A Two-Stage Fault Detection and Isolation Platform for Industrial Systems Using Residual Evaluation

Heydarzadeh, Mehrdad; Nourani, Mehrdad

doi:10.1109/tim.2016.2575179

Cited by 53 publications

(9 citation statements)

References 22 publications

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“…Performance [8] 95% accurate under simulation [9] 93.2% accurate for wear fault detection under simulation [11] 78% accurate under simulation [13] 95% accurate under simulation [14] 89% accurate [36] 95% prediction accuracy under simulation…”

Section: Papermentioning

confidence: 99%

“…A sensor-less, control valve fault-detection method using an observer is discussed in [12]. In [13], fault detection and isolation in a pneumatic control valve used in a flow process are performed using residual evaluation of wavelet transform signal derived from process parameters. Fault diagnosis in actuators is simulated on the Development and Application Methods for Actuator Diagnosis in Industrial Control System (DAMADICS) workbench using fuzzy logic algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Venkata

Rao

2019

Electronics

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A control valve plays a very significant role in the stable and efficient working of a control loop for any process. In a fluid flow process, the probability of failure of a control valve may increase for many reasons pertaining to a flow process such as high pressures at the inlet, different properties of the liquid flowing through the pipe, mechanical issue related to a control valve, ageing, etc. A method to detect faults in the valve can lead to better stability of the control loop. In the proposed work, a technique is developed to determine the fault in a pneumatic control valve by analyzing the vibration data at the outlet of the valve. The fault diagnosis of the valve is carried out by analyzing the change in vibration of the pipe due to the change in flow pattern induced by the control valve. The faults being considered are inflow and insufficient supply pressure faults. Vibration data obtained is processed using a signal processing technique like amplification, Fourier transform, etc. The support vector machine (SVM) algorithm is used to classify the vibration data into two classes, one normal and the other faulty. The designed algorithm is trained to identify faults and subjected to test with a practical setup; test results show an accuracy of 97%.

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Section: Papermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Venkata

Rao

2019

Electronics

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“…The occurrence of false alarms is likely. Some alternative approaches take normal and/or abnormal patterns into consideration [7][8][9][10] by deep convolutional neural networks (CNN). For applications such as building defect detection, high classification accuracy can be achieved [10].…”

Section: Introductionmentioning

confidence: 99%

Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks

et al. 2021

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This study aims to develop a novel automated computer vision algorithm for quality inspection of surfaces with complex patterns. The proposed algorithm is based on both an autoencoder (AE) and a fully convolutional neural network (FCN). The AE is adopted for the self-generation of templates from test targets for defect detection. Because the templates are produced from the test targets, the position alignment issues for the matching operations between templates and test targets can be alleviated. The FCN is employed for the segmentation of a template into a number of coherent regions. Because the AE has the limitation that its capacities for the regeneration of each coherent region in the template may be different, the segmentation of the template by FCN is beneficial for allowing the inspection of each region to be independently carried out. In this way, more accurate detection results can be achieved. Experimental results reveal that the proposed algorithm has the advantages of simplicity for training data collection, high accuracy for defect detection, and high flexibility for online inspection. The proposed algorithm is therefore an effective alternative for the automated inspection in smart factories with a growing demand for the reliability for high quality production.

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“…In addition, the noise reduction method based on transform filtering theory [11][12][13] has also been widely studied by many scholars. Among them, the stationary wavelet transform has canceled the downsampling process of wavelet transform and has small translational degeneration, so it is widely used [14][15][16][17]. An improved adaptive closed-value denoising method for discrete stationary wavelets is proposed, which can remove some noise.…”

Section: Introductionmentioning

confidence: 99%

Construction of Value Chain E-Commerce Model Based on Stationary Wavelet Domain Deep Residual Convolutional Neural Network

Wang

2020

Complexity

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This paper mainly analyzes the current situation of e-commerce in domestic SMEs and points out that there are limited initial investment and difficulty in financing in China’s SMEs; e-commerce control is not scientific; e-commerce personnel of SMEs are not of high quality, in the case of improper setting of the e-commerce sector and shortage of talents, rigid management model, and outdated management concepts. By using the loss function and the value chain management theory of the deep learning in the stationary wavelet domain residual learning model, the e-commerce model of SMEs is newly constructed, and the e-commerce department as the core department of the enterprise is proposed. By training the optimal parameters of the deep residual network and comparing the results with other models, the method of this paper has a good effect against the sample. The original loss function based on the residual learning model deep learning is modified to solve the original model fuzzy problem, which improves the effect and has good robustness. Finally, based on the wavelet residual depth residual evaluation method, this paper evaluates the application effect of this model and proposes relevant suggestions for improving this model, including rationalizing and perfecting the external value chain coordination mechanism, establishing the e-commerce value chain sharing center, and promoting integration of e-commerce business, strengthening measures and recommendations in various aspects of e-commerce information construction. At last, taking the business activities of a company as an example, applying the theory described in this paper to specific practice proves the feasibility and practical value of the theory.

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A Two-Stage Fault Detection and Isolation Platform for Industrial Systems Using Residual Evaluation

Cited by 53 publications

References 22 publications

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks

Construction of Value Chain E-Commerce Model Based on Stationary Wavelet Domain Deep Residual Convolutional Neural Network

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