Automotive industry
Automotive companies can use Predictive Maintenance Solutions to avoid early failures of vehicles.
We develop customized AI-based predictive maintenance software for organizations. Our predictive maintenance consultants guide and support you from the initial concept to the implementation of the solution.
The timing of maintenance, repair, and replacement of machines and vehicles is a central challenge for many companies. Especially sudden defects can disrupt the production process, interrupt supply chains, and cause high repair costs. Predictive Maintenance includes methods and models from statistics and machine learning to detect defects and maintenance needs of devices, vehicles, machines, and systems early on. Accordingly, Predictive Maintenance helps companies to minimize downtime, extend product lifecycles, and reduce operating costs.
Due to the advancing networking of vehicles and machines, Predictive Maintenance has become the central IoT use case for many companies. Historical operating data of the respective machines are collected via sensors and linked to past failures and defects. Mathematical models and algorithms are used to establish patterns and relationships between operating data and failure times. Subsequently, future failures can be predicted based on the currently available data and measurements.
Our machine learning and AI consultants advise and support you in developing predictive maintenance software. We design the necessary systems and implement them into your IT environment. Take advantage of the opportunities that AI-based predictive maintenance solutions offer for your business.
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Early detection of defects or maintenance needs can minimize unexpected downtime of machines and systems.
Regular maintenance of vehicles, machines, and systems before they fail can avoid expensive repairs.
Proactive maintenance systems enable the extension of the life cycle of machines, as wear and tear can be remedied early on.
Through statistical analyses, influencing factors on the failure of machines and components can be identified.
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