This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method co...

The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating...

Abstract In this paper we examine several features derived from the speech signal for the purpose of identification of speaker or language from the speech signal. Most of the current systems for speaker and language identification use spectral features from short segments of speech. There are additional features which can be derived from the residual of the speech signal, which correspond to th...

In order to obtain the useful information from the raw data which contain the state data reflecting the structure condition and the noise, de-noising and feature extraction techniques based on Wavelet analysis were studied. An improved wavelet thresholding algorithm to eliminate the noise for vibration signals was proposed. Comparison analysis with other thresholding algorithms shows that the n...

In this paper artificial neural networks and support vector machines are used to reduce the amount of vibration data that is required to estimate the Time Domain Average of a gear vibration signal. Two models for estimating the time domain average of a gear vibration signal are proposed. The models are tested on data from an accelerated gear life test rig. Experimental results indicate that the...

This paper proposes a novel feature extraction method for intrusion event recognition within a phase-sensitive optical time-domain reflectometer (Φ-OTDR) sensing system. Feature extraction of time domain signals in these systems is time-consuming and may lead to inaccuracies due to noise disturbances. The recognition accuracy and speed of current systems cannot meet the requirements of Φ-OTDR o...

As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying th...

This pattern recognition method can effectively identify vibration signals collected by a phase-sensitive optical time-domain reflectometer (Φ-OTDR) and improve the accuracy of alarms. An alignment-free end-to-end multi-vibration event detection based on Φ-OTDR is proposed, detecting different events in frequency bands. The pulse accumulation cancellers determine location events. local differen...

Anti-vibration units have been developed to realize micro-vibration circumstance. Recently, the anti-vibration units using the piezo-elements have been widely developed. In these units, acceleration sensors were ordinarily used. However, in the literature 3), velocity sensors were attached to detect the vibration of structure. Then, the state-feedback theory was naturally applicable to the anti...

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the ...