Predictive Maintenance

MOTIVATION

Interactively Boost Analytical Productivity with enhanced Predictive analytics

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.

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The Problem Canvas
  • Limitations in Statistical techniques
  • Limited availability of datasets
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The Data Question
  • Use the growing volume of available vehicle data to anticipate repair needs and reduce unplanned maintenance. Do the same with your factory equipment

Solution Canvas

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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

Business Impact

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  • The Root Cause Analysis helped in identifying minute causes for the failure of each part in the machine
  • In addition to the identification of root causes, geography-wise distributions of such causes added to the efficient planning of maintenance activities thereby reducing costs and improving maintenance planning
  • The Relationship Association and Correlation analysis helped in diving deeper into the causes to identify if any long-term and permanent fixation/ action was required
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