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Preventive Maintenance

Vehicles today generate vast amounts of data every second that automakers can leverage to provide services that will help make the shift from Preventative or condition-based maintenance to Prognostics or Predictive Maintenance. As the mindset shifts towards leading less stressful lives, prognostics can intimate consumers ahead of critical failures in their vehicles leading to breakdowns and disruptions to their planned commute.
Prognostics and Health Monitoring (PHM) are enabled by predicting Remaining Useful Life (RUL) for various components leveraging big data technologies and AI algorithms. These same algorithms when applied in the context of machinery can be used to predict failures of equipment on the manufacturing plants.
At LTI, with our domain expertise and real life experience in building PHM solutions, we are uniquely positioned to recommend the right curated list of data science algorithms and models that refine to create a methodical approach to drive outcomes. Our solutions are portable and can be installed on your existing platforms .

Our Success Stories

LTI’s Enterprise IT Solutions for the Automotive industries enable you to offer superior customer experiences using our proprietary

 
Prognostic Health Management of Service Parts for Leading Automotive Major

Prognostic Health Management of Service Parts for Leading Automotive Major

LTI’s bespoke prognostic solution helped predict the status and expiration of service parts and set up a proactive channel for timely communication.

Welding Analytics for Global Auto Major

Welding Analytics for Global Auto Major

LTI’s prognostic solution helped predict the occurrences of bad welds, resulting in a 20-30% reduction in manual labour for weld inspection.

Increased Asset Shelf Life for US-based Midstream Oil Major

Increased Asset Shelf Life for US-based Midstream Oil Major

LTI helped a major petroleum services company proactively generate views on asset health that helped increase the shelf life and performance of assets

What We Think?

 

Active Condition Monitoring for Accurate Predictive Maintenance