Lovejinder Singh

Lovejinder Singh has a bachelor's in Air Navigation and a Master's Degree in Aerospace Engineering (Major in Vehicles) from the Polytechnic University of Catalonia. He is currently working in DMD Solutions focused on the sectors of Reliability (FRACAS, RPA, FMEA) and Maintainability (MSG-3, AMM). His thesis verses on Predictive Maintenance Deep Learning methodologies.

Call for partnership in predictive maintenance research project

by Lovejinder Singh | Maintainability

Exploit the full value of aerospace field dataDo you work with aerospace systems and collect large quantities of data? Do you manage fleets’ reliability and failure reports? Would you like to exploit the full value of your data to optimize your maintenance operations?...

Implementation of a Predictive Maintenance Method based on Deep Learning for Aviation

by Lovejinder Singh | Maintainability, RAMS Academy

BIG DATA TO IMPROVE AEROSPACE OPERATIONS In this article, we explain the development of a Predictive Maintenance Deep Learning methodology based on Big Data. The term “Big data” is widely spread these days in technological industries such as aerospace. Collecting a...

Cybersecurity requirements for airborne systems

by Lovejinder Singh | Certification, RAMS Academy, Safety

In the past years, the aircraft systems and components have increased the data connections between them. These connections are delicate links susceptible to security threats. This is the major reason EASA started to work on a Cybersecurity framework for aerospace in...

Best practices for an effective FRACAS implementation

by Lovejinder Singh | RAMS Academy, Reliability

FRACAS (Failure Reporting, Analysis, and Corrective Action System) is one of the most extended methodologies for reliability analysis in the aerospace industry. FRACAS provides a structured process for the calculation of reliability parameters such as the Mean Time...