Intelligent diagnosis methods for plant machinery

Abstract

This paper reports several intelligent diagnostic approaches based on artificial neural network and fuzzy algorithm for plant machinery, such as the diagnosis method using the wavelet transform, rough sets, and fuzzy neural network; the diagnosis method based on the sequential inference and fuzzy neural network; the diagnosis approach by the possibility theory and certainty factor model; and the diagnosis method on the basis of the adaptive filtering technique and fuzzy neural network. These intelligent diagnostic methods have been successfully applied to condition diagnosis in different types of practical plant machinery.

A vibration cavitation sensitivity parameter based on spectral and statistical methods

“…Reasonable accuracy in cavitation detection was obtained by Wang et al who utilised wavelet analysis, rough sets, and partially linearized neural networks (PNN). The system was able to correctly detect cavitation with an 85.1% accuracy, (H. Wang, 2010;Huaqing Wang & Chen, 2007). Overall, the use of neural networks to detect cavitation has resulted in mixed results.…”

Intelligent Diagnosis Method for Centrifugal Pump System Using Vibration Signal and Support Vector Machine

“…Moreover, the unknown state except state and state is also model state UN, its possibility function ( UN ( )) is calculated by the following formula (10). And the common area between UN ( ) and ( ) is calculated by the following formula (11). Consider…”

“…However, it is gradually being understood that vibration signature is the most revealing information reflecting the condition of rotating machinery [5][6][7]. Vibration signals were employed for fault detection and condition monitoring in [8][9][10][11][12][13][14][15][16][17][18][19][20][21], so it is important that fault signal should be sensitively extracted from the measured signal when fault occurs. However, it is difficult since fault signal is so weak that it is often buried in strong noise, especially at an early stage.…”

“…However, it is difficult since fault signal is so weak that it is often buried in strong noise, especially at an early stage. Studies in [8][9][10][11][12][13][14] presented that wavelet analysis could detect effectively condition change. However, different wavelet basis functions were adopted.…”

Analysis and Simulation of BIG-PLUS Tooling System

“…Tool Holder as to connect high-speed machine tool spindle and cutting tools, power transmission torque of the important structures, high-speed processing is the most common type of tool holder. Good dynamic performance of the tooling system in the high-speed cutting conditions to ensure the stability of the cutting, the cutting of high precision and an important indicator of processing efficiency [1][2][3][4].…”