Solution

Predictive Maintenance

Fix machines before they fail, not after.

The Problem

Most plants still run reactive or calendar-based maintenance. The result is unplanned breakdowns, costly overtime, premature part replacements and OEE that plateaus below world-class.

Unplanned downtime costs manufacturers from roughly $10,000 to over $250,000 per hour depending on the sector
Reactive repairs cost 3–5x more than planned interventions
Calendar-based PM replaces healthy parts and still misses real failures
Skilled maintenance teams burn out firefighting instead of optimising

The KLVIN Approach

KLVIN deploys wireless edge sensors (S.A.M, THRIVE, I.S.M) on critical rotating assets. Vibration, temperature, current and process data stream to SENTINEL, where AI models compute Remaining Useful Life (RUL) and flag anomalies 7–21 days before failure. Electrical motors are our core focus — covered by India Patent 566486 for full-stack AI that prevents motor breakdowns.

Electrical motors are the failure point behind most rotating-asset downtime. KLVIN's patented approach to them is detailed on the electrical motor failure prediction page — motor current signature analysis, ISO 10816 vibration severity and winding-insulation modelling on a single sensor.

Expected Outcomes

30–50%

Reduction in unplanned downtime

20–40%

Lower maintenance cost

10–25%

OEE improvement

< 6 months

Typical payback period

What You Get

RUL Prediction

AI models estimate days-to-failure for bearings, windings, seals and rotors with confidence intervals.

Anomaly Detection

Edge models flag drift, imbalance, cavitation and misalignment as it emerges.

Auto Work-Order Trigger

Severity-tagged alerts feed CMMS / ERP (SAP, Maximo) to create PMs automatically.

Asset Health Scoring

Single 0–100 health index per asset, rolled up to line, plant and enterprise dashboards.

Frequently Asked Questions

How is predictive maintenance different from preventive maintenance?

Preventive maintenance is time-based — you service at fixed intervals whether the machine needs it or not. Predictive maintenance is condition-based — sensors and AI tell you the actual health of the asset, so you intervene only when the data says you must.

How long does it take to deploy KLVIN's predictive maintenance solution?

A typical pilot on 10–20 critical assets goes live in 2–4 weeks. Sensors mount magnetically without machine shutdown, and the SENTINEL platform is cloud-hosted with no on-prem install required.

What ROI can we expect?

Most KLVIN customers see payback in under 6 months — driven by 30–50% reduction in unplanned downtime, 20–40% lower maintenance cost and 10–25% OEE improvement.