Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis

Wong, Pak Kin; Zhong, Jiajun; Yang, Zhixin; Vong, Chi‐Man

doi:10.1016/j.neucom.2015.02.097

Cited by 69 publications

(41 citation statements)

References 33 publications

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“…Once features are extracted, traditional learning methods are then applied to separate, classify, and predict faults from 2 Shock and Vibration learned patterns present within the layers of the feature vector [5,6]. These layers of features are constructed by human engineers; therefore, they are subject to uncertainty and biases of the domain experts creating these vectors.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

294

198

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Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Timefrequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

294

198

View full text Add to dashboard Cite

show abstract

“…For example, some common features include correlation, contrast, energy, homogeneity, and entropy . These features are then fed to a traditional learning method with the task to identify, separate, and classify damage . Feature extraction relies on prior engineering knowledge of the data such that choosing which features to include or exclude within the model is subjected to uncertainty and biases of the domain experts.…”

Section: Convolutional Neural Network‐based Approach For Damage Detecmentioning

confidence: 99%

Convolutional neural networks for automated damage recognition and damage type identification

Modarres

Astorga

Droguett

et al. 2018

Struct Control Health Monit

141

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Recurring expenses associated with preventative maintenance and inspection produce operational inefficiencies and unnecessary spending. Human inspectors may submit inaccurate damage assessments and physically inaccessible locations, like underground mining structures, and pose additional logistical challenges. Automated systems and computer vision can significantly reduce these challenges and streamline preventative maintenance and inspection.The authors propose a convolutional neural network (CNN)-based approach to identify the presence and type of structural damage. CNN is a deep feed-forward artificial neural network that utilizes learnable convolutional filters to identify distinguishing patterns present in images. CNN is invariant to image scale, location, and noise, which makes it robust to classify damage of different sizes or shapes. The proposed approach is validated with synthetic data of a composite sandwich panel with debonding damage, and crack damage recognition is demonstrated on real concrete bridge crack images. CNN outperforms several other machine learning algorithms in completing the same task. The authors conclude that CNN is an effective tool for the detection and type identification of damage.

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“…Table 2 presents data about the engine, which are called faults feature factors as inputs of the BP neural network and faults type, which are the output of the BP neural network. 6 Crankshaft rolling speed(r/min) y 6 Oxygen sensor fault x 7 Throttle position(mv) x 8 air flow sensor (Hz)…”

Section: Tests and Experimentsmentioning

confidence: 99%

“…The final results have shown that the diagnostic system based on simulation can efficiently diagnose misfire, including location and severity. In general, currently available methods for engine fault diagnosis are mainly classified into model-based, knowledge-based and data-driven 8,9 according to Prof. PM Frank from Germany who is an international fault diagnosis authority.…”

Section: Introductionmentioning

confidence: 99%

Using Sensors Data and Emissions Information to Diagnose Engine’s Faults

Xie¹,

Wang²,

MacIntyre³

et al. 2018

IJCIS

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This paper proposes using engine's sensors data flow and exhaust emissions information to diagnose engine's faults, enhancing the accuracy of fault diagnosis. Engine fault diagnosis model is built using both this information and the mature BP neural network and genetic algorithms. In order to verify the method, we build a test platform, which includes South Korea Hyundai fault test vehicle and X-431 diagnosis instrument and AUTO5-1 exhaust gas analyzer and computer. The diagnostic accuracy rate can reach 98.33%, which is higher than using sensors data flow or the exhaust emissions information alone.

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Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis

Cited by 69 publications

References 33 publications

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Convolutional neural networks for automated damage recognition and damage type identification

Using Sensors Data and Emissions Information to Diagnose Engine’s Faults

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