MUMBAI, India, June 19 -- Intellectual Property India has published a patent application (202524084593 A) filed by Intangles Lab Private Ltd. on September 05, 2025, for Normal Operation Model Generation And Anomaly Detection For A Common Rail Internal Combustion Engine.
Inventors include Bhushan Dayaram Patil; Hariharan Ravishankar; Vikram Reddy Melapudi; Abhijit Vishwas Patil; Shafaq Ansari; Nishant Srivastava; Harleen Kaur Bagga; Mukund Nagare; and Aman Singh.
The application for the patent was published on June 05, 2026, under issue no. 23/2026.
Abstract: A method for generating a normal operation model indicative of a normal operational condition of a fuel rail of a common rail internal combustion engine. The method comprises receiving, from an engine management system of the common rail internal combustion engine via a data interface, engine model data obtained periodically during operation of the common rail internal combustion engine. The engine model data comprises fuel rail pressure data indicative of a fuel rail pressure of the fuel rail and engine speed data indicative of an engine speed of the engine. The method further comprises storing the engine model data in a memory. The engine model data is accumulated over a sampling time period. The method further comprises dividing, by a processor, the fuel rail pressure data into a predetermined number of engine speed bins derived from the engine speed data during the sampling time period so as to generate binned engine model data indicative of frequency of occurrence of fuel rail pressures within each bin, and generating, by the processor for each bin of the predetermined number of bins, statistical attribute model data based on the binned engine model data. The method also comprises generating, by the processor, the normal operation model based on the statistical attribute model data for each bin when taken together, and storing, in the memory, the normal operation model for comparison with engine test data accumulated over a detection time period for enabling detection of an anomaly in an operational condition of the fuel rail based on comparison between the engine test data accumulated over the detection time period and the statistical attribute model data of the normal operation model.
Disclaimer: Curated by HT Syndication.
