MOTIVATION
In almost all of the manufacturing units, predictive maintenance is of paramount importance, given the fact that they increase the overall efficiency of the unit and at the same time, reduce revenue burden and avoid abrupt (and often expensive) cost liabilities. With huge amount of data generated through sensors located on tools and machines and employing advanced analytical tools, majority of the manufacturers are discovering that predictive maintenance ensures maximum ROI from their equipment. This is especially true for industries that are adopting digital manufacturing and also predictive maintenance solution.
Approach:
Rubiscape helps experts in the field of Predictive Maintenance by creating predictive ML models that learn from historical data and predict and analyze machine failure patterns. This helps in optimum resource utilization and predicting failure before it occurs.
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Toolset:
RubiStudio – Data Preparation, Statistical Analysis and Hypothesis, Code Fusion, Sampling, Outlier Detections
RubiML – Regression, Classification models
RubiSight – Dashboarding and Story
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Skillset:
Machine Learning
Domain Knowledge
Data wrangling
Data visualisations
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Dataset:
The dataset contains information on the machine model and its age.
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OUTCOMES