A predictive maintenance model that processes data at high speed supports faster decision-making and real-time monitoring through sensors. The model reduces downtime, improves mission readiness and flight safety for pilots and translates into better fleet and safe availability for operations. While indigenous predictive maintenance is a low-cost force multiplier, its value will depend on data integrity, cyber hardening, and timely project completion.

On 27 May 2026, the Indian Air Force (IAF) and the Indian Institute of Technology (IIT) Bombay formalised three landmark agreements to develop an indigenous, AI-driven maintenance model for the Su-30 MKI fighter aircraft. The Su-30 is already going through several upgrade programmes, and this agreement is a further boost. It is a meaningful step towards ‘Atmanirbhar Bharat’ (self-reliant India). This development can strengthen day-to-day fleet operations and, just as important, lay a path for AI integration across other defence equipment and systems.

This brief argues that indigenous predictive maintenance is a low-cost force multiplier. Still, its value will depend on data integrity, cyber hardening, and completing the project on time. The brief studies the Su-30 upgrade programmes, examines the requirement for AI in the fleet and how it can lift operational efficiency, looks at similar efforts by other air forces, and finally suggests measures to keep in mind while executing such projects.

The Su-30 MKI and Its Upgrade Trajectory

The Su-30 MKI is a twin-seat, long-range fighter built for multirole and air-superiority missions.[1] It was first inducted into the IAF in September 2002.[2] The Russian-origin air-dominance fighter pairs heavy firepower with super-manoeuvrability and, with in-flight refuelling, can roughly double its range of about 3,200 km on internal fuel.[3] The Su-30 forms the backbone of the IAF, with over 270 aircraft in the fleet, making India the world’s largest operator of the type. There have been steady efforts to modernise the aircraft and keep pace with global technological advances.[4]

In 2024, the Ministry of Defence advanced the ‘Super Sukhoi’ modernisation—a project costing around Rs 60,000 crore—to be carried out by Hindustan Aeronautics Limited (HAL) with support from the Defence Research and Development Organisation (DRDO). It aimed to add advanced indigenous mission and weapons systems, stronger electronic warfare protection, new radars for better air-to-air and air-to-ground detection and engagement, and upgraded avionics for greater combat capability. The project has now reached the radar-integration testing stage.[5]

In addition, in September 2024, the Cabinet Committee on Security cleared a purchase worth about Rs 26,000 crore for 240 AL-31FP engines, to be made at HAL’s Koraput division with more than 54 per cent indigenous content, keeping the fleet flying for years to come.[6] The IAF has also worked with the private sector and academic institutions—including IIT Bombay and IIT Jodhpur—to automate maintenance using Artificial Intelligence (AI) and robotics.[7]

The IAF–IIT Bombay Project: Agreement’s Scope and Architecture

In a data-driven era, one of the most significant steps in this upgrade journey is the IAF signing three contracts with IIT Bombay on 27 May 2026 to build an indigenous ‘predictive maintenance’ system for the Su-30 MKI fleet.[8] They aim to develop maintenance technologies that are ‘prognostic and prescriptive in nature’ and built entirely on Indian know-how. [9]

The project will be run by the Centre for Machine Intelligence and Data Science (C-MInDS) and the Mechanical Engineering Department of IIT Bombay, and will monitor engines undergoing mid-life maintenance.[10] The team’s goal is an AI-driven model—a smarter way to read the ‘health index’ of the gas-turbine engines. It will create a virtual copy of each real engine—a ‘digital twin’—to assess the engine’s condition.[11] The ‘digital twin’ is a ‘physics-informed’ model—it blends AI with established engineering, so the virtual engine behaves like the real one, predicts likely faults early, spots problems quickly, flags where attention is needed and suggests the best fixes, while tracking the engine’s key health parameters.[12]

At GMC’25 (the Global Manufacturing Conclave), the Principal Investigator (PI) of this project highlighted how industrial AI and digital twins can transform operations, improve sustainability, and increase throughput—and stressed that data sits at the centre of any AI model.[13] According to the PI, the impact on the IAF could be significant: engine maintenance turnaround times would fall, leading to cost savings and longer engine availability. IIT Bombay further stated that this shift will strengthen the........

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