The technology industry continues to be a hotbed of patent innovation. Activity is driven by increasing adoption of Internet of Things (IoT) devices, which provide a wealth of data for predictive maintenance, as well as the potential for substantial cost savings and efficiency improvements, and growing importance of technologies such as machine learning models for predictive analytics, IoT sensors for data collection, and cloud computing for data processing and storage, collectively driving advancements in intelligent predictive maintenance solutions. In the last three years alone, there have been over 4.1 million patents filed and granted in the technology industry, according to GlobalData’s report on Artificial intelligence in technology: intelligent predictive maintenance. Buy the report here.
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 stabilizing 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.
190+ innovations will shape the technology industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the technology industry using innovation intensity models built on over 1.5 million patents, there are 190+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, GenAI for design, finite element simulation, and deep reinforcement learning are disruptive technologies that are in the early stages of application and should be tracked closely. AI in EHR, intelligent predictive maintenance, and forward inferencing are some of the accelerating innovation areas, where adoption has been steadily increasing.
Innovation S-curve for artificial intelligence in the technology industry
Intelligent predictive maintenance is a key innovation area in artificial intelligence
Intelligent predictive maintenance involves employing cutting-edge technologies such as sensors, data analysis, and machine learning to monitor asset conditions and foresee maintenance requirements, streamlining maintenance operations. Through ongoing data collection and pattern analysis, these systems offer proactive insights and suggestions, empowering organizations to minimize downtime, optimize resource allocation, and enhance overall operational effectiveness.
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 370+ companies, spanning technology vendors, established technology companies, and up-and-coming start-ups engaged in the development and application of intelligent predictive maintenance.
Key players in intelligent predictive maintenance – a disruptive innovation in the technology industry
‘Application diversity’ measures the number of applications identified for each patent. It broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of countries each patent is registered in. It reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to intelligent predictive maintenance
Source: GlobalData Patent Analytics
Among the companies innovating in intelligent predictive maintenance, General Electric is one of the leading patent filers. The company's patents comprise model library designed for system modelling, containing numerous subsystem models, each capable of assessing reliability. Additionally, it includes inputs for fault tolerance and maintenance policy. The system is equipped with a Dynamic Risk Calculation Engine (DRCE) that utilizes a user-defined subset of subsystem models, along with the fault tolerance and maintenance policy inputs, to compute the overall risk of an apparatus. Other prominent patent filers in the space include Siemens and Hitachi.
In terms of application diversity, Geotab leads the pack, while Rolls-Royce Holdings and Raytheon Technologies stood in the second and third positions, respectively. By means of geographical reach, Geotab held the top position, followed by Daikin Industries and Emerson Electric.
Intelligent predictive maintenance in AI holds immense significance in optimizing the upkeep of assets and machinery. By leveraging advanced technologies such as sensors, data analysis, and machine learning, it allows for the proactive detection of potential issues, enabling timely maintenance to prevent costly breakdowns. It not only reduces downtime, but also maximizes operational efficiency, ultimately leading to substantial cost savings and improved overall productivity in various industries reliant on machinery and equipment.
To further understand the key themes and technologies disrupting the technology industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI).