RSTER provides an integrated reliability‑engineering platform that combines model‑based systems engineering with data‑driven failure prediction for e‑mobility and renewable energy assets. Its cloud‑hosted digital twin ingests real‑time sensor data, runs machine‑learning anomaly detection and automated fault‑tree analysis, and delivers predictive‑maintenance alerts and compliance reporting via dashboards and APIs.
Funding
Funding not disclosed
Founders
Product
Problem
Complex engineering systems in e‑mobility and renewable energy face high failure rates, safety incidents, and costly downtime due to insufficient reliability analysis and limited predictive insight. Traditional safety assessments often lack integration with modern digital tools, making it difficult to anticipate failures across the system lifecycle. This gap hampers operators’ ability to maintain performance, meet regulatory standards, and protect assets.
Solution
RSTER delivers an integrated reliability‑engineering service suite that combines model‑based systems engineering (MBSE) with data‑driven failure prediction and root‑cause analysis. The approach embeds safety and reliability requirements directly into system models, enabling traceable risk assessments and compliance mapping (e.g., ISO 26262, IEC 61508). RSTER’s aftermarket software platform ingests real‑time sensor streams, applies machine‑learning anomaly detection, and generates predictive‑maintenance recommendations through a cloud‑hosted digital‑twin environment. Clients receive actionable dashboards, API‑based data export, and a structured mitigation plan that reduces unplanned outages and extends asset life. The solution is packaged for both project‑based engineering engagements and ongoing SaaS subscriptions, allowing seamless scaling from prototype to fleet‑wide deployments.
Target Audience
Primary customers are OEMs and system integrators in electric‑vehicle and battery pack manufacturing, as well as wind‑turbine and solar‑farm operators seeking to improve asset reliability and regulatory compliance. Engineering consultancies and asset‑management firms that require advanced safety analysis and predictive‑maintenance tools also constitute a core market segment.
Features
- MBSE framework that integrates reliability and safety attributes into SysML models for end‑to‑end traceability
- Statistical and machine‑learning failure‑prediction engine leveraging historical failure data and real‑time sensor inputs
- Automated fault‑tree analysis and FMEA workflows for rapid root‑cause identification
- Risk‑mitigation planning tools aligned with industry standards (ISO 26262, IEC 61508, IEC 62443) and compliance reporting
- Cloud‑based digital‑twin platform for wind turbines and other renewable assets, supporting scenario simulation and performance optimization
- Predictive‑maintenance SaaS suite with real‑time monitoring, anomaly detection, and prescriptive alerts via customizable dashboards
- REST and OPC‑UA APIs for seamless integration with OEM BMS, SCADA, and enterprise asset‑management systems
- Aftermarket diagnostics module offering KPI tracking, lifecycle cost analysis, and remote firmware update capabilities