For commercial airlines, keeping flights running safely and on time is the highest priority. With limited information, identifying and isolating plane maintenance needs could take hours, even with an experienced crew. Read more to learn how SparkCognition used its natural language processing platform DeepNLP to mine work orders to identify faults and corrective actions.
Other content in this Stream
Why Machine Learning is the Future of Predictive Maintenance in the Airline Industry
Preventing failures and accidents is critical for the aerospace industry. Minor incidents can ground planes for extended periods, and a single day of grounding for a plane costs roughly four to five
AI in Biometrics: The Flight Path to Safer Aviation
When boarding an airplane, you’re probably not wondering how many hours of sleep your pilot got last night. However, new research into the effects of fatigue on pilot safety and effectiveness...
The Practical Guide to Industrial AI: Benefits and Implementation
Artificial intelligence is certainly not a new topic. The field has seen starts and stalls, as many of AI's successes have been promising for research progress. With new developments and impactful
5:22Shaping the Future of Travel and Transport - Boeing and SparkCognition
Shaping the Future of Travel and Transport - Boeing and SparkCognition.Boeing is collaborating with SparkCognition to deliver unmanned aircraft system traffic management solutions.
A Cognitive Approach to Industrial Safety and Efficiency
SparkPredict enables truly predictive capabilities to deliver millions of dollars in cost savings and operational efficiency improvements to machine operators. This not only provides more time for...
22:57Augmenting our World With AI 3.0 - SparkCognition at the 2017 Honeywell Tech Symposium
"Software is eating the world and AI is eating software." SparkCognition CEO, Amir Husain discusses scaling, applying, and the third coming of AI at the 2017 Honeywell Tech Symposium. Want to learn m
A More Effective Approach to Unsupervised Learning with Time Series Data