---
pdfexport: true
alias: tutorials-predictivemaintenance
timetoread: true
tutorial: full
description: "Implement predictive maintenance for a milling machine using machine learning to forecast failures and optimize operations"
---

# Predictive Maintenance with a Milling Machine Use Case  

The information that follows serves to guide you on how to implement a predictive maintenance workflow using machine learning.

## Overview  

Predictive maintenance uses machine data to predict when a failure is likely to occur, which enables proactive maintenance to minimize downtime and reduce costs. In this tutorial, we will demonstrate how to implement predictive maintenance for a milling machine, leveraging insights gathered during data collection.  

This use case focuses on the following key failure scenarios:

1. **Tool Wear Failure (TWF)**  
2. **Heat Dissipation Failure (HDF)**  
3. **Power Failure (PWF)**  
4. **Overstrain Failure (OSF)**  
5. **Random Failures (RNF)**  

We will also estimate the Remaining Useful Life (RUL) of the milling machine based on observed time intervals between failures.

## Preconditions  

Before implementing predictive maintenance, ensure the following:

1. **Data Collection**:  
    - Historical failure data with features such as tool wear, temperature, rotational speed, torque, and power usage.  

2. **Analytical Tools**:  
    - A data visualization platform (example: Grafana) and access to Data Platform ML Model.  

![Diagram showing a data point with various metrics, including "PEBS" and "Alarm", in a data visualization platform.](../../images/pdm_diagram.png)

## Steps to Implement Predictive Maintenance  

As context, let's consider a milling machine with the following failure scenarios:

- **TWF**: Tool wear reaches its limit (200-240 min).  
- **HDF**: Heat dissipation fails when temp difference < 8.6 K and rotational speed < 1380 rpm.  
- **PWF**: Power fails when P < 3500 W or P > 9000 W.  
- **OSF**: ToolWear x Torque > 11000 minNm for load L, 12000 for M, 13000 for H.  
- **RNF**: Rare random failures (<1%).  

### Step 1: Data Preprocessing  

1. **Import Data**: Load the data via Post Event and store it in a Data Set. For more information, see [[tutorials-outlierdetection]]. Ensure it contains fields you need.  
2. **Clean Data**: Handle missing values and outliers. Standardize or normalize data for consistent analysis. This can be done for you in the ML Model wizard.
3. **Feature Engineering**:  
    - Optionally, we may compute derived metrics like power using P = Torque * Rot. Speed (rad/s).

    !!! info
        Once again, these last steps may be performed within the ML Model.

### Step 2: Exploratory Data Analysis  

1. **Visualize Data**: Use graphs and dashboards to identify trends and patterns.  
    - Example: Plot tool wear over time to observe failure intervals.  
2. **Correlations**: Analyze correlations between failure events and parameters like tool wear, temperature, and power.  
3. **Failure Insights**: Validate that failure conditions align with the thresholds provided:  
    - Example: Check if failures occur when temp difference < 8.6 K and rotational speed < 1380 rpm (HDF).  

![dataset](../../images/pdm_dataset.png)

In the image, we can see the data structure along with their correlation matrix. For this particular dataset, we observe the dataset is not balanced - for classification problems, you may first consider apply over-sampling and/or under-sampling techniques.

### Step 3: Predictive Modeling  

Let's divide the predictive modeling into two parts:

1. **Label RUL**:  
    - Calculate the time remaining until the next failure for each data point.  
    - Use a linear approximation based on the observed time intervals between failures.  

    ![rul](../../images/pdm_rul.png)

2. **Train RUL Model**:  
    - Train the model using features like tool wear, temperature, power, and torque. Select, during the ML Model wizard, the RUL column as label. This will define the experiment as a regression problem - you can check the Details view later on.

    - Use the ML Model to predict RUL. Validate the ML Model's performance.

    ![rul train](../../images/rul_training.png)

As this is a regression problem, choose a metric that fits your needs. When it is trained, you only need to set it effective to deploy the model in MES ecosystem.

![Screenshot showing a predictive modeling step with a histogram labeled "Prediction Error" and a model named "Be Your Model".](../../images/dataset_test.png)

!!! info
    Remember that the ML Model consist in an AutoML framework, thus, it will automatically select the best model for your problem. For more information, see this [link](https://learn.microsoft.com/en-us/dotnet/machine-learning/resources/tasks).

### Step 4: Integrate with Real-Time Monitoring  

1. **Set Up IoT workflow**:  
    - Assign an IoT workflow to post the readings. The workflows, when triggered, will then predict the time to fail and record the value in a Data Set via Post Event, as shown in the image:

    ![Diagram showing a workflow diagram illustrating the integration with real-time monitoring.](../../images/workflow_diagram.png)

2. **Add ML Task**:  
    - Drag the ML Task and connect the inputs, but first you must select the right IoT Event Definition - select the settings button to do so. For more information, see [[tutorials-outlierdetection]].

3. **Add API Post Event Task**:
    - Add API Post Event Task to record the predicted RUL in a Data Set.
    - Select the settings button to choose the right event definition.

When this is done, connect the blocks and run the workflow by manually posting an event. Example:

![post event](../../images/pdm_post_event.png)

You should see a new record in the predicted RUL Data Set.

### Step 5: Validate and Optimize

1. **Create Dashboards**:  
    - Display real-time RUL estimates, failure type predictions, and key parameters (example: tool wear, power).

2. **Test Predictions**:  
    - Compare model predictions with actual failures to evaluate accuracy.

3. **Adjust Models**:  
    - Refine thresholds and retrain models with updated data to improve predictions.

4. **Monitor Performance**:  
    - Continuously monitor the workflow to ensure reliability.  

![dashboard](../../images/pdm_dashboard.png)

## Final remarks

The example dashboard for the milling machine could include:

- **Tool Wear Monitor**: Visualize tool wear over time, highlighting when it nears failure thresholds.  
- **Telemetry Trends**: Show temperature differences and power consumption to detect anomalies.  
- **RUL Estimation**: Display predicted RUL in minutes.  

By following this tutorial, you can build a predictive maintenance workflow for a milling machine that improves uptime, reduces costs, and ensures smooth production processes. For further assistance, refer to your data analytics platform documentation or consult with your engineering team.