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 LTIMindtree, 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 .

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Prognostics and health management based Preventative Maintenance:

Data lake architecture, data pipeline design and data lake processing layer design for data ingestion, transformation and processing.
Cost effective lake formation for optimum utilization of cloud resources for query processing and reporting.
Smart parsing and processing of telematics data that delivers analysis ready inputs to data scientists and business users.
Smart entity linking systems that stitch Enterprise datasets for model training.
Sophisticated computation for degradation rate that can be trained through Machine Learning algorithms for the future data usage.
Machine Learning algorithms for RUL calculations for various parts.
Proactive repair order advice based on RUL value and component failure detection

Key Features

Artificial intelligence based models conjoining enterprise data with publicly available data
Condition monitoring and data analysis expertise to create new value from existing data.
Problem decomposition, organizing the data for model training and combining predictions to generate recommendation

Benefits

Strong technical expertise in pre-processing publicly available data with enterprise data to drive accurate geolocation interpretation
Rich data science and machine learning capability to build advanced models for accurate and scalable prediction
Natural Language Processing (NLP) based feature extraction to enrich and translate telematics, repair and warranty data
Rich Artificial intelligence solution capabilities conjoining computer vision, NLP and publicly available data with Enterprise data to drive holistic insights.
Domain expertise in Prognostics and Health management Based Preventative Maintenance can guide your team in selection of assets to start with small, proven gains and then embrace the scaling up to more parts to multiply gains.