Diagnosis of electrocatalyst degradation in polymer electrolyte fuel cells under automotive conditions

This paper presents a fuzzy inference system approach for diagnosis of electrocatalyst degradation in polymer electrolyte fuel cells (PEFC’s) under automotive conditions. The fuzzy inference system enables diagnosis of electrocatalyst degradation based on fuel cell operating conditions. The method incorporates classification of selected input parameters on a scale of membership to fuzzy sets or categories and provides connection to any consequential degradation through a database of diagnostic rules. Experimental procedures involved drive cycle durability testing including the world harmonized light-duty vehicle test procedure (WLTP) and start/stop cycling. The observed results support the validation of the proposed membership functions within the fuzzy inference system and the database of diagnostic rules. This approach can provide a fast and effective diagnosis of electrocatalyst degradation in PEFC’s and enable proactive decision support for planning operation and maintenance strategies for improved fuel cell reliability, availability and durability.</div

Cancer Detection Using Neuro Fuzzy Classifier in CT Images

In this study, we have implemented an adaptive neuro fuzzy inference system (ANFIS) for detection of mass in CT images for early diagnosis of lung cancer. After completion of preprocessing and segmentation process four features have been extracted from images and given to ANFIS classifier as an input. The fuzzy system detects the severity of the lung nodules depends on IF-THEN rules. Feature based data set has been created with five fuzzy membership functions of each input. The proposed model is applied on more than 150 images and the computer added diagnosis (CAD) system achieved sensitivity of 97.27% and specificity of 95% with accuracy of 96.66%

Diagnosa Penyakit Tuberculosis (Tbc) Menggunakan Sistem Neuro Fuzzy

Tuberculosis (TBC or TB) is an infectious disease that usually attacks the lungs, caused by the bacterium Mycobacterium tuberculosis. WHO data report in 2006 put Indonesia as the third largest contributor of TB in the world. The high risk of dying of lung disease patients (18.7%) indicate that these diseases should be taken seriously. In addition to the lungs, where TB germs attack the brain and central nervous system, this will also lead to death (death). In this study the author uses neuro fuzzy system for diagnosing TB disease based mainly on clinical symptoms. Neuro fuzzy systems are part of the major components forming soft computing, integrated between fuzzy systems and artificial neural networks. With the method of Adaptive Neuro Fuzzy Inference System (ANFIS) in determining the classification rule with fuzzy logic that is able to provide a diagnosis like an expert whether someone is diagnosed: Negative TB, Other Disease and Positive TB. Based on ANFIS editor can be seen the results of measurements of the accuracy of the algorithm, the hybrid gets the same value of four types of membership function as Trapmf, gbellmf, gaussmf and psigmf of 99.99%. While the backpropagation algorithm produces different accuracies depending on each type of MF her. Where Trapmf membership type has an accuracy rate higher than the other three types of memberships by using the back propagation algorithm. And to see what the diagnosis was designed using Matlab toolbox applications, such as appearance and surface at the FIS rule editor, diagnosis and therapeutic treatment

Real Time Monitoring and Neuro-Fuzzy Based Fault Diagnosis of Flow Process in Hybrid System

Process variables vary with time in certain applications. Monitoring systems let us avoid severe economic losses resulting from unexpected electric system failures by improving the system reliability and maintainability The installation and maintenance of such monitoring systems is easy when it is implemented using wireless techniques. ZigBee protocol, that is a wireless technology developed as open global standard to address the low-cost, low-power wireless sensor networks. The goal is to monitor the parameters and to classify the parameters in normal and abnormal conditions to detect fault in the process as early as possible by using artificial intelligent techniques. A key issue is to prevent local faults to be developed into system failures that may cause safety hazards, stop temporarily the production and possible detrimental environment impact. Several techniques are being investigated as an extension to the traditional fault detection and diagnosis. Computational intelligence techniques are being investigated as an extension to the traditional fault detection and diagnosis methods. This paper proposes ANFIS (Adaptive Neural Fuzzy Inference System) for fault detection and diagnosis. In ANFIS, the fuzzy logic will create the rules and membership functions whereas the neural network trains the membership function to get the best output. The output of ANFIS is compared with Back Propagation Algorithm (BPN) algorithm of neural network. The training and testing data required to develop the ANFIS model were generated at different operating conditions by running the process and by creating various faults in real time in a laboratory experimental model

Evaluation of Novelty Detection Methods for Condition Monitoring applied to an Electromechanical System

Dealing with industrial applications, the implementation of condition monitoring schemes must overcome a critical limitation, that is, the lack of a priori information about fault patterns of the system under analysis. Indeed, classical diagnosis schemes, in general, outdo the membership probability of a measure in regard to predefined operating scenarios. However, dealing with noncharacterized systems, the knowledge about faulty operating scenarios is limited and, consequently, the diagnosis performance is insufficient. In this context, the novelty detection framework plays an essential role for monitoring systems in which the information about different operating scenarios is initially unavailable or restricted. The novelty detection approach begins with the assumption that only data corresponding to the healthy operation of the system under analysis is available. Thus, the challenge is to detect and learn additional scenarios during the operation of the system in order to complement the information obtained by the diagnosis scheme. This work has two main objectives: first, the presentation of novelty detection as the current trend toward the new paradigm of industrial condition monitoring and, second, the introduction to its applicability by means of analyses of different novelty detection strategies over a real industrial system based on rotatory machinery

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments