Built to Last: Predictive Maintenance and Intelligent Healthcare
The Matrix AI Network believes in socially beneficial applications. Matrix 2.0 is perfectly suited for intelligent maintenance and healthcare industries.
The three core pillars of artificial intelligence are data, computing power and AI models. Following the release of the Matrix 2.0 Green Paper, the team is publishing a series of articles delving deeper into key aspects of Matrix 2.0. Part 1 of this series introduced the relationship between data, computing power and AI models. Part 2 introduced a few prominent data and computing power issues. Part 3 introduced AI models and the Matrix AI marketplace. This article focuses on AI applications in predictive maintenance and intelligent healthcare fields.
Advancing technological trends and a growing demand for more reasonable operational costs set the stage for a transition from preventive maintenance to intelligent predictive maintenance. This shift is expected to save the manufacturing industry USD 630 billion by 2025.
In contrast to preventive maintenance, predictive maintenance effectively maintains devices and cuts operational costs by constantly monitoring a device’s status and offering real-time information to proactively issue warnings and avoid potential faults.
By combining a blockchain-powered multi-dimensional big data platform and IoT, the Matrix AI Network is developing a series of solutions including fault prediction, fault diagnosis and process optimization systems.
Fault prediction system
Matrix is developing a fault prediction system to monitor, predict and analyze equipment. The system comprises three primary layers:
1. The first layer involves data collection and modeling. Sensors collect data which is fed into models to anticipate and estimate future equipment degradation based on the physical modeling of specific equipment parts.
2. The second layer involves fault warning informed by deep learning models trained using collected data. These models are tuned to identify likely problems in the equipment.
3. The third layer involves predicting secondary fault-related consequences, as well as correlating specific data with specific faults.
Fault diagnosis system
The fault diagnosis system comprises a full analysis of an isolated fault including a careful examination of failure modes and the influences of outlier data. The fault diagnosis system also provides relevant advice according to maintenance records and specific operation guidelines.
Process optimization system
The process optimization system takes inputs including design objectives and other initial parameters and uses machine learning and other AI processes to attack and identify any vulnerabilities in order to prompt alternative design decisions.
These types of predictive maintenance will result in fewer equipment faults, increased diagnostic efficiency and fully optimized operations. Such a predicative maintenance can reduce faults in devices, improve diagnosis efficiency and optimize operations system and come to become more digital with the integration of digital twin, a digital replica of a living or non-living physical entity.
According to CBInsights, a lack of high-quality data and ineffective privacy protection are hindering the rapid development of AI in healthcare. To remedy the situation, Matrix 2.0 aims to build a safer, sustainable and more accessible intelligent platform with higher accuracy to ensure direct access to medical information and effective treatment of patients.
In the Matrix 2.0 distributed storage system, personal information is stored separately in the blockchain and undergo distributed analysis to better protect data privacy.
High quality data empowers this intelligent platform to produce more accurate diagnosis and distinguish between a greater number of disease types.
Access to medical treatment
Patients can easily get access individual diagnostic results by uploading industry-standard medical data or CT scans by paying a small fee in MAN tokens.
Outstanding data scientists, together with an ever-growing number of tools and models provided by the Matrix AI Network will enable medical professionals to directly optimize data and train AI models to ensure the sustainability of this intelligent platform.
Today, Matrix’s AI-powered cancer diagnosis system is already in use in multiple hospitals in China. The world’s first diagnostic model for small cell cancer and lifespan prediction systems have seen early successes. With the cooperation of valuable partners, such as bitgrit , more high-quality data and AI models will provided the basis for the sustainable development of Matrix’s intelligent healthcare platform.
Besides predicative maintenance and intelligent health care, Matrix 2.0 will explore the potential of AI and blockchain technologies in financial industries by developing a decentralized consumer credit protection platform, an intelligent consumer credit system and contractual fund management systems to address ongoing concerns regarding data privacy and capital safety. Matrix 2.0 is inextricably tied to AI and blockchain technologies. Development in these areas will also open new worlds of possibilities.