Multi-scale kernel Fisher discriminant analysis with adaptive neuro-fuzzy inference system (ANFIS) in fault detection and diagnosis framework for chemical process systems

2019 ◽  
Vol 32 (13) ◽  
pp. 9283-9297 ◽  
Author(s):  
Norazwan Md Nor ◽  
Mohd Azlan Hussain ◽  
Che Rosmani Che Hassan
Keyword(s):  
Fuzzy Inference ◽  
Fault Detection And Diagnosis ◽  
Fisher Discriminant Analysis ◽  
Inference System ◽  
Process Systems ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis ◽  
Neuro Fuzzy ◽  
Detection And Diagnosis ◽  
Chemical Process Systems

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR FAULT DETECTION AND DIAGNOSIS OF PNEUMATIC VALVE IN CEMENT INDUSTRY

2011 ◽  
Vol 10 (04) ◽  
pp. 399-423
Author(s):  
P. SUBBARAJ ◽  
B. KANNAPIRAN
Keyword(s):  
Fault Detection ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Cement Industry ◽  
Anfis Model ◽  
Inference System ◽  
Pneumatic Valve ◽  
Spray System ◽  
Neuro Fuzzy ◽  
Detection And Diagnosis

The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. The early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. Since the operator cannot monitor all the variables simultaneously, an automated approach is needed for the real time monitoring and diagnosis of the system. This paper presents the design and development of adaptive neuro-fuzzy inference system (ANFIS) model based for the fault detection of pneumatic valve in cooler water spray system in cement industry. The fault detection model is developed by using two different approaches, namely, ANFIS and feed forward network with back propagation algorithm (BPN). The training and testing data required are developed for the ANFIS model and BPN model that were generated at different operating conditions by operating the pneumatic valve and by creating various faults in real time in a laboratory experimental model. The performance of the developed ANFIS model and back propagation were tested and also compared for a total of 19 faults in pneumatic valve used in cooler water spray system. Obtained results of the ANFIS performed better than BPN.

A review of data-driven fault detection and diagnosis methods: applications in chemical process systems

2020 ◽  
Vol 36 (4) ◽  
pp. 513-553 ◽  
Author(s):  
Norazwan Md Nor ◽  
Che Rosmani Che Hassan ◽  
Mohd Azlan Hussain
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Chemical Process ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Learning Approaches ◽  
Multivariate Statistical ◽  
Process Systems ◽  
Detection And Diagnosis ◽  
Chemical Process Systems

AbstractFault detection and diagnosis (FDD) systems are developed to characterize normal variations and detect abnormal changes in a process plant. It is always important for early detection and diagnosis, especially in chemical process systems to prevent process disruptions, shutdowns, or even process failures. However, there have been only limited reviews of data-driven FDD methods published in the literature. Therefore, the aim of this review is to provide the state-of-the-art reference for chemical engineers and to promote the application of data-driven FDD methods in chemical process systems. In general, there are two different groups of data-driven FDD methods: the multivariate statistical analysis and the machine learning approaches, which are widely accepted and applied in various industrial processes, including chemicals, pharmaceuticals, and polymers. Many different multivariate statistical analysis methods have been proposed in the literature, such as principal component analysis, partial least squares, independent component analysis, and Fisher discriminant analysis, while the machine learning approaches include artificial neural networks, neuro-fuzzy methods, support vector machine, Gaussian mixture model, K-nearest neighbor, and Bayesian network. In the first part, this review intends to provide a comprehensive literature review on applications of data-driven methods in FDD systems for chemical process systems. In addition, the hybrid FDD frameworks have also been reviewed by discussing the distinct advantages and various constraints, with some applications as examples. However, the choice for the data-driven FDD methods is not a straightforward issue. Thus, in the second part, this paper provides a guideline for selecting the best possible data-driven method for FDD systems based on their faults. Finally, future directions of data-driven FDD methods are summarized with the intent to expand the use for the process monitoring community.

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

2021 ◽  
Vol 13 (2) ◽  
pp. 58-79
Author(s):  
Imadeddine Harzelli ◽  
Abdelhamid Benakcha ◽  
Tarek Ameid ◽  
Arezki Menacer
Keyword(s):  
Induction Motor ◽  
Closed Loop ◽  
Sliding Mode ◽  
Fault Detection And Diagnosis ◽  
Data Set ◽  
Inference System ◽  
Stator Current ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Detection And Diagnosis

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

Sign in / Sign up

Export Citation Format

Share Document