Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines

Yang, Bo‐Suk; Hwang, Won-Woo; Kim, Dongjo; Tan, Andy

doi:10.1016/j.ymssp.2004.06.002

Cited by 138 publications

(45 citation statements)

References 17 publications

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“…Unfortunately, not all the extracted features are equally useful in trouble-shooting and experience has shown that even the most useful features are seldom used in the most effective way. In particular the interactions between and among features are not fully considered or even ignored [1] which may undermine the accuracy of diagnosis when the features employed are synergetic.…”

Section: Introductionmentioning

confidence: 99%

“…Significant features were extracted from both acoustic signals and vibration signals. The selection of relevant radial basis function (RBF) kernel parameters was carried out through iteration [1] for more accurate classification of healthy and faulty compressors. In a similar application, SVM methods were applied to reciprocating compressor butterfly valves to classify cavitation faults [3].…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis of reciprocating compressors using revelance vector machines with a genetic algorithm based on vibration data

Ahmed

Smith

et al. 2014

2014 20th International Conference on Automation and Computing

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Abstract-This paper focuses on the development of an advanced fault classifier for monitoring reciprocating compressors (RC) based on vibration signals. Many feature parameters can be used for fault diagnosis, here the classifier is developed based on a relevance vector machine (RVM) which is optimized with genetic algorithms (GA) so determining a more effective subset of the parameters. Both a one-against-one scheme based RVM and a multiclass multi-kernel relevance vector machine (mRVM) have been evaluated to identify a more effective method for implementing the multiclass fault classification for the compressor. The accuracy of both techniques is discussed correspondingly to determine an optimal fault classifier which can correlate with the physical mechanisms underlying the features. The results show that the models perform well, the classification accuracy rate being up to 97% for both algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of reciprocating compressors using revelance vector machines with a genetic algorithm based on vibration data

Ahmed

Smith

et al. 2014

2014 20th International Conference on Automation and Computing

View full text Add to dashboard Cite

show abstract

“…The interpretation of the extracted features has been mainly addressed with pattern recognition techniques, which match meaningful features in the measured data with patterns that characterize the different machine faults. In particular, research has been focused on neural networks [22], for their ability to model nonlinear systems, and statistical clustering with Support Vector Machines [23], to optimise the boundary curve between fault clusters.…”

Section: B Fault Detection and Diagnosis In The Context Of Machine Cmentioning

confidence: 99%

Importance of auxiliary systems for process fault detection and diagnosis

Cecı́lio

Chen

Thornhill

2011

2011 19th Mediterranean Conference on Control &Amp; Automation (MED)

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Abstract-In industrial processes, fault detection and diagnosis is traditionally applied separately to process and equipment without relating the information on both systems. This paper shows the interaction during fault propagation between the process and its auxiliary systems, i.e. mechanical, electrical and utility systems. Evidence is given with an industrial case study. The relevance of this finding is demonstrated by showing the advantages of including information from electrical and mechanical data in a root cause diagnosis test.

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“…pump, engine etc. [15] [16]. However the shallow and fault-specific nature of these features limits their performance for general vibration monitoring.…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Extraction on Vibration Data under Deep-Learning Framework: An Application for Fault Identification in Rotary Machines

Shaheryar¹,

Yin²,

Ramay³

2017

IJCA

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Mechanical failures in rotating machinery (e.g. wind turbines, generators, motor-derives etc.) may result in catastrophic failures. Different mechanical faults induce characteristic vibrations in the equipment structure. Online vibration monitoring helps mitigate catastrophic failures through early detection and identification of underlying mechanical faults. However, extracting characteristic vibration features that improve fault classification performance and are robust to various noises in the vibration signals is a challenging task. Various statistical and signal processing-based vibrationfeatures have been proposed in the literature. These vibrationfeatures were devised on the basis of prior knowledge about characteristics of vibration signals from different fault types. Recently, automatic feature extraction through unsupervised learning in deep neural architectures has resulted in state of art performance on image and speech recognition tasks. So, Instead of feature-engineering, we, here, hypothesized that feature learning on raw vibration signal possibly will extract vibration-features that can improve fault identification performance of subsequent classifier. To the purpose, we explored Convolutional Neural Network for unsupervised feature learning on vibration signals and Denoising AutoEncoder for extracting vibration features that are robust and invariant to the noises in vibration signals. We proposed a Hybrid deep-model consisting of a Multi-channel Convolutional Neural Network followed by a stack of Denoising Auto-Encoders (MCNN-SDAE) with a single classification layer at the top. We compared the fault identification and classification performance of the proposed model with other models employing tradition statistical and signal processing based vibration-features. We validated the performance of all models on a benchmark vibration data collected from an experimental test-rig specifically designed to study vibration characteristics of bearing related faults.

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Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines

Cited by 138 publications

References 17 publications

Fault diagnosis of reciprocating compressors using revelance vector machines with a genetic algorithm based on vibration data

Fault diagnosis of reciprocating compressors using revelance vector machines with a genetic algorithm based on vibration data

Importance of auxiliary systems for process fault detection and diagnosis

Robust Feature Extraction on Vibration Data under Deep-Learning Framework: An Application for Fault Identification in Rotary Machines

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