# Service: Predictive Maintenance Architecture

## metadata
- title: Predictive Maintenance Architecture Service | Klyff
- route: /services/predictive-maintenance-architecture
- timeline: 8-12 weeks implementation
- focus: Vibration, thermal, and acoustic equipment reliability

## Overview
End-to-end reliability engineering for plant operations teams: asset criticality audits, sensor retrofitting (piezoelectric accelerometers, thermal probes), edge gateway configuration, and SCADA dashboard integration.

## Performance Benchmarks
- **Deployment Timeline:** Reliability architecture commissioned within 8-12 weeks.
- **Unplanned Downtime:** 30-50% reduction in equipment outages on monitored assets.
- **Asset Longevity:** 20-30% increase in Mean Time Between Failures (MTBF).
- **Failure Warnings:** 48-72 hours early advance warning before critical breakdown.

## What We Do

### 1. Sensor Strategy & Optimal Placement
- Critical asset audits across rotating machinery (pumps, electric motors, bearings, gearboxes, industrial compressors).
- Recommendation of tri-axial accelerometers, temperature probes, and acoustic sensors positioned for maximum signal-to-noise ratio.
- Captures mechanical degradation signatures (imbalance, misalignment, looseness, bearing race wear) long before catastrophic damage occurs.

### 2. High-Frequency Feature Engineering
- Transforms high-rate raw sensor streams into actionable mechanical features.
- Computes RMS and peak vibration amplitudes, Fast Fourier Transform (FFT) spectral bands, envelope analysis, kurtosis, crest factors, and acoustic spectrograms.
- Condenses intensive telemetry into interpretable health indexes feeding directly into local anomaly detection algorithms.

### 3. Unsupervised Anomaly Detection Training
- Trains machine learning models (autoencoders, isolation forests, one-class SVMs) on baseline telemetry from healthy equipment operation.
- Flags deviations from learned baselines in real time, enabling reliable early detection without needing massive historical failure libraries.
- Practical for both new machine installations and rare mechanical failure modes.

### 4. Threshold Optimization & Multi-Level Alerting
- Calibrates warning limits against pilot production runs to eliminate false positives and alert fatigue.
- Configures tiered alert triggers: Warning (inspect at next shift), Critical (schedule repair within 48h), and Shutdown (immediate interlock).
- Escalation routing to maintenance technicians, PLC controls, and enterprise CMMS software.

### 5. Privacy-Preserving Zero-Cloud Design
- 100% of telemetry, signal transforms, and anomaly inference executes locally on plant edge hardware.
- Raw vibration and thermal data never exits plant firewalls.
- Meets strict enterprise cybersecurity standards, trade secret safeguards, and regulatory compliance (IEC 62304).

## Engagement Model
1. **Weeks 1-2:** Asset criticality assessment and sensor placement guidance.
2. **Weeks 3-6:** Edge signal acquisition and anomaly baseline model training.
3. **Weeks 7-9:** Gateway deployment and CMMS/SCADA alerting integration.
4. **Weeks 10-12:** Threshold tuning and 3 months operational reliability support.