Detecting Classifier-Coal Mill Damage Using a Signal Vibration Analysis

Abstract

A classifier plays a crucial role in the cement industry. It is in charge of separating coal that has been smoothened out and is ready to be burned although the coal is still rough after going through the grinding process. It takes a long time to burn coal that is not perfectly processed with a classifier. Therefore, it will reduce the amount of cement production, and the factories will release more energy. The closed arrangement and the number of components in the unit classifier requires a sophisticated method to detect damage that occurs early. Vibration analysis is a method that has been effectively employed in detecting the initial damage that occurs to the engine, especially the classifier. This study was aimed at detecting the location of the damage occurring in the classifier by using a vibration signal analysis and by measuring the magnitude of vibration and presenting it to the frequency domain (spectrum) form using Fast Fourier Transform. Engine condition assessment referred to ISO 10816-3 standard in velocity and displacement modes. Based on data spectrum analysis, the dominant damage laid in the unbalanced rotor. Spectrum characteristics of the damage appeared to be in the spectrum line worth 438.01 μm at a 3.5-Hz frequency (1X) radially. This analysis proved to be supported by the decrease in vibration value to 18.65 μm after balancing the Classifier rotor.

Intelligent system design for identification of unbalance and misalignment using Fuzzy Logic methods

“…And, if the amplitude is 0.5X the frequency, the diagnosis is unknown. This membership function indicates the machine condition based on the standard ISO 10816-3 [35]; if 0 ≤ amplitude (RMS) ≤ 2.3, then the diagnosis is the best machine condition; if 2.3 < amplitude ≤ 4.5, then the diagnosis is a good machine condition, if 4.5 < amplitude ≤ 7.1, then the diagnosis is a bad machine state, and if the amplitude > 7.5, then the diagnosis is the worst machine state, then the author makes four membership functions as shown in Figure 8. This variable indicates the phase difference on the same axis that will later be used to determine the unbalance on the demo unit, namely static unbalance, or dynamic unbalance, with a membership function of 0 ≤ 1-plane ≤ 30° it indicates static unbalance, while at 2-plane > 30° it means dynamic unbalance, as shown in Figure 9.…”

“…The output results comprise various categories of machine damage, which are identified through the application of Fuzzy logic rules that are based on the principles of vibration analysis and draw upon information from relevant literature sources [8, 35,36,37,38]. Specifically, the analysis focuses on the effects of unbalance and misalignment.…”

Perancangan Sistem Diagnosis Getaran Motor Menggunakan Jaringan Saraf Tiruan Propagasi Mundur

“…Pada sebuah data spektrum yang didapat dari proses pengukuran motor DC 12 kW dilakukan sebuah inisialisasi untuk mendapatkan jenis karakteristik sebuah kerusakan. Pada karakteristik sebuah kerusakan, karakteristik yang diambil mengacu pada standar yang ada yakni menggunakan standar ISO [2]. Pada data spektrum tersebut terdapat nilai Root Mean Square (RMS) yang nantinya akan dimasukkan ke dalam sistem diagnosis.…”

The Implementation of Artificial Neural Networks in Designing Intelligent Diagnosis Systems for Centrifugal Machines Using Vibration Signal

“…It is, however, very often possible for the object being diagnosed to be the source of diagnostic signals. For example, it possible to use the characteristics of vibration signals to assess the technical conditions of a centrifugal machine [15,16,17].…”