Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This p...

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to hig...

The vibration signal is nonstationary and it is difficult to acquire the sample with typical fault. An improved ACO algorithm based on adaptive control parameters is introduced into SVM model to propose a new fault diagnosis (IMASFD) method in this paper. In the IMASFD method, the EMD method is used to decompose fault vibration signal into IMF components, the energy of IMF components is selecte...

Envelope Detection (ED) is traditionally always used with Fast Fourier Transform (FFT) to identify the rolling element bearing faults. The inability of FFT to detect non-stationary signals makes Wavelet Analysis (WA) an alternative for machinery fault diagnosis as WA can detect both stationary and non-stationery signals. A comparative study of ED with FFT and WA techniques for bearing fault dia...

The wavelet de-noising technique with wavelet based function has been used in this paper for bearing fault detection. The applications of the wavelet de-noising show that the fault pulses in time-domain of the de-noised signals are easily to be detected as a result of removing the covering noise, which is not possible through the time-domain analysis of the original signal. Furthermore, the rec...

Fault diagnosis of monoblock centrifugal pump is conceived as a pattern recognition problem. There are three important steps to be performed in pattern recognition namely feature extraction, feature selection and classification. In this study, Stationary wavelet transform (SWT) is used for feature extraction from the input signals and Bayes net classifier is used for classification. A WEKA impl...

Intelligent fault diagnosis benefits from efficient feature selection. Neighborhood rough sets are effective in feature selection. However, determining the neighborhood value accurately remains a challenge. The wrapper feature selection algorithm is designed by combining the kernel method and neighborhood rough sets to self-adaptively select sensitive features. The combination effectively solve...

Fault diagnosis of monoblock centrifugal pump essentially forms a pattern recognition problem. There are three important steps to be performed in pattern recognition namely feature extraction, feature selection and classification. In this study, stationary wavelet transform (SWT) is used for feature extraction and SMO algorithm (a WEKA implementation of Support Vector Machine (SVM) algorithm) i...

Health diagnosis of bearing is essential reduce the breakdowns of rotating machinery. An intelligent method to diagnose the bearing fault using vibration signal is proposed. This paper proposes a binary genetic algorithm (BGA) in feature selection process and discuss about the role of fitness functions in feature selection process by application of different fitness functions in GA process. A v...

The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. In complex machines, the vibration generated by a component is easily affected by the vibration of other components or is corrupt...