Extension Neural Network Learning Algorithms and Models and their Applications in Fault Diagnosis of Rolling Bearing
نویسندگان
چکیده
Extension neural network is a new type of neural network that combines extension theory and artificial neural network. Extension neural network has been applied to pattern recognition, fault diagnosis and clustering. According to fault characteristics of rolling bearing, we propose a fault diagnostic method for rolling bearing based on extension neural network. We construct the fault diagnosis model based on extension neural network along with the learning algorithm, which are then applied to fault recognition of rolling bearing. Simulation experiment indicates that this algorithm is easy to implement and has small training error and fast convergence speed. The algorithm has both theoretical and practical value.
منابع مشابه
A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks
A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here. The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...
متن کاملBearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm
Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estima...
متن کاملA Novel Intelligent Fault Diagnosis Approach for Critical Rotating Machinery in the Time-frequency Domain
The rotating machinery is a common class of machinery in the industry. The root cause of faults in the rotating machinery is often faulty rolling element bearings. This paper presents a novel technique using artificial neural network learning for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (harmmean and median), whic...
متن کاملImproving the Performance of Machine Learning Algorithms for Heart Disease Diagnosis by Optimizing Data and Features
Heart is one of the most important members of the body, and heart disease is the major cause of death in the world and Iran. This is why the early/on time diagnosis is one of the significant basics for preventing and reducing deaths of this disease. So far, many studies have been done on heart disease with the aim of prediction, diagnosis, and treatment. However, most of them have been mostly f...
متن کاملFault Diagnosis for a Rolling Bearing Used in a Reciprocating Machine by Adaptive Filtering Technique and Fuzzy Neural Network
This paper presents a method of fault diagnosis for a rolling bearing used in a reciprocating machine by the adaptive filtering technique and a fuzzy neural network. The adaptive filtering is used for noise cancelling and feature extraction from vibration signal measured for the diagnosis. A fuzzy neural network is used to automatically distinguish the fault types of a bearing by time domain fe...
متن کامل