Continuous Inline Meta Machine Learning & CDS runs mass models in parallel & chooses best algos & recommendations
DataRPM delivers industry's only Cognitive Predictive Maintenance (CPdM) Platform for Industrial IoT. DataRPM uses patent pending Meta-Learning technology, integral component of Artificial Intelligence to automate predictions of asset failures. CPdM is a full stack platform available on cloud or on premises that connects to the data lake and then automatically runs multiple Machine Learning experiments, finds patterns & anomalies in data, identifies influencing factors & predictors, and builds an ensemble of predictive models to automate Predictive Maintenance. Not only does CPdM platform delivers descriptive, predictive, prescriptive analytics through Natural Language Discovery module but also provides a closed loop system by integrating these actionable insights through APIs to ERP, CRM, and CMS systems.
The Meta Learning environment runs multiple live automated ML experiments on datasets, extracts data from every experiment, trains an ensemble of models on this meta data repository, applies models to predict the best algorithms and finally builds machine-generated and human verified Machine Learning models for Predictive Maintenance. The entire workflow uses various recipes like feature engineering, segmentation, influencing factors and prediction recipes to give prescriptive recommendations.
Connect to the
Finds Patterns & Anomalies in data
Identify Influencing Factors & Predictors
Builds an ensemble of predictive models
Predictive Mainteinance Insights
Collection of data is our first step to transform raw machine data to meaningful insights. The Data connectors collect unsystematic, incoherent or noisy data coming from different machine sensors, other manufacturing equipment and factory floor systems which come at different time windows. It opens the gateway to predictive maintenance.
Once the data is collected, it is cleansed and aligned to make it usable. The CPdM platform aligns this incoherent machine data by using the feature engineering Meta Learning to derive real value from data. Drastically reduce the number of iterations and combinations that you would need to play with and save huge amount of time.
Identify potential failure areas and analyse combination of sensors that can cause machine failure. The Clustering Meta Learning detects patterns and anomalies for accurate predictions.
We top up our domain and data science expertise on derived patterns to generate labelled training data for faster predictions around asset failure. The power of analytics is maximized by user validation and prior machine fault pattern analysis embedded into this labelled data to enable decision makers to make valuable decisions at the right time.
Our Platform creates and runs thousands of manufacturing models in parallel and identifies the best fit model for you. Predictive Maintenance is automated by the "Algorithmic survival of the fittest" model.
Get powerful insights through our API framework to make well informed smart business decisions.
Optimize maintenance. Reduce operational cost. Derive business benefits.