Investigations on generalized Hjorth's parameters for machine performance degradation assessment

2022 ◽  
Vol 168 ◽  
pp. 108720
Author(s):  
Tongtong Yan ◽  
Dong Wang ◽  
Tangbin Xia ◽  
Zhike Peng ◽  
Lifeng Xi
Keyword(s):  
Performance Degradation ◽  
Machine Performance

Machine Performance Degradation Recognition Using Locality Preserving Projections and Clustering Approach

2012 ◽  
Vol 443-444 ◽  
pp. 929-934
Author(s):  
Jian Bo Yu ◽  
Jian Ping Liu ◽  
Mei Fang Liu ◽  
Ji Ting Yin ◽  
Yong Guo Wang
Keyword(s):  
Principal Component ◽  
Performance Degradation ◽  
Experimental Result ◽  
Test Bed ◽  
Machine Performance ◽  
Locality Preserving ◽  
Systematic Feature ◽  
Automatic Guidance ◽  
Clustering Approach ◽  
Low Dimensional

The sensitivity of various features that are characteristics of machine performance may vary significantly under different working conditions. Thus it is critical to devise a systematic feature extraction (FE) approach that provides a useful and automatic guidance on using the most effective features for machine performance recognition without human intervention. This paper proposes a locality preserving projection (LPP)-based FE approach for machine performance degradation recognition. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP is capable to discover local structure of the data manifold. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. This experimental result on a bearing test-bed shows that LPP-based FE improves the performance of recognizers for identifying performance degradation of bearings.

Time-Frequency Based Sensor Fusion in the Assessment and Monitoring of Machine Performance Degradation

2002 ◽  
Author(s):  
Dragan Djurdjanovic ◽  
Jun Ni ◽  
Jay Lee
Keyword(s):  
Signal Analysis ◽  
Performance Degradation ◽  
Frequency Signal ◽  
Time Frequency ◽  
Multiple Sensors ◽  
Machine Performance ◽  
Signal Features ◽  
Cnc Lathe ◽  
Lathe Machine ◽  
Assessment And Monitoring

Machines degrade as a result of aging and wear, which decreases their performance reliability and increases the potential for faults and failures. In contemporary manufacturing it becomes increasingly important to predict and prevent machine failures, rather than allowing the machine to fail and then fixing the failure. In this paper, methods of time-frequency signal analysis will be used to capture information from multiple machine sensors. This information could be used to assess machine performance degradation and subsequently take appropriate action. Signals emanating from three different sensors were collected when a sharp and a worn tool have been mounted on a CNC lathe machine. Several combinations of sensors and signal features have been tried in order to demonstrate the ability to use the information from multiple sensors and increase sensitivity to tool wear.

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