Predictive Maintenance System

Overview

A predictive maintenance platform for manufacturing, distribution, and process industries with a meaningful base of monitored equipment. The system connects to existing sensor data (vibration, temperature, acoustic, current draw, hydraulic pressure — anything you're already collecting in your historian or PLC) and learns the normal operating envelope of every asset. It detects anomalies before they cascade into failures, classifies the likely failure mode, estimates time-to-failure with calibrated confidence, and writes a scheduled work order into your CMMS with the recommended fix and parts. Operations and maintenance leadership see a live asset-health dashboard with the actual dollar value of prevented failures.

The Challenge

Predictive maintenance is real engineering, not buzzword consulting. Every asset is different. Sensor data is messy, often misaligned in time, and full of false positives if you just threshold on raw values. Failure modes are equipment-specific — a bearing wear signature looks nothing like a misalignment signature. The model has to be specific enough to predict the failure mode (so the maintenance team knows what parts to order), not just 'something is wrong.' And the output has to land in the maintenance team's actual workflow — the CMMS — not in a separate dashboard they'll stop checking after two weeks.

Our Approach

We start with a feasibility audit: which assets, which sensors, what historical data exists. We build per-asset baseline models (typically gradient-boosted or autoencoder-based depending on the data) calibrated against your historical failure events. Anomaly detection runs continuously; classified anomalies become predicted failure modes with confidence scores and time-to-failure ranges. CMMS integration (Maximo, SAP PM, eMaint, Fiix, custom) generates scheduled work orders with parts pre-populated. Dashboards show live asset health, prevented-failure dollar value, and forecast. The whole system runs at the edge or in your cloud — your choice.

Key Features

• Per-asset baseline modeling on existing sensor data
• Failure-mode classification (bearing, alignment, lubrication, fatigue)
• Calibrated time-to-failure prediction with confidence ranges
• CMMS integration — scheduled work orders with parts pre-listed
• Live asset-health dashboard for operations and maintenance leadership
• Prevented-failure dollar value tracking
• Edge or cloud deployment, your choice
• Continuous tuning from confirmed failure outcomes

Results

Try It Yourself