The startup offers a real-time monitoring solution for industrial machines that utilizes IoT sensors and data analytics to track performance metrics. This technology enables manufacturers to minimize downtime and maintenance costs by detecting potential failures before they occur.
Funding
$2.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
JFounders
Product
Problem
Manufacturers often face unexpected downtime and high maintenance costs due to the inability to accurately predict machine failures. Existing monitoring solutions lack the granularity needed to detect subtle signs of degradation, leading to reactive maintenance and inefficient resource allocation. This results in reduced productivity, increased expenses, and a shortened lifespan for critical machine components.
Solution
IPercept offers a predictive maintenance solution that uses AI-powered analytics and IoT sensors to monitor the health and performance of industrial machines, specifically CNC machines. By analyzing motion patterns and operational behavior, IPercept identifies early signs of wear, misalignment, and other degradation trends. This enables manufacturers to proactively address potential issues before they escalate into costly breakdowns. The system provides actionable insights, clear recommendations, and seamless integration with existing workflows, empowering maintenance teams to optimize resource allocation, reduce downtime, and extend the lifespan of critical machine parts.
Target Audience
The primary target audience includes maintenance and production teams in manufacturing facilities, particularly those utilizing CNC machines, who seek to improve machine reliability, optimize maintenance schedules, and reduce unplanned downtime.
Features
- Condition monitoring based on integrated motion pattern recognition for spindles, rotary axes, and linear axes
- Operation monitoring based on integrated behavioral profile analysis for utilization, load intensity, collision, and process monitoring
- AI-driven insights into wear, misalignment, and degradation trends, providing 1,000 times more accurate data than existing solutions
- Automated identification of collisions with classification to severity levels
- Process monitoring for automated identification of abnormalities and corresponding root causes
- Integration independent of machine brand, controller type, or IT infrastructure
- Predictive maintenance capabilities to map gradual and sudden machine degradation
- Web interface providing clear, ready-to-use insights for smarter planning