Predictive Maintenance

Model equipment failures based on data referencing observations of past machine runs and failures. Apply model to current situations to anticipate machine failures and schedule maintenance preemptively.

Step 1:
Load data of past machine runs, labeled with information about whether there has been a failure or not.

Step 2:
Determine influence factors using various attribute weighting algorithms and averaging their weights results

 Step 3:
Train a k-NN model - optimizing for k (the number of reference situations to take into account for prediction) to produce a maximum failure prediction accuracy.

Step 4:
Load new data and apply the machine failure model to current machine runs to predict potential machine failures.

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Step 1:
Load data of past machine runs, labeled with information about whether there has been a failure or not.

Step 2:
Determine influence factors using various attribute weighting algorithms and averaging their weights results

process weights.png
weights.png

Output:

Influence Factors

 Step 3:
Train a k-NN model - optimizing for k (the number of reference situations to take into account for prediction) to produce a maximum failure prediction accuracy.

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Step 4:
Load new data and apply the machine failure model to current machine runs to predict potential machine failures.

Final Step:

Run Process

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Prediction Results

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Final Results

Visualization of results

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