Predictive Maintenance

Reddy IoT

IIoT
Embedded Systems Developer
ML Engineer
AWS
C++
Visual Studio Code
CHALLENGE: The challenge lies in developing an effective predictive maintenance strategy for industrial equipment to mitigate unexpected downtime, reduce operational costs, and enhance safety by gaining real-time insights into machinery health and performance. HARDWARE USED: ❖ Sensors: ➝ Siemens SITRANS F M MAG 5100 W – (Flow Sensor)
➝ SITRANS TS500 (Temperature Sensors)
➝ SITRANS P DS III Series (Pressure Sensors)
❖ SIEMENS SIMATIC S7-1500 PLC
❖ MULTITECH CONDUIT 300
OUTCOMES: The predictive maintenance system model can make predictions upto 5 days in advance which enabled:
Early Maintenance of Boilers
Reduced the downtime by <22%
Improved life of boilers and associated components by 16%
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