The startup develops a machine diagnostics and monitoring system that integrates hardware and software to remotely track the performance of industrial machinery by analyzing electric current and voltage. This technology enables original equipment manufacturers to detect malfunctions in real-time, enhancing operational efficiency and reducing downtime.
Funding
$7.5M 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.
Founders
Product
Problem
Industrial facilities face challenges in maintaining the performance and reliability of rotating electrical assets, leading to downtime and increased operational costs. Traditional monitoring methods often fail to provide real-time insights into asset condition and predict potential failures before they occur. This results in inefficient maintenance scheduling, increased energy consumption, and reduced overall equipment effectiveness.
Solution
Elmodis offers an Industrial Internet of Things (IIoT) solution that combines hardware and software to provide comprehensive monitoring and diagnostics for electric-powered industrial machines. Smart edge devices collect electrical signals and process data using machine learning algorithms, generating key performance indicators (KPIs) that are transmitted to a secure cloud platform. This enables real-time monitoring of asset performance, early detection of anomalies, and prediction of potential failures. The Elmodis system provides users with actionable insights through a web portal, SMS alerts, and API integrations, allowing for optimized maintenance, reduced energy consumption, and increased asset uptime.
Target Audience
The primary target audience includes utilities, manufacturing plants, original equipment manufacturers (OEMs), energy companies, and electric transportation providers seeking to optimize the performance and reliability of their electric-powered industrial assets.
Features
- Compact edge devices for real-time data acquisition from electrical signals and process parameters
- Patented machine-learning algorithms for predictive maintenance and anomaly detection
- Secure cloud platform for data storage, analytics, and reporting
- Integration of vibration sensors and process parameters (temperature, pressure, flow rate)
- Customizable alerts and notifications via SMS, email, and web interface
- REST API for integration with existing data management systems
- Support for various communication protocols, including cellular, Wi-Fi, and Ethernet
- Scalable architecture for easy deployment across multiple assets and locations