Vibration-based condition monitoring and fault diagnosis is an effective approach to maintain the reliable operation of a scroll compressor. Unfortunately, the vibration signal from the scroll compressor always has characteristics of being non-linear and non-stationary, which makes vibration signal analysis and fault feature extraction very difficult. To extract the significant fault features, ...

During production of glottal stops the glottal vibration has unequal cycles and is caused by laryngealization. While one can perceive the features of laryngealization in the speech, it is difficult to analyse the signal to detect these source features from the standard spectrum-based analysis methods. In this paper we propose methods to extract the voice source vibration characteristics, and sh...

Textural features are applied for detection of morphological pathologies of vocal cords. Cooccurrence matrices as statistical features are presented as well as filter bank analysis by Gabor filters. Both methods are extended to handle color images. Their robustness against camera movement and vibration of vocal cords is evaluated. Classification results due to three in vivo sequences are in bet...

Hydraulic brakes in automobiles play a vital role for the safety on the road; therefore vital components in the brake system should be monitored through condition monitoring techniques. Condition monitoring of brake components can be carried out by using the vibration characteristics. The vibration signals for the different fault conditions of the brake were acquired from the fabricated hydraul...

This paper proposed a new method for fault diagnosis of rolling bearings based on SURF (Speeded-Up Robust Features) algorithm, where two-dimension signal is used. Different from other classical 1-d signal processed methods, the proposed method transforms the 1-dimensional vibration signals into images, then image processed methods are utilized to analyze the image signal so as to reach the goal...

This paper presents a new approach to machine health monitoring based on the Approximate Entropy (ApEn), which is a statistical measure that quantifies the regularity of a time series, such as vibration signals measured from an electrical motor or a rolling bearing. As the working condition of a machine system deteriorates due to the initiation and/or progression of structural defects, the numb...

This paper presents a new application of mathematical morphology (MM) operators for low speed slew bearing condition monitoring. The MM operators were used as a signal processing step and feature extraction method for bearing vibration signals. Four basic MM operators; erosion, dilation, closing and opening, were studied. This paper also investigates another potential MM operator, namely gradie...