Pattern Recognition based Fault Diagnosis in Industrial Processes: Review and Application

Rauber, Thomas W.; Nascimento, Eduardo Mendel do; Wandekokem, Estefhan Dazzi; Varejão, Flávio Miguel

doi:10.5772/9365

Cited by 6 publications

(2 citation statements)

References 57 publications

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“…The extraction of feature attributes is in time or frequency domain in this area. The time domain analysis has physical significance however, the frequency domain analysis purely depends upon the statistical feature extraction [34,35]. The extracted features are in vector or scalar form that depicts the parameters from spectral analysis in frequency domain or in the form of a statistical vector indexing.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on fan vibration fault diagnosis based on image recognition

Huang¹,

Qiao²,

Khanna³

et al. 2021

J. vibroeng.

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The conventional methods for vibration fault detection and diagnosis relies on feature extraction from the waveforms of the vibration signals. This article exploits the scope of image recognition application for the detection and diagnosis of fan vibration faults. In this paper, a novel image recognition technique is proposed for vibration-based fault diagnosis using the spectrum images of the vibration signals. 1D vibration signal spectrum is initially computed using Fast Fourier Transform (FFT) and the FFT frequencies are adjusted such that it captures a vibration spectrum diagram as 2D image representation. FFT based vibration analysis is done and the image recognition concept is utilized for feature extraction and a machine learning classification module is used for fault analysis and diagnosis. Effective feature generation is done using Principal Component Analysis (PCA) by removing the redundancy from the feature vectors and machine learning classifiers are used to obtain improved image recognition and classification performance. Artificial Neural Network (ANN) classifier yields better performance in terms of various performance parameters and percentage improvement in terms of accuracy for ANN classification methods over Support Vector Machine (SVM), k-Nearest Neighbours (kNN) and Random Forest Ensemble (RFE) methods are 10.01 %, 4.51 % and 2.01 % respectively. Comparative scenarios are considered in this work for fan vibration fault detection as well as diagnosis based on the image features for various realistic vibration fault conditions. Effectiveness of the proposed image recognition-based technique is compared with the state-of-the-art methods, justifying its outperformance for fan fault detection and diagnosis using the combination of spectrum adjustment, PCA and ANN classification method.

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Section: Literature Reviewmentioning

confidence: 99%

Research on fan vibration fault diagnosis based on image recognition

Huang¹,

Qiao²,

Khanna³

et al. 2021

J. vibroeng.

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“…Feature extraction is critical and involves extracting useful information for fault diagnosis from a large amount of raw data [13]. Pattern recognition and fault classification are essential, requiring the application of deep learning algorithms to accurately discriminate and classify fault types based on the extracted features [14]. In recent years, deep learning technology has made significant advancements in pattern recognition and data analysis, demonstrating strong performance, particularly in fields such as image recognition, speech processing, and natural language processing.…”

Section: Introductionmentioning

confidence: 99%

The DMF: Fault Diagnosis of Diaphragm Pumps Based on Deep Learning and Multi-Source Information Fusion

Meng,

Shi,

Song

2024

Processes

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Effective fault diagnosis for diaphragm pumps is crucial. This paper proposes a diaphragm pump fault diagnosis method based on deep learning and multi-source information fusion (DMF). The time-domain features, frequency-domain features, and modulation features are extracted from the vibration signals from eight different positions. After feature enhancement and data preprocessing, the features are input into auto encoders (AE), convolutional neural networks (CNN), and support vector machines (SVM) to obtain the diagnostic results. The results indicate that the DMF method achieves a fault diagnosis accuracy of 99.98%, which is on average 9.09% higher than using a single diagnostic model. The demodulation method is more suitable for vibration signal feature extraction of the diaphragm pump, while the CNN is more suitable for identification of diaphragm pump faults. Specifically, it outperformed the sampling point 1-DPCA-AE model by 13.98% and the sampling point 4-DPCA-SVM model by 8.98%.

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