The automotive industry continues to be a hotbed of innovation, with activity driven by enhanced driving experience, safety, and emission standards, and growing importance of technologies such as electric, connected and autonomous vehicles. In the last three years alone, there have been over 1.2 million patents filed and granted in the automotive industry, according to GlobalData’s report on Artificial intelligence in Automotive: Predictive maintenance systems. Buy the report here.

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However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.

Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.

290+ innovations will shape the automotive industry

According to GlobalData’s Technology Foresights, which plots the S-curve for the automotive industry using innovation intensity models built on over 619,000 patents, there are 290+ innovation areas that will shape the future of the industry.

Within the emerging innovation stage, manufacturability analysis, autonomous parking, and lidar for vehicle anti-collision are disruptive technologies that are in the early stages of application and should be tracked closely. Speed profile estimation, smart light dimmers, and driver drowsiness detection are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are road slope estimation and adaptive cruise control, which are now well established in the industry.

Innovation S-curve for artificial intelligence in the automotive industry

Predictive maintenance systems is a key innovation area in artificial intelligence

Predictive maintenance is a technique that finds anomalies in operations and potential flaws in equipment and processes so you can fix them before they break down. The primary objective of predictive maintenance is to foresee equipment failures based on specific parameters and factors. When a failure is anticipated, manufacturers implement the necessary corrective or planned maintenance. Condition monitoring is essential to predictive maintenance.

GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 50+ companies, spanning technology vendors, established automotive companies, and up-and-coming start-ups engaged in the development and application of predictive maintenance systems.

Key players in predictive maintenance systems – a disruptive innovation in the automotive industry

‘Application diversity’ measures the number of different applications identified for each relevant patent and broadly splits companies into either ‘niche’ or ‘diversified’ innovators.

‘Geographic reach’ refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.

Patent volumes related to predictive maintenance systems

Company Total patents (2010 - 2022) Premium intelligence on the world's largest companies
Strong Force Iot Portfolio 2016 329 Unlock Company Profile
Fanuc 75 Unlock Company Profile
Mitsubishi Electric 74 Unlock Company Profile
Hitachi 62 Unlock Company Profile
Siemens 53 Unlock Company Profile
NEC 32 Unlock Company Profile
Applied Materials 27 Unlock Company Profile
Samsung Group 26 Unlock Company Profile
Boeing 25 Unlock Company Profile
Honeywell International 23 Unlock Company Profile
Veolia Environnement 21 Unlock Company Profile
Toshiba 20 Unlock Company Profile
General Electric 18 Unlock Company Profile
Palantir Technologies 18 Unlock Company Profile
Tokyo Electron 16 Unlock Company Profile
Safran 16 Unlock Company Profile
Panasonic 15 Unlock Company Profile
Omron 15 Unlock Company Profile
Doosan 15 Unlock Company Profile
Hewlett Packard Enterprise 15 Unlock Company Profile
International Business Machines 14 Unlock Company Profile
Rockwell Automation 11 Unlock Company Profile
AB SKF 11 Unlock Company Profile
Falkonry 11 Unlock Company Profile
Tata Sons Pvt 11 Unlock Company Profile
Scottish Mortgage Investment Trust 11 Unlock Company Profile
Johnson Controls International 10 Unlock Company Profile
Shinwa Controls 10 Unlock Company Profile
Intel 10 Unlock Company Profile
ASML Holding 10 Unlock Company Profile
Preferred Networks 9 Unlock Company Profile
General Motors 9 Unlock Company Profile
Capital One Financial 8 Unlock Company Profile
Emerson Electric 8 Unlock Company Profile
Yokogawa Electric 7 Unlock Company Profile
Munchener Ruckversicherungs-Gesellschaft Aktiengesellschaft (Munich Re) 7 Unlock Company Profile
Nippon Telegraph and Telephone 7 Unlock Company Profile
JTEKT 7 Unlock Company Profile
Lockheed Martin 7 Unlock Company Profile
Nissan Motor 6 Unlock Company Profile
Buhler 6 Unlock Company Profile
Furukawa 6 Unlock Company Profile
Raytheon Technologies 6 Unlock Company Profile
Toshiba Mitsubishi-Electric Industrial Systems 6 Unlock Company Profile
Sumitomo Heavy Industries 6 Unlock Company Profile
Xerox Holdings 6 Unlock Company Profile
Toyota Motor 6 Unlock Company Profile
Accenture 6 Unlock Company Profile
Oracle 6 Unlock Company Profile
Grid4C 6 Unlock Company Profile

Source: GlobalData Patent Analytics

Strong Force Iot Portfolio 2016 is one of the top companies to file for predictive maintenance systems patents with 329 patents filed. The company provides an industrial machine data analysis facility that creates streams of industrial machine health monitoring data by applying machine learning to data, which is representative of the conditions of individual industrial machine components. Fanuc, NEC, and Intel are a few other notable patent filers in the same industry.

To further understand how artificial intelligence is disrupting the automotive industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) in Automotive.

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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalData’s Patent Analytics tracks patent filings and grants from official offices around the world. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.