Multivariable Trend Analysis Using Neural Networks for Intelligent Diagnostics of Rotating Machinery

1997 ◽  
Vol 119 (2) ◽  
pp. 378-384 ◽  
Author(s):  
S. Zhang ◽  
R. Ganesan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Trend Analysis ◽  
Condition Monitoring ◽  
Diagnostic Procedure ◽  
On Line ◽  
Neural Network Algorithm ◽  
Intelligent Diagnostics ◽  
Bearing System ◽  
Self Organizing

The objective of this paper is the development of an efficient intelligent diagnostic procedure that considers several diagnostic indices for the quantification of developing faults and for monitoring machine condition. In this procedure, the condition monitoring is performed based on the on-line vibration measurements, and further, the fault quantification is formulated into a multivariate trend analysis. Self-organizing neural networks are then deployed to perform the multivariable trending of the fault development. The attributes for the disordering of “knots” in the trend analysis are determined. The disordering of neural network units is then eliminated by suitably altering the self-organizing neural network algorithm. Applications of this diagnostic procedure to the condition monitoring and life estimation of a bearing system are fully developed and demonstrated. The efficiency and advantages of the intelligent diagnostic procedure in precisely monitoring and quantifying the fault development are systematically brought out considering this bearing system.

A New Approach to Machinery Monitoring and Diagnostics Using Self-Organizing Maps

1995 ◽  
Author(s):  
Siyu Zhang ◽  
R. Ganesan ◽  
T. S. Sankar
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Trend Analysis ◽  
New Approach ◽  
Machine System ◽  
Network Algorithm ◽  
On Line ◽  
Neural Network Algorithm ◽  
Monitoring And Diagnostics ◽  
Self Organizing

Abstract The problem of estimating an unknown multivariate function from on-line vibration measurements, for determining the conditions of a machine system and for estimating its service life is considered. This problem is formulated into a multiple-index based trend analysis problem and the corresponding indices for trend analysis are extracted from the on-line vibration data. Selection of these indices is based on the simultaneous consideration of commonly-observed faults or malfunctions in the machine system being monitored. A neural network algorithm that has been developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of this neural network algorithm to the condition monitoring and life estimation of both a bearing system as well as a gearbox are fully demonstrated. The efficiency and computational supremacy of the new algorithm are established through comparing with the performance of Self-Organizing Mapping (SOM) and Constrained Topological Mapping (CTM) algorithms. Further, the usefulness of multiple-index based trend analysis in precisely predicting the condition and service life of a machine system is clearly demonstrated. Using on-line vibration signal to constitute the set of variables for trend analysis, and employing the newly-developed self-organizing neural algorithm for performing the trend analysis, a new approach is developed for machinery monitoring and diagnostics.

Multivariable Trend Analysis for System Monitoring Through Self-Organizing Neural Networks

1997 ◽  
Vol 119 (2) ◽  
pp. 223-228
Author(s):  
Siyu Zhang ◽  
R. Ganesan
Keyword(s):  
Trend Analysis ◽  
Diagnostic Procedure ◽  
Vibration Signal ◽  
Machine Condition Monitoring ◽  
Diagnostic Indices ◽  
Condition Monitoring Systems ◽  
On Line ◽  
Neural Network Algorithm ◽  
Diagnostic Index ◽  
Self Organizing

For precise and reliable fault detection it is essential to consider simultaneously the changes in several diagnostic indices that are extracted from the on-line vibration signal. Existing machine condition monitoring systems consider each diagnostic index separately. Development of an automated diagnostic procedure that considers simultaneously several diagnostic indices is the objective of the present paper. The multivariable trend analysis of on-line vibration signals is deployed as the basis for this procedure. An efficient self-organizing neural network algorithm that is highly suitable to this diagnostic procedure is developed and deployed. Applications to both a bearing system as well as a gearbox system are fully developed and demonstrated.

Self-Organizing Neural Networks for Automated Machinery Monitoring Systems

1995 ◽  
Author(s):  
Siyu Zhang ◽  
R. Ganesan ◽  
T. S. Sankar
Keyword(s):  
Neural Network ◽  
Trend Analysis ◽  
Real Life ◽  
Vibration Signal ◽  
Statistical Parameters ◽  
Data Set ◽  
Machine System ◽  
On Line ◽  
Machinery System ◽  
Self Organizing

Abstract Two fundamental problems that are frequently encountered in automated machinery monitoring and diagnostics are formulated into their corresponding mathematical problems of clustering and trend analysis. The need for and the efficiency of multiple-index based trend analysis, in both precisely evaluating the current conditions of a machine system using on-line vibration measurements and obtaining a reliable prediction about its future behaviour, are systematically brought out. Neural network solutions to these problems, particularly the solutions using Self-Organizing Maps (SOM) are sought. Statistical parameters of the on-line vibration signal such as peak-to-peak value, absolute mean value, crest factor etc., are used to form the data set depending on the machinery system being monitored and diagnosed. Self-organizing mapping algorithm is then employed to perform the clustering and feature extraction which takes as the input the multi-dimensional data set and provides as the output the condition of the machinery system. Associated one-layer neural network is developed during the process of SOM and the training of this network is performed in an unsupervised learning mode. A new efficient neural network algorithm that has been previously developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of the above neural network algorithms to the condition monitoring and life estimation of both a bearing system as well as a rotor system are fully demonstrated using real-life data.

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks

2011 ◽  
Vol 90-93 ◽  
pp. 2173-2177
Author(s):  
Chen Cai ◽  
Tao Huang ◽  
Xun Li ◽  
Yun Zhen Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Water Inflow ◽  
Water Volume ◽  
Network Algorithm ◽  
Forecast Precision ◽  
Neural Network Algorithm ◽  
Computation Analysis

The submarine tunnel water-inflow question has many kinds of factor synthesis influences, has highly the complexity and the misalignment, This article used the BP neural network algorithm to establish the submarine tunnel welling up water volume forecast model and to carry on the computation analysis, The result indicated that this model restraining performance is good, the forecast precision is high and simple feasible. This method has provided a new mentality for the submarine tunnel welling up water volume's forecast.

The Character Recognition of Vehicle's License Plate Based on BP Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 3805-3808 ◽  
Author(s):  
Wen Bo Liu ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Character Recognition ◽  
License Plate ◽  
Image Preprocessing ◽  
Matrix Operations ◽  
Neural Network Algorithm ◽  
Character Segment

This paper based on license plate image preprocessing ,license plate localization, and character segment ,using BP neural network algorithm to identify the license plate characters. Through k-l algorithm of characters on the feature extraction and recognition of license plate character respectively then taking the extraction of license plate character features into the character classifier to the training. When the end of training, extracting the net-work weights and offset matrix, and storing in the computer. To take the identified character images input to the MATLAB, and with the preservation weights and offset matrix operations, obtain the final results of recognition.

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