A Novelty Detection Approach to Compressor Valve Fault Detection and Localization

2020 ◽  
Author(s):  
Colton M. Scott ◽  
Jason R. Kolodziej
Keyword(s):  
Frequency Analysis ◽  
Learning Algorithm ◽  
Novelty Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Flow Profile ◽  
Shape Factors ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Vibration Data

Abstract Presented in this paper is the development of a vibration-based novelty detection algorithm for locating and identifying valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis, image-based pattern recognition, and one-class support vector machines. A commonly reported cause of valve wear-related machine downtime is wear in the valve seat, causing a change in flow profile into and out of the compression chamber. Seeded faults are introduced into the valve manifolds of the ESH-1 industrial compressor and vibration data collected and separated into individual crank cycles before being analyzed using time-frequency analysis. The result is processed as an image and features used for classification are extracted using 1st and 2nd order images statistics and shape factors. A one-class support vector machine learning algorithm is then trained using data collected during healthy operation and then used to both detect and locate anomalous valve behavior with a greater than 82% success rate.

scholarly journals Time-Frequency Analysis of Nonlinear Systems: The Skeleton Linear Model and the Skeleton Curves

2003 ◽  
Vol 125 (2) ◽  
pp. 170-177 ◽  
Author(s):  
Lili Wang ◽  
Jinghui Zhang ◽  
Chao Wang ◽  
Shiyue Hu
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Linear Model ◽  
Frequency Analysis ◽  
Nonlinear Behavior ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Vibration Data ◽  
Identification Technique ◽  
Stiffness And Damping

The joint time-frequency analysis method is adopted to study the nonlinear behavior varying with the instantaneous response for a class of S.D.O.F nonlinear system. A time-frequency masking operator, together with the conception of effective time-frequency region of the asymptotic signal are defined here. Based on these mathematical foundations, a so-called skeleton linear model (SLM) is constructed which has similar nonlinear characteristics with the nonlinear system. Two skeleton curves are deduced which can indicate the stiffness and damping in the nonlinear system. The relationship between the SLM and the nonlinear system, both parameters and solutions, is clarified. Based on this work a new identification technique of nonlinear systems using the nonstationary vibration data will be proposed through time-frequency filtering technique and wavelet transform in the following paper.

scholarly journals Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis

2019 ◽  
Vol 19 (4) ◽  
pp. 185-194 ◽  
Author(s):  
Meng-Kun Liu ◽  
Peng-Yi Weng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Ball Bearing ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Time Frequency Analysis ◽  
Bearing Faults ◽  
Time Frequency ◽  
Low Efficiency

Abstract Motor-driven machines, such as water pumps, air compressors, and fans, are prone to fatigue failures after long operating hours, resulting in catastrophic breakdown. The failures are preceded by faults under which the machines continue to function, but with low efficiency. Most failures that occur frequently in the motor-driven machines are caused by rolling bearing faults, which could be detected by the noise and vibrations during operation. The incipient faults, however, are difficult to identify because of their low signal-to-noise ratio, vulnerability to external disturbances, and non-stationarity. The conventional Fourier spectrum is insufficient for analyzing the transient and non-stationary signals generated by these faults, and hence a novel approach based on wavelet packet decomposition and support vector machine is proposed to distinguish between various types of bearing faults. By using wavelet and statistical methods to extract the features of bearing faults based on time-frequency analysis, the proposed fault diagnosis procedure could identify ball bearing faults successfully.

scholarly journals Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6239
Author(s):  
Asif Khan ◽  
Salman Khalid ◽  
Izaz Raouf ◽  
Jung-Woo Sohn ◽  
Heung-Soo Kim
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Experimental Testing ◽  
Laminated Composites ◽  
Structural Vibration ◽  
Support Vector ◽  
Learning Models ◽  
Time Frequency ◽  
Vibration Data

Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data.

scholarly journals Application of Time-Frequency Analysis to Transient Data from Centrifuge Earthquake Testing

2000 ◽  
Vol 7 (4) ◽  
pp. 195-202 ◽  
Author(s):  
David E. Newland ◽  
Gary D. Butler
Keyword(s):  
Frequency Analysis ◽  
Good Method ◽  
Centrifuge Modeling ◽  
Transient Vibration ◽  
Time Frequency Analysis ◽  
Harmonic Wavelets ◽  
Time Frequency ◽  
Vibration Data ◽  
Harmonic Wavelet ◽  
Detailed Interpretation

Centrifuge model experiments have generated complex transient vibration data. New algorithms for time-frequency analysis using harmonic wavelets provide a good method of analyzing these data. We describe how the experimental data have been collected and show typical time-frequency maps obtained by the harmonic wavelet algorithm. Some preliminary comments on the interpretation of these maps are given in terms of the physics of the underlying model. Important features of the motion that are not otherwise apparent emerge from the analysis. Later papers will deal with their more detailed interpretation and their implications for centrifuge modeling.

Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders

2007 ◽  
Vol 37 (4) ◽  
pp. 571-578 ◽  
Author(s):  
Everthon Silva Fonseca ◽  
Rodrigo Capobianco Guido ◽  
Paulo Rogério Scalassara ◽  
Carlos Dias Maciel ◽  
José Carlos Pereira
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Frequency Analysis ◽  
Voice Disorders ◽  
Support Vector ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Vector Machines
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