The startup offers a cloud-based prescriptive analytics platform for the oil and gas industry, utilizing deep learning and machine learning algorithms alongside proprietary physics-based performance models. This technology enables early fault detection and real-time performance analysis of complex turbomachinery, enhancing operational reliability and efficiency for clients.
Funding
$3M 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
The oil and gas industry faces challenges in maintaining the reliability and efficiency of complex turbomachinery, leading to unplanned downtime and increased operational costs. Traditional methods often fail to detect faults early enough to prevent significant equipment failures.
Solution
Mechademy offers Turbomechanica, a cloud-based prescriptive analytics platform designed for the oil and gas industry that addresses these challenges by providing early fault detection and real-time performance analysis of turbomachinery. The platform integrates deep learning and machine learning algorithms with proprietary physics-based performance models to empower plant personnel with predictive and prescriptive alerts. By identifying potential issues weeks or months before a failure occurs, Turbomechanica enables proactive maintenance, maximizes equipment uptime, extends asset life, and reduces operational risks. The platform's diagnostics engine provides actionable insights, allowing operators to plan maintenance proactively and reduce the risk of unplanned downtime and costly repairs.
Target Audience
The primary target audience includes operators and maintenance teams in the oil and gas industry, specifically in LNG, refining, upstream, midstream, offshore, and petrochemical sectors, who are responsible for ensuring the reliability and efficiency of rotating equipment.
Features
- Integration of deep learning, machine learning, and physics-based performance models
- Early fault detection and diagnosis, identifying potential issues weeks or months in advance
- Real-time performance analysis of complex turbomachinery
- OEM agnostic performance models
- Automated "Fouling Detection" capabilities for specialized applications
- Estimation of gas composition in centrifugal compressors for variable mole weight applications
- Multi-layered validation of diagnostics algorithms for high-confidence alerts
- Digital twins capable of detecting faults significantly before other solutions
- CMMS integration with service requests and work orders