The operating state of bearing affects the performance rotating machinery; thus, how to accurately extract features from original vibration signals and recognise faulty parts as early possible is very critical. In this study, one-dimensional ternary model which has been proved be an effective statistical method in feature selection introduced shapelet transformation proposed calculate parameter...

This work presents a technique for the analysis of Facial Electromyogram signal activities to classify five different facial expressions for Computer-Muscle Interfacing applications. Facial Electromyogram (FEMG) is a technique for recording the asynchronous activation of neuronal inside the face muscles with non-invasive electrodes. FEMG pattern recognition is a difficult task for the researche...

Empirical Mode Decomposition (EMD), due to its adaptive decomposition property for the non-linear and non-stationary signals, has been widely used in vibration analyses for rotating machinery. However, EMD suffers from mode mixing, which is difficult to extract features independently. Although the improved EMD, well known as the ensemble EMD (EEMD), has been proposed, mode mixing is alleviated ...

Statistical vibroacoustics, also called statistical energy analysis (SEA) in the field of engineering, is born from the application of statistical physics concepts to the study of random vibration in mechanical and acoustical systems. This article is a discussion on the thermodynamic foundation for that approach with particular emphasis devoted to the meaning of entropy, a concept missing in SE...

In the area of fault detect for rotating machinery, the vibration signal should be filtered before parameter detection and quality evaluation. For the filtering of the vibration signal, it is needed to keep the linear phase characteristics while obtaining good filtering effect. Aiming at the filtering problem of vibration signal, a filtering algorithm based on singular value subspace decomposit...

The periodic impulse feature is the most typical fault signature of the vibration signal from fault rolling element bearings (REBs). However, it is easily contaminated by noise and interference harmonics. In order to extract the incipient impulse feature from the fault vibration signal, this paper presented an autocorrelation function periodic impulse harmonic to noise ratio (ACFHNR) index base...

Mechanical fault diagnostics for quality control of manufacturing appliances is often based on the analysis of machine vibrations. In the presence of mechanical faults, vibration signals comprise periodic impulses with a characteristic frequency corresponding to a particular defect. Vibrometers based on LDV (Laser Doppler Vibrometry) are an alternative to the traditional use of piezo-electric a...

Artiicial neural networks provide a means of capturing stationary statistical information about machine vibrations in the form of non-linear autoregressive models. These models can be used as one step ahead pre-dictors allowing comparison of signals for the purposes of fault detection and diagnosis. From the prediction error, features can be extracted and used to determine the machine's conditi...