Early Warning Detection for Pulverizer Abnormalities Unit 2 Suralaya PGU 1 × 400 MW with Noise Spectrum Analysis

Abstract

The coal pulverizer/mill abnormalities in coal power plants significantly affect the corporation’s reliability level. This paper introduces an early warning system for detecting pulverizer/mill abnormalities, specifically in PT PLN Indonesia Power's Suralaya PGU Unit 2 1x400 MW. Acoustic data from pulverizer 2E in Unit 2 of PT PLN Indonesia Power's Suralaya PGU were collected between September 2022 and September 2023. This dataset was acquired through recording devices and subsequently processed and visualized using MATLAB with the establishment of upper and lower threshold values based on recorded data during periods when the pulverizer is inoperative and when the pulverizer exhibits abnormal sound patterns. This paper reveals a correlation between increased sound abnormalities and the cumulative operational hours of the pulverizer. The results underscore that as the pulverizer operational hours accumulate, vibrations become more occurred. This paper introduces a novel approach to pulverizer/mill maintenance from the conventional strategy of interval-based maintenance every 3000 operating hours of the pulverizer, to a real- time graph-based strategy using data processed with MATLAB. This approach proposes a comprehensive enhancement of maintenance strategy within coal power plant pulverizers.