Nanoprecise Sci Corp > Case Studies > Detecting Cavitation And High Vane Pass Frequency For Pumps

Detecting Cavitation And High Vane Pass Frequency For Pumps

Technology Category

Analytics & Modeling - Edge Analytics
Analytics & Modeling - Predictive Analytics
Functional Applications - Remote Monitoring & Control Systems
Sensors - Pressure Sensors
Sensors - Vibration Sensors

Applicable Industries

Utilities

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance

The Customer

Nabha Power Plant

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About The Customer

Nabha Power Plant is a supercritical 700 MW thermal power plant located near Rajpura Punjab, India. For an industrial plant of this magnitude, unplanned maintenance shutdowns have a major impact on productivity and profitability.

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The Challenge

The Condensate Cooling Water (CCW) pump, one of the critical pumps in maintaining steadystate operations, is a horizontal vane pump operating at up to 1650 m3/hr with a discharge pressure of 9 MPa (62 psi) at 986 rpm. Each day this pump is offline costs the plant $250,000 in lost revenue and each failure costs tens of thousands of dollars to execute an unplanned repair. Thus, Larsen & Toubro (L&T) really needed a predictive maintenance solution to detect faults at an early stage and provide a reliable prediction of Remaining Useful Life (RUL)

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The Solution

We proposed our RotationLF system under which we installed around 24 wireless sensors as a part of a pilot project on air compressors, ACW and CCW Pumps, and Fans.

The specific placement of the VibrationLF sensors are selected to monitor:

1) Non-drive side bearing, electric motor

2) Drive side bearing, electric motor

3) Drive side Bearing, pump

4) Non-drive side bearing, pump

Once installed, strong battery-powered wireless sensors started monitoring pump and motors and sending data to our SaaS-based platform through an encrypted & secured network using Edge and Cloud computing. As data was received, RotationLF platform worked on data analysis using highly sophisticated algorithms.

Approximately six weeks after the sensors were installed, the AI alerted L&T that a vane fault had been detected on the pump, causing cavitation. The fault frequency depicted in the system is indicative of an early-stage failure.

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Operational Impact

[Data Management - Data Availability]
The RotationLF analytics sensed & detected the anomaly in the pattern and alerted L&T plant staff about this unusual trend automatically through mobile text and email alert.
[Process Optimization - Predictive Maintenance]
The RUL prediction of 25 days to failure provided sufficient time to schedule the pump replacement during an already planned maintenance outage.

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