Vibration Analytics & Predictive Maintenance Suite
Budget: ₹1,000,000 – ₹2,500,000 INR
I am commissioning a full-featured vibration analytics and predictive maintenance platform dedicated to rotating equipment. The end product must run smoothly on both Windows and Linux, interface seamlessly with industrial vibration sensors, and stream data in real time.
Core analytics
Real-time FFT and spectrum analysis, envelope detection, bearing defect diagnostics, and automatic alarm generation are mandatory. The system should reference a built-in bearing database covering the major bearing types and models we see in the field, so users can jump straight from a fault signature to a likely root cause.
Dashboards & visualisation
Inside the application, I need rich, configurable dashboards that present data as interactive graphs and charts, heatmaps, and concise tables. Historical asset views, comparative plots across multiple machines, and exportable reports must be included.
AI / ML layer
The platform has to deliver failure prediction, anomaly detection, remaining useful life estimation, plus clear alerts and root-cause insights driven by the model outputs. Models should learn continuously from new data without disrupting production.
Industrial readiness
• Hot-swap sensor support and edge-to-cloud scalability
• Role-based access and secure multi-plant deployment
• Minimal-latency processing so an operator never waits for results
Deliverable & acceptance
A production-ready installer (Windows & Linux) with all source code, a populated bearing database, deployment documentation, and a short video demoing live data acquisition through to an automated fault diagnosis. Bench tests on at least two compressor or pump datasets will serve as acceptance criteria.
Only teams or individuals with demonstrable success building similar vibration or condition-monitoring software should reply, backed by links, screen captures, or customer references to prove it.
Core analytics
Real-time FFT and spectrum analysis, envelope detection, bearing defect diagnostics, and automatic alarm generation are mandatory. The system should reference a built-in bearing database covering the major bearing types and models we see in the field, so users can jump straight from a fault signature to a likely root cause.
Dashboards & visualisation
Inside the application, I need rich, configurable dashboards that present data as interactive graphs and charts, heatmaps, and concise tables. Historical asset views, comparative plots across multiple machines, and exportable reports must be included.
AI / ML layer
The platform has to deliver failure prediction, anomaly detection, remaining useful life estimation, plus clear alerts and root-cause insights driven by the model outputs. Models should learn continuously from new data without disrupting production.
Industrial readiness
• Hot-swap sensor support and edge-to-cloud scalability
• Role-based access and secure multi-plant deployment
• Minimal-latency processing so an operator never waits for results
Deliverable & acceptance
A production-ready installer (Windows & Linux) with all source code, a populated bearing database, deployment documentation, and a short video demoing live data acquisition through to an automated fault diagnosis. Bench tests on at least two compressor or pump datasets will serve as acceptance criteria.
Only teams or individuals with demonstrable success building similar vibration or condition-monitoring software should reply, backed by links, screen captures, or customer references to prove it.