Adopted by Major Semiconductor Fab: Prognosis Deploys Over 907 Sensors across 223 Machine Assets, Validating Large-Scale Diagnostic Capabilities
As AI and smart manufacturing deepen, the central question for industrial leaders has evolved from "are machines connected?" to "can we act before equipment fails?" At the 2026 Taipei Int'l Industrial Automation Exhibition, Prognosis Technology will showcase its proprietary high-precision triaxial MEMS vibration & temperature sensors alongside the PILOT AI Fault Prevention & Diagnostic System. By continuously collecting 24/7 operational telemetry, the system extracts critical feature vectors from time-domain waveforms and frequency spectra to construct dynamic machine health profiles.
A Three-Tiered Asset Health Management Architecture
Rather than simply alerting operators when threshold limits are breached, Prognosis delivers a structured, three-tiered asset health management framework:
1. Real-Time Health Scoring: Converts complex vibration metrics into quantifiable health tiers (Normal, Observe, Warning, High Risk), allowing field teams to assess machine status instantly without parsing raw waveforms manually.
2. Degradation Trends & RUL Analysis: Tracks health progression over time to perform Remaining Useful Life (RUL) estimation—empowering plants to optimize maintenance windows, labor allocation, and spare parts inventory ahead of time.
3. Precision Fault Localization: Covers 6 critical component categories—spindles, bearings, motors, gears, belts, and impellers—with 21 diagnostic models designed to pinpoint specific root causes such as unbalance, misalignment, mechanical looseness, and bearing or gear defects.
"The key differentiator of our platform lies in combining mechanical domain physics, vibration expertise, and AI algorithms with edge computing," notes Leo Hsu, Technical Director at Prognosis Technology. "By executing feature extraction on local edge IPCs, we reduce cloud processing loads by approximately 80%—alleviating network bandwidth and cybersecurity strains while cutting manual diagnostic interpretation time by 50%."
This technology has advanced beyond Proof-of-Concept (PoC) into full-scale industrial deployment. Prognosis has deployed over 907 sensors monitoring 223 critical assets—including air compressors and exhaust fans—at a leading international semiconductor manufacturing facility. Delivering 24/7 telemetry with a data completeness rate exceeding 99.9%, the project validates the system's capacity to scale across multi-facility operations.
Looking forward, Prognosis is integrating Digital Twin technology and an Industrial AI Copilot, enabling maintenance personnel to query machine health and historical logs using natural language.
The company will continue expanding its footprint across high-tech fab applications—focusing on vacuum pumps, dry pumps, scrubbers, exhaust treatment systems, facility fans, and micro-vibration monitoring for high-precision tools. Through software licensing, white-label partnerships, and technology authorizations with OEMs and System Integrators, Prognosis aims to embed PHM intelligence directly into machinery—giving every critical asset its own digital health record and driving a proactive predictive maintenance ecosystem.
Sources：https://www.ctee.com.tw/news/20260811700643-431203