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This paper proposes an adaptive multiclass neurofuzzy classifier (MC-NFC) for fault detection and classification in solar photovoltaic (PV) systems. The designed fuzzy classifier was optimized by seeking for the best numerical values of the parameters that tune its membership functions. The experiments have been conducted on the basis of collected data from a real time PV array emulator (namely array...
Unmanned Aerial Vehicle (UAV) is being used in a wide range of human life. Researcher preferred quadrotor as it can be brought into the first generation of simulator map of an aircraft. It can be developed into larger manned flight. In this regard, extensive research in Fault detection (FD) is necessary, so that it can enhance its safety features. FD is designed to respond and to exclude the wrong...
The dynamic and system reliability of driving system in battery electric vehicles (BEVs) highly depend on the fault diagnosis technology. In this paper, we provided a new data compression approach and validated it on a method based on neural network (NN) to detect both failures' types and degree in drive system. In time-/frequency domain several statistical features were extracted from signals acquired...
In the industry, maintenance costs can be reduced by early detection and diagnosis. It can also improve the overall equipment efficiency of the machine system. To diagnose the problem is required a diagnosis system with a particular method. The Hidden Markov Model (HMM) method is used because it can determine the parameters that are hidden from the observable parameters. Then, The specified parameters...
In this paper using a machine with a motor configuration that is connected with 3 discs. Performance of a machine can be known by analyzing the vibrations that occur in the machine. Vibration that occurs on the machine may be normal or abnormal. Abnormal vibrations on a machine can cause severe damage. This abnormal vibration can be caused by the mass distribution of rotation no longer exists in the...
Early detection of small faults in closed-loop systems is a challenging issue in the fault diagnosis literature. The effect of faults in closed-loop systems will be obscured by a robust feedback control, especially when the controller is coupled with nonlinear uncertainty. In this paper, an approach for rapid detection for small faults in a class of closed-loop uncertain systems is proposed based...
In this paper, an approach for rapid fault detection for a class of nonlinear sampled-data systems is proposed. Firstly, a learning estimator is constructed to capture the unknown system dynamics effects in sampled-data systems. The key issue in the learning process is that partial neural weights will converge into their optimal values based on the deterministic learning theory. Then a knowledge bank...
This paper presents neural network based dynamic threshold generator for sensor failure detection in a three tank interacting level process. The Fault Detection and Identification scheme performs the tasks of failure Detection and Identification by continuously monitoring the outputs of the sensors and the estimates of the states. Estimation errors are observed and the decision functions are formed...
A new technique for the training of ANNs is presented. The time-domain vibration signals of rolling bearings with different fault conditions are preprocessed using differential evolution method, then further being trained by Levenberg Marquardt method. The processed data are applied as input vectors to artificial neural networks (ANNs) for rolling bearing fault classification. The hybrid training...
Fuel cell (FC) is considered as one of the most interesting solutions to overcome future energy crisis announced by the International Energy Agency. However, various bottlenecks, whether technological or societal, slow the industrial interest for this technology and therefore the mass production of fuel cells. One of these bottlenecks is related to the limited lifetime of FC system. To counter it,...
Fault Detection and Identification (FDI) monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A Quadrotor robot is used to represent a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. This dynamic model is based on the first principles of...
The naval gun weapon systems are the complex and large mechatronic systems which consist of mechanical system, electrical system and hydraulic system and so on. The systems have a wider working range, a worse working environment and a higher failure rate. Whether ship borne gun weapon systems work normally or not, they directly affect the performance indexes of the weapon systems, even do the entire...
Indian Power System is equipped mainly with the overhead (OH) line. The inclination towards the use of underground cable (UG) is less. Nowadays with the advent of the XLPE cable with high capacity to transmit power is taking up the interest of the power system engineers to use underground cable along with the overhead line. It is also a solution in the areas where population is more; environmental...
This paper presents a simple and reliable method for detecting the faults occurring in a permanent magnet synchronous generator (PMSG) connected with a three-phase uncontrolled rectifier bridge (TPURB), which is based on monitoring the output DC current. A PMSG connected with a TPURB at constant speed is discussed under two types of faults, namely phase winding faults and diode faults. An adaptive...
Any diagnosis procedure should be rapid and efficiency. In the presence of the noise and unknown inputs, the diagnosis procedure could generate false alarms. This paper focuses in the fault detection, isolation and identification when a fault affected the sensors of a DC motor worked in disturbance environment. The chosen technique should take account the two issues: the noise and the unknown inputs...
In this paper a survey on fault diagnosing techniques of electronic circuits are presented which are related mainly to industrial applications. Diagnozing the faults in circuit boards is very essential for achieving better reliability and easy maintainance of electronic systems. The circuit fault finding diagnosis is treated as the pattern recognition case and uses machine learning methodology. Increasing...
In the paper, a hybrid technique based fault tolerant control of Static Synchronous Series Compensator (SSSC) is utilized for power system stability improvement. The hybrid technique is a combination of artificial neural network (ANN) and Gravitational search algorithm (GSA). Here, ANN is used to evaluate the healthy and faulty sensor data of the system and which is correctly classified. The proposed...
This paper presents sensor fault detection in a three interacting level system and fault detection and control under sensor failure conditions. Trained Back Propagation neural networks estimate the process states. The fault detection and isolation is done by decision logic. If any failure is identified, the control law is modified accordingly using the estimated value as the substitution for the failed...
Power transmission is one of the fields drastically growing in the world presently. In this paper, it is aimed to provide a solution for detecting the fault and its location accurately by utilizing ISFHA-[Improved Sheep Flock Heredity Algorithm]. It is necessary to satisfy the customer in terms of power quality transmission. Power quality damages occur due to short circuit, natural disasters and other...
Fault Detection and Isolation (FDI) is important in many industries to provide safe operation of a process. To determine the kind, size, location and time of fault, many Fault detection and Identification (FDI) Techniques are proposed. The Characteristic of FDI techniques include robustness, fast detection and isolation of faults. In this paper a comparison of fault diagnosis system based on Artificial...
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