Vibration feature extraction via graph-modeled SVD for running stability assessment of industrial machine

2019 ◽  
Author(s):  
Xin Wen ◽  
Guoliang Lu ◽  
Peng Yan
Keyword(s):  
Feature Extraction ◽  
Stability Assessment ◽  
Industrial Machine

scholarly journals Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning

2020 ◽  
Vol 10 (21) ◽  
pp. 7413
Author(s):  
Taing Borith ◽  
Sadirbaev Bakhit ◽  
Aziz Nasridinov ◽  
Kwan-Hee Yoo
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active State ◽  
Economic Losses ◽  
Inactive State ◽  
Machine Learning Methods ◽  
Catastrophic Failures ◽  
Modern Manufacturing ◽  
Active Detection ◽  
Industrial Machine

In modern manufacturing, the detection and prediction of machine anomalies, i.e., the inactive state of the machine during operation, is an important issue. Accurate inactive state detection models for factory machines can result in increased productivity. Moreover, they can guide engineers in implementing appropriate maintenance actions, which can prevent catastrophic failures and minimize economic losses. In this paper, we present a novel two-step data-driven method for the non-active detection of industry machines. First, we propose a feature extraction approach that aims to better distinguish the pattern of the active state and non-active state of the machine by multiple statistical analyses, such as reliability, time-domain, and frequency-domain analyses. Next, we construct a method to detect the active and non-active status of an industrial machine by applying various machine learning methods. The performance evaluation with a real-world dataset from the automobile part manufacturer demonstrates the proposed method achieves high accuracy.

scholarly journals Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach

2021 ◽  
Vol 11 (13) ◽  
pp. 5792
Author(s):  
Siu Ki Ho ◽  
Harish Chandra Nedunuri ◽  
Wamadeva Balachandran ◽  
Jamil Kanfoud ◽  
Tat-Hean Gan
Keyword(s):  
Feature Extraction ◽  
Random Vibration ◽  
Correct Diagnosis ◽  
Ground Truth ◽  
False Alarms ◽  
Optimal Filtering ◽  
Vibration Data ◽  
Reliable Performance ◽  
Vibration Signatures ◽  
Industrial Machine

Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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