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...

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...

torque, speed, and power as mechanical variables are associated with the functional performance of any rotating machinery. the real-time performance and the efficiency of a machine can be determined with on-line measurement of these parameters. in this investigation a rotary torque meter (transducer) was constructed from strain gauge sensors for measuring the torque of rotating shafts. the syst...

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...

Reliability of rotating machinery has a significant relation with personal safety and economic efficiency. With the development of science and technology, the improvement of performance degradation of machinery becomes increasingly higher. Research of traditional reliability theory depends on failure data. Some lifetime data result in little or even no failure. For mechanical equipment, degrada...

Novelty detection has been developed into a state-of-the-art technique to detect abnormal behavior and trigger alarm for in-field machine maintenance. With built-up models of normality, it has been widely applied to several situations with normal supervising dataset such as shaft rotating speed and component temperature available meanwhile in the absence of fault information. However, the resea...

In diagnosing mechanical faults of rotating machinery, it is very important to know the vibration feature of the machine with various forms of fault. A rotor system with fault is generally a complicated non-linear vibrating system. Its vibration is in a very complex form. Rotating machinery is very popular in industrial applications. Most of the mechanical failures are due to vibrations. It is ...

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...

This paper reports several intelligent diagnostic approaches for rotating machinery based on artificial intelligence methods and feature extraction of vibration signals. That is: the diagnosis method based on wavelet transform, rough sets and neural network; the diagnosis method based on sequential fuzzy inference; diagnosis approach by possibility theory and certainty factor model; the diagnos...