Efficient signal processing algorithms determine need for preventative maintenance
This solution use case is provided by Maya HTT, a MindSphere® and Mendix partner. Central to the use case is the Maya HTT Smart Signal Explorer application, developed for deployment on MindSphere. It provides a toolbox of advanced signal processing math functions for use on IoT data from assets connected to MindSphere. Maya HTT and users can use Siemens’ Mendix low code application development platform to extend Smart Signal Explorer capabilities (e.g., for real-time alerting, custom reporting, and building predictive maintenance algorithms). The easy-to-use visualization interface helps users improve the health and condition of machinery by identifying performance degradation and recognizing the need for preventative maintenance. Workflows are accelerated through efficient signal processing directly within the MindSphere IoT platform, eliminating the need to work offline and enabling maintenance to be scheduled to minimize downtime.
Smart Signal Explorer provides its own signal processing, math functions and algorithms to allow subject matter experts to identify performance degradation and recognize the need for preventative maintenance.
Maya HTT offers a range of professional services to deploy this solution, depending on your needs, and accelerate your digital transformation. Services may include consulting, implementation, integration, customization and training.
Siemens’ Mendix low code application development platform to optionally extend Smart Signal Explorer capabilities for real-time alerting, custom reporting, building predictive maintenance algorithms, etc.
MindAccess™ IoT Value Plan connects your assets and uses MindSphere applications to leverage your data.
MindService® offers training and professional services to support the development and implementation of your MindSphere solution.
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