Prognosis Monitoring System, PMS 2026/09/29

Introductions

Traditional manufacturing often faces interruptions in operations due to equipment inspection and maintenance cycles. These gaps can result in issues or shutdowns, requiring expert intervention and impacting production capacity. Moreover, inefficient maintenance scheduling can lead to higher costs. AI-based prognosis monitoring systems, learning from expert analysis techniques, streamline complex sensor data to provide intuitive equipment status information. This system acts as a 24-hour monitoring AI engineer, accurately predicting machine conditions, diagnosing anomalies, and offering immediate feedback.

Technical Advantages and Features

• Customizable Equipment Health Monitoring: Monitor the health of equipment according to ISO vibration standards, such as air compressors and mechanical vibrations. Customize specific health indicators to accurately predict equipment lifespan.
• Automatic Fault Diagnosis for Rotary Machine: Capable of automatically diagnosing 17 common faults in rotary machines, including bearing and gear damage. The accuracy of fault diagnosis exceeds 85%. Proven cases have reduced maintenance operation time by up to 30%.
• Equipment Health Trend Forecasting & Optimal Maintenance Scheduling: Capable of predicting the deterioration of health status or even the Remaining Useful Life (RUL), with an error rate of less than 10%. Providing recommendations for optimized maintenance scheduling, with proven cases that have increased equipment lifespan by at least 50%.

Industrial Applications and Business Opportunities

• Industry Applications
  • Electronics, semiconductors, metals, petrochemicals, automation, and information services industries, among others, are all applicable as long as they can utilize vibration signal collection and AI analysis.
  • Intelligent Factory.
• Application Examples
  • This technology has been applied to over 500 pieces of machinery. Partnering with leading manufacturers, such as a domestic semiconductor vacuum equipment producer, has led to a 50% increase in equipment lifespans. Similarly, collaboration with a top domestic machining factory has improved tool lifespans by 10% through optimized drilling processes. In the green energy sector, we worked with a state-owned enterprise to diagnose gearbox anomalies in wind turbines a month in advance, effectively leveraging the benefits of predictive maintenance.