Rotating Shaft Vibration Data

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Rotating Shaft Vibration Data

This post describes how to perform AI learning based on vibration data of a production facility shaft and detect abnormalities.

Table of Contents

  1. Data Introduction
  2. Data Visualization with Machbase Neo
  3. Table Creation and Data Upload in Machbase Neo
  4. Experimental Methodology
  5. Experiment Code
  6. Experimental Results

1. Data Introduction


  • DataHub Serial Number: 2024-8.
  • Data Name: Rotating Shaft Vibration Data.
  • Data Collection Methods: Vibration data was collected at a rate of 4096 samples per second through three sensors, inducing an imbalance on the shaft using weights of various widths.
  • Data Source: Link
  • Paper : Link
  • Raw data size and format: 11GB, CSV.
  • Number of tags: 60.
    • Each tag has 6 instances.
TAG DESCRIPTION
V_in Input voltage values.
Measured_RPM Rotational speed of the rotating body.
Vibration_1 Data from the first vibration sensor.
Vibration_2 Data from the second vibration sensor.
Vibration_3 Data from the third vibration sensor.
Unbalance_Factor Unbalance values.
A table showing the width, weight, and other details of the weights installed for each experimental group

2. Data Visualization with Machbase Neo


  • Data visualization is possible through the Tag Analyzer in Machbase Neo.
  • Select desired tag names and visualize them in various types of graphs.
  • Below, access the 2024-8 DataHub in real-time, select the desired tag names from the data of 60 tags, visualize them, and preview the data patterns.
DataHub Viewer

3. Table Creation and Data Upload in Machbase Neo


  • In the DataHub directory, use setup.wrk located in the Rotating Shaft Vibration Dataset folder to create tables and load data, as illustrated in the image below.

1) Table Creation

  • The table is created immediately upon pressing the "Run" button in the menu.
  • If the vibe_unbal table exists, execute the first line and then the second. If it does not exist, start from the second line.

2) Data Upload


  • Loading tables in two different ways.
Method 1) Table loading method using TQL in Machbase Neo (since machbase-neo v8.0.29-rc1

  • Pros

    • Machbase Neo loads as soon as you hit the launch button.

  • Cons

    • Slower table loading speed compared to other method.
Method 2) Loading tables using commands

  • Pros

    • Fast table loading speed.

  • Cons

    • The table loading process is cumbersome.
    • Run cmd window - Change machbase-neo path - Enter command in cmd window.
  • If run the below script from the command shell, the data will be entered at high speed into the vibe_unbal table.
curl http://data.yotahub.com/2024-8/datahub-2024-08-vibration_unbalance.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns vibe_unbal
  • If specify a separate username and password, use the --user and --password options (if not sys/manager) and add the options as shown below.
curl http://data.yotahub.com/2024-8/datahub-2024-08-vibration_unbalance.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns vibe_unbal --user USERNAME --password PASSWORD

4. Experimental Methodology


  • Model Objective: Rotating Shaft Anomlay Detection.
  • Tags Used: 0th group tags, 1th group tags.
  • Model Configuration: XGBoostClassifier Ensemble.
  • Learning Method: Supervised Learning.
    • Train: Model Training.
    • Test: Model Performance Evaluation.
  • Model Performance Metric: F1 Score.
  • Data Loading Method
    • Loading the Entire Dataset.
    • Loading the Batch Dataset.
  • Data Preprocessing
    • Preprocess three datasets, each corresponding to different vibration data.
    • Hanning Window.
    • Fast Fourier Transform.
    • MinMax Scaling.
    • Principal Component Analysis.

5. Experiment Code


  • Below is the code for each of the two ways to get data from the database.
  • If all the data can be loaded and trained at once without causing memory errors, then method 1 is the fastest and simplest.
  • If the data is too large, causing memory errors, then the batch loading method proposed in method 2 is the most efficient.

Method 1) Loading the Entire Dataset


  • The code below is implemented in a way that loads all the data needed for training from the database all at once.
  • It is exactly the same as loading all CSV files (The only difference is that the data is loaded from Machbase Neo).
  • Pros
    • Can use the same code that was previously utilizing CSVs (Only the loading process is different).
  • Cons
    • Unable to train if trainable data size exceeds memory size.

Method 2) Loading the Batch Dataset


  • Method for loading data from the Machbase Neo for a single batch size.
  • The code below is for fetching a time range sequentially for a single batch size.
  • Pros
    • It is possible to train the model regardless of the data size, no matter how large it is.
  • Cons
    • It takes longer to train compared to method 1.

6. Experimental Results


Method 1) Loading the Entire Dataset Result


Method 2) Loading the Batch Dataset Result


  • The F1 score for loading the entire dataset resulted in 1.0, loading the batch dataset resulted in 0.92.
  • Machine learning algorithms like XGBoost do not seem well-suited for batch learning.





※ Various datasets and tutorial codes can be found in the GitHub repository below.

datahub/dataset/2024 at main · machbase/datahub
All Industrial IoT DataHub with data visualization and AI source - machbase/datahub
GitHubmachbase

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