Introduction to MindSphere Predictive Learning

MindSphere Predictive Learning provides a workspace into which users can bring data in structured, unstructured, and semi-structured formats, and multiple sources. Once your data is in the workspace, you can run analyses that help you anticipate coming events and predict when issues might arise. This will allow you to boost product quality and proactively:

• Intervene with preventive maintenance
• Disrupt fatal sequences of manufacturing events
• Reduce the number of field failures
• Enhance product performance
• Enhance customer experience

MindSphere Predictive Learning employs machine learning (ML) algorithms, statistics functions, transformations, and filtering to bring you the most comprehensive and flexible means to access and work with your data.

Features of MindSphere Predictive Learning

MindSphere Predictive Learning helps companies attain unprecedented product quality. Forecasting field performance reveals not only potential failure sequences, but also optimal sequences for higher product performance. By correlating field performance data with product design and manufacturing process data, MindSphere Predictive Learning enables you to improve and optimize product quality – before the product leaves the factory. Tremendous value accrues as these higher quality products translate into lower service and warranty costs. MindSphere Predictive Learning allows you to benefit from:

• Simple data collection and preparation from external sources
• Rapidly build and execute predictive models
• Automatically analyze numerous sequences through predictive algorithms
• Discover the myriad sequences that create product field failures and preemptively disrupt fatal sequences
• Proactively push fixes (parts, software, hardware, or firmware) to resolve predicted points of failure or degraded performance
• Forecast field performance to improve product quality

You can also schedule preventive maintenance before failures occur, and:

• Identify patterns within normal operations that may otherwise go unnoticed
• Identify precursors, such as sudden spikes within a normal operating range and specific test results
• Correlate the precursors with machine maintenance records to predict which machines may suddenly require unscheduled maintenance or downtime
• Schedule preventative maintenance to avoid unscheduled machine downtime