Self adaptive growing neural network classifier for faults detection and diagnosis

Barakat, M.; Druaux, Fabrice; Lefebvre, Dimitri; Khalil, Mohamad; Mustapha, O.

doi:10.1016/j.neucom.2011.08.001

Cited by 80 publications

(32 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the following, Wen and Zhou's and the proposed methods are used to deal with the rotor fault diagnosis problem, respectively. Based on (11) and (14), the degree of similarity between generalized trapezoidal fuzzy numbers̃and each fault feature parameter shown in Table 3 can be calculated. The calculating rotor fault diagnosis results of Wen and Zhou's and the proposed methods are shown in Tables 5 and 6, respectively.…”

Section: Calculate the Degree Of Similarity Between Fuzzy Numbersmentioning

confidence: 99%

“…To date, a large number of valuable approaches have been proposed for dealing with fault analysis issues, such as fuzzy theories [4,5], expert system [6], wavelet analysis [7,8], data fusion [9,10], and neural network [11,12]. Particularly, fuzzy approach is most successfully applied in fault diagnosis because it is in the simplest and most used form [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Similarity Measure of Generalized Trapezoidal Fuzzy Numbers and Its Application on Rotor Fault Diagnosis

Zuo

Wang

Yin

2013

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Fault diagnosis technology plays a vital role in the variety of critical engineering applications. Fuzzy approach is widely employed to cope with decision-making problems because it is in the simplest and most used form. This paper proposed a new similarity measure of generalized trapezoidal fuzzy numbers used for fault diagnosis. The presented similarity measure combines concepts of the geometric distance, the center of gravity point, the perimeter, and the area of the generalized trapezoidal fuzzy numbers for calculating the degree of similarity between generalized trapezoidal fuzzy numbers. This method is proposed to deal with both standardized and nonstandardized generalized trapezoidal fuzzy numbers. Some properties of the proposed similarity measure have been proved, and 12 sets of generalized fuzzy numbers have been used to compare the calculation results of the proposed similarity measures with the existing similarity measures. Comparison results indicate that the proposed similarity measure can overcome the drawbacks of existing similarity measures. Finally, a fault diagnosis experiment is carried out in laboratory based on multifunctional flexible rotor experiment bench. Experimental results demonstrate that the proposed similarity measure is more effective than other methods in terms of rotor fault diagnosis.

show abstract

Section: Calculate the Degree Of Similarity Between Fuzzy Numbersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Similarity Measure of Generalized Trapezoidal Fuzzy Numbers and Its Application on Rotor Fault Diagnosis

Zuo

Wang

Yin

2013

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The target from this idea is to optimize the number of neurons and to locate them in suitable positions that are most sensitive to the training data so the dimension of subspaces are determined at the end of training process. [3] SAGNN is an input-output pair classifier. It consists of three feed forward hidden layers.…”

Section: A Self Adaptive Growing Neural Networkmentioning

confidence: 99%

“…However, they suffer from serious limitations, such as the moving target problem which is the interference between old and new learned knowledge. To overcome these problems, we proposed the use of growing neural networks in an advanced and optimized learning based on our previous contribution Self Adaptive Growing Neural Network (SAGNN) [3]. This approach is based on generating feature vectors by mean of Discrete Wavelet Transform (DWT) at the preprocessing stage.…”

Section: Introductionmentioning

confidence: 99%

Advanced Growing Neural Network approach for the fault diagnosis of large scale industrial plants

Barakat¹,

Khalil

Lefebvre

et al. 2012

2012 16th IEEE Mediterranean Electrotechnical Conference

Self Cite

View full text Add to dashboard Cite

Detecting and diagnosing faults before deteriorating the system performance is a crucial task for the reliability and safety of many engineering systems. A parameter selection with Self Adaptive Growing Neural Network (SAGNN) is developed for automatic Fault Detection and Diagnosis (FDD) in industrial environments. The growing and adaptive skill of SAGNN allows it to change its size and structure according to the training data. An advanced parameter selection criterion is embedded in SAGNN algorithm based on the computed performance rate of training samples. At growing stage, neurons are added to hidden subspaces of SAGNN while its competitive learning is an adaptive process in which neurons become more sensitive to different input patterns. The proposed classifier is applied to the fault detection and diagnoses of disturbances in chemical plant. Classification results are analyzed, explained and compared with various techniques based on neural networks.

show abstract

“…Indeed, fault diagnosis has been very much regarded as an integrated part for many process control systems. In this context, many methods have been developed as for instance observer-based approaches [1][2][3][4], identification-based methods [5,6], fuzzy logic-based approaches [7][8][9][10] and neural-network-based methods [11,12]. The effectiveness of these approaches for detecting and diagnosing certain types of system faults, which can take the form of sensor faults, actuator faults or process faults, was demonstrated in several application cases [13,14].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis and fault tolerant control scheme for a class of non‐linear singular systems

Yao

Cocquempot

Wang

2015

IET Control Theory & Applications

View full text Add to dashboard Cite

A new fault detection and diagnosis method is developed in this study for a class of singular non-linear systems via the use of observers with adaptive updating rules. The Lyapunov stability theory is applied to obtain a stable observation error system and to obtain the required adaptive tuning rules for the estimation of the process faults. Based on the fault estimation information, the controller is reconfigured to keep the post-fault system stable, leading to the active fault tolerant control of the non-linear singular system. A simulated numerical example is included to demonstrate the effectiveness of the proposed approach.

show abstract

Self adaptive growing neural network classifier for faults detection and diagnosis

Cited by 80 publications

References 30 publications

A New Similarity Measure of Generalized Trapezoidal Fuzzy Numbers and Its Application on Rotor Fault Diagnosis

A New Similarity Measure of Generalized Trapezoidal Fuzzy Numbers and Its Application on Rotor Fault Diagnosis

Advanced Growing Neural Network approach for the fault diagnosis of large scale industrial plants

Fault diagnosis and fault tolerant control scheme for a class of non‐linear singular systems

Contact Info

Product

Resources

About