.png)

## Challenges

The customer wanted to use AI to predict the operating condition of bearings in industrial fans

## HBLAB's Solutions

- Analyze and process data from bearing sensors (~ 300 sensor from 64 fans) such as temperature, vibration, amount of lubricant used…
- Train an AI (time series) model to understand historical data and predict the future performance of bearings (temperature and vibration)

## Project details

- Used Technologies 2 AI Engineer + 0.5 AI PM
- Development Team 2 AI Engineer + 0.5 AI PM
- Duration 3 months

## Results

- For temperature (range value 20 -> 60 Celsius): MSE ~ 2
- For vibration (range value ~0.7 -> ~1.2 mm/s): MSE ~ 0.05

*MSE: Mean squared error

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