Model Based Fault Diagnosis in Rotating Machinery
نویسندگان
چکیده
A continuing task in engineering is to increase the reliability, availability and safety of technical processes and to achieve these fault diagnosis becomes an advanced supervision tool in the present industries. Vibration in rotating machinery is mostly caused by unbalance, misalignment, shaft crack, mechanical looseness and other malfunctions. The objective of this paper is to propose a model based scheme for fault diagnosis of a rotor system. Presence of faults changes the dynamic behaviour of the system which is taken into account by equivalent loads acting on the healthy system model. In order to diagnose the faults in a rotor system the experimental time responses for healthy system as well as for faulty system were used. It was observed that the proposed scheme successfully detects and identifies the type, location and amount of fault in a rotor system for unbalance, misalignment and crack. This method has thus demonstrated the efficacy of the model based fault detection system for a simple rotor-bearing system.
منابع مشابه
A 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 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 کاملRotating Machinery Fault Diagnosis Based on Wavelet Fuzzy Neural Network
According to complicated fault characteristic of rotating machinery, its fault diagnosis based on wavelet fuzzy neural network (WFNN) which combines wavelet packet analysis and fuzzy neural network is put forward. By using it, the fuzzy fault diagnosis of rotating machinery is realized. All the arithmetic process of WFNN is realized through the computer. The results of simulation and test indic...
متن کاملFault Diagnosis for Rotating Machinery: A Method based on Image Processing
Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based...
متن کاملLMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information
Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. Firs...
متن کاملFault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning
Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represente...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011