High Pressure Air Compressor Condition Monitoring Using Unsupervised Pattern Classification Based on Projection Networks

2005 ◽  
Author(s):  
C. James Li ◽  
C. Jansuwan
Keyword(s):  
High Pressure ◽  
Supervised Classification ◽  
Clustering Algorithm ◽  
Unsupervised Classification ◽  
Single Class ◽  
Air Compressor ◽  
Classifier System ◽  
Discharge Valve ◽  
Air Compressors ◽  
Projection Network

High pressure air compressors (HPAC) are a high maintenance machine for they break down more often than expected and they serve critical roles. This study established the utility of an unsupervised pattern classifier system integrating a clustering algorithm based on DBSCAN and a dynamic classifier based on projection network to classify the condition of a 4-stage high pressure air compressor. The clustering algorithm is used to form clusters from un-labeled data and eliminate outliers. Subsequently, a system of projection networks is established to recognize all the significant clusters. The compressor data is consisted of pressures and temperatures at all four stages taken under various conditions including different baseline conditions, 3rd stage suction valve fault, 3rd stage discharge valve fault, and cylinder pitting and corrosion. The clustering algorithm was able to form clusters that each individually contains data mostly from a single class, and the projection network was able to differentiate these clusters and therefore classify the condition of the compressor correctly about 94% of the time. The ability of unsupervised classification does come with a price of lower classification accuracy. It was about 5% lower than what was accomplished by supervised classification.

Projection Network for Unsupervised Pattern Classification

2005 ◽  
Author(s):  
C. James Li ◽  
C. Jansuwan
Keyword(s):  
Clustering Algorithm ◽  
Unsupervised Classification ◽  
Least Square ◽  
Data Sets ◽  
Numerical Instability ◽  
Benchmark Data ◽  
Supervised Classifiers ◽  
The Difference ◽  
Projection Network ◽  
Iris Data

Projection network, being a non-linear dynamic system itself, has been shown to be superior to static classifiers such as neural networks in some applications where noise is significant. However it is a supervised classifier by nature. To extend its utility for unsupervised classification, this study proposes an unsupervised pattern classifier integrating a clustering algorithm based on DBSCAN and a dynamic classifier based on the projection network. The former is used to form clusters out of un-labeled data and eliminate outliers. Then, significant clusters in terms of size are identified. Subsequently, a system of projection networks is established to recognize all the significant clusters. The unsupervised classifier is tested with three well-known benchmark data sets (by ignoring data labels during training) including the Fisher’s iris data, the heart disease data and the credit screening data and the results are compared to those of supervised classifiers based on the projection network. The difference in performance is small. However, the ability of unsupervised classification comes at a price of a more complex classifier system and the need of data pre-conditioning. The former is because more than one cluster could be formed for a class and therefore more computational units are needed for the classifier, and the latter is because increased similarity of data after clustering increases the chances of numerical instability in the least square algorithm used to initialize the classifier.

Parameter Optimization of Compressor Based on an Ant Colony Optimization

2012 ◽  
Vol 201-202 ◽  
pp. 916-919
Author(s):  
Mei Peng Zhong
Keyword(s):  
High Pressure ◽  
Ant Colony Optimization ◽  
Low Pressure ◽  
Ant Colony ◽  
High Pressure Cylinder ◽  
Air Compressor ◽  
Pressure Cylinder ◽  
Set Up ◽  
Air Compressors ◽  
Particle Approach

A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pressure piston, the journey of high pressure piston and the rotate speed of BLDCM. Simulated result shows that the efficiency of BLDCM is more than that before optimizating. The test is done. The result shows that the specifc Power of air compressor is much less than before optimizating on 2.5Mpa. The result also shows that an Ant Colony Optimization which optimizating the air compressor is availability and practicality.

Utility of Projection Network for Diagnosis of High Pressure Air Compressor Faults

Manufacturing ◽  
2003 ◽  
Author(s):  
C. Jansuwan ◽  
C. James Li
Keyword(s):  
High Pressure ◽  
Training Data ◽  
Training Methods ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Air Compressor ◽  
Testing Data ◽  
And Training ◽  
Projection Network

The utility of a dynamic neural network, i.e., projection network, was established to diagnose the condition of a 4-stage high pressure air compressor. Network structure and parameter initialization and training methods were developed. Using measurements of the compressor’s four stages’ discharge temperatures and pressures collected under different baseline conditons, 3rd stage suction and exhaust value faults, and an unanticipated 3rd stage cylinder pittings as training data, a 99+% of correct classification rate was accomplished with testing data.

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

scholarly journals A Spectral-Based Approach for BCG Signal Content Classification

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1020
Author(s):  
Mohamed Chiheb Ben Nasr ◽  
Sofia Ben Jebara ◽  
Samuel Otis ◽  
Bessam Abdulrazak ◽  
Neila Mezghani
Keyword(s):  
Human Body ◽  
Supervised Classification ◽  
Unsupervised Classification ◽  
Gaussian Mixture ◽  
K Nearest Neighbors ◽  
Content Classification ◽  
Extraction Step ◽  
Vital Sign Measurement ◽  
Definition Of ◽  
Spectral Flatness Measure

This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals—in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step. Unsupervised classification is used to explore the content of the BCG signals, justifying the existence of different classes and permitting the definition of useful hyper-parameters for effective segmentation. In contrast, the considered supervised classification approach aims to determine if the BCG signal content allows the measurement of the heart rate (HR) and the respiratory rate (RR) or not. Furthermore, two levels of supervised classification are used to classify human-body activities into many realistic classes from the BCG signal (e.g., coughing, holding breath, air expiration, movement, et al.). The first one considers frame-by-frame classification, while the second one, aiming to boost the segmentation performance, transforms the frame-by-frame SFM and SC features into temporal series which track the temporal variation of the measures of the BCG signal. The proposed approach constitutes a novelty in this field and represents a powerful method to segment BCG signals according to human body activities, resulting in an accuracy of 94.6%.

Detection of Yellow Hawkweed (Hieracium pratense) with High Resolution Multispectral Digital Imagery

1995 ◽  
Vol 9 (3) ◽  
pp. 477-483 ◽  
Author(s):  
Hubert W. Carson ◽  
Lawrence W. Lass ◽  
Robert H. Callihan
Keyword(s):  
Supervised Classification ◽  
Digital Images ◽  
Ground Cover ◽  
Unsupervised Classification ◽  
Digital Imagery ◽  
Charge Coupled Device ◽  
Spectral Filters ◽  
A Charge ◽  
Northern Idaho

Yellow hawkweed infests permanent upland pastures and forest meadows in northern Idaho. Conventional surveys to determine infestations of this weed are not practical. A charge coupled device with spectral filters mounted in an airplane was used to obtain digital images (1 m resolution) of flowering yellow hawkweed. Supervised classification of the digital images predicted more area infested by yellow hawkweed than did unsupervised classification. Where yellow hawkweed was the dominant ground cover species, infestations were detectable with high accuracy from digital images. Moderate yellow hawkweed infestation detection was unreliable, and areas having less than 20% yellow hawkweed cover were not detected.

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