MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641078667 A) filed by Dr. N. Satish Kumar; Mrs. K. Santhosh Priya; Dr. K. Ratna Raj; Mr. D. Raju; Mr. A. S. Praveen; Mr. K. Prudvi; Mr. M. Hithesh Sai; and Mrs. Kovelakuntla Sumalatha on June 25, 2026, for Hybrid Triboelectric-Piezoelectric Nanogenerator With Self-Powered Mechanical Vibration Monitoring For Smart Industrial Predictive Maintenance.
Inventors include Dr. N. Satish Kumar; Mrs. K. Santhosh Priya; Dr. K. Ratna Raj; Mr. D. Raju; Mr. A. S. Praveen; Mr. K. Prudvi; Mr. M. Hithesh Sai; and Mrs. Kovelakuntla Sumalatha.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: A novel Hybrid Triboelectric-Piezoelectric Nanogenerator with Self-Powered Mechanical Vibration Monitoring for Smart Industrial Predictive Maintenance is proposed for simultaneously harvesting mechanical vibration energy and performing real-time condition monitoring of industrial equipment. The invention integrates a triboelectric nanogenerator module, a piezoelectric nanogenerator module, an energy conditioning circuit, an energy storage unit, a vibration signal processing module, a wireless communication interface, and an intelligent predictive maintenance platform within a single compact architecture. The hybrid configuration enables the system to convert low-frequency and irregular mechanical vibrations generated by industrial machinery into electrical energy while also producing vibration signatures that can be analyzed to assess machine health. The triboelectric unit efficiently captures energy from surface contact-separation motions, while the piezoelectric unit converts dynamic mechanical strain into electrical signals. The combined output improves energy harvesting efficiency, sensing accuracy, and operational reliability compared with conventional single-source vibration sensors. The generated energy powers the monitoring electronics, eliminating the need for external batteries or wired power sources. Advanced signal processing algorithms analyze vibration amplitude, frequency, and pattern variations to identify developing faults, wear conditions, imbalance, misalignment, and bearing degradation. The proposed invention provides a fully autonomous, self-sustaining predictive maintenance solution suitable for factories, power plants, manufacturing facilities, transportation systems, and smart industrial environments.
Disclaimer: Curated by HT Syndication.
