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Bearings are a critical component of a rotary engine. Bearing faults caus a decrease in engine performance and has the potential to stop a production process. Therefore, monitoring and diagnosis of bearing conditions is very important. Envelope analysis is a reliable method used to diagnose bearings. The selection of demodulation band in envelope analysis is often done by comparing the healthy bearing spectrum with the faulty bearing spectrum to determine the resonance frequency at which the transient signal occurs due to the fault. However, information on the spectrum of healthy bearings is not always practically available in the field. Spectral kurtosis (SK) is known as a technique that can determine the modulated band without the condition of historical information. Kurtosis is a statistical parameter whose value is close to zero for gaussian noise and stationary signals, but its value is large for impulsive signals with short transient properties such as those produced by bearing faults. The paper proposes the application of SK in determining the optimum resonance band on the analysis envelope for diagnosing bearings. SK analysis parameters (i.e. window length) were explored to obtain optimal results. The diagnostic results are presented using the actual signal bearing the inner race fault. [ABSTRACT FROM AUTHOR]

River erosion is a serious and worrying problem. River erosion can lead to disasters such as landslides and damage to water structures. The material that is often carried away from the erosion process is bedload, a large sedimentary material that moves on the riverbed. The process of moving the bedload is called bedload transport. To anticipate erosion problems, steps are taken to examine the characteristics and properties of vibrations on the bedload. The research was conducted in a laboratory using an open channel cross-sectional model, namely the multi-purpose teaching flume. The research was conducted using the frequency and time domain method. In this study, the bedload vibration signal was recorded with the accelerometer sensor. The data recorded by the accelerometer was extracted and analyzed. From the results of the accelerometer data extract, it can be concluded that the diameter and discharge of the bedload affect the properties and characteristics of the vibration of the bedload which varies at each frequency and time parameter. [ABSTRACT FROM AUTHOR]

Most of the existing technique for water pipeline leakage detection is based on the vibration characteristic such as spectrum which is relatively difficult to be applied due to transient and non-stationary vibration. This paper proposes a vibration-based method for leakage detection of water pipeline network using support vector machine (SVM) as a classifier with strong generalization ability. Eight statistical parameters are selected for feature extraction from time waveform. In this paper linear discriminant analysis (LDA) will be introduced as a pre-processing step before classification process. LDA transform original statistical parameters by project them onto discriminant direction thus maximize between-class and minimize within-class variance. The proposed method will produce better hyperplane margin than that of SVM solely thus produce good generalizations to new instances. The novel pipeline leakage detection and classification called LDA-SVM is established. The performance of LDA-SVM is validated by applying it to leakage detection of pipeline network test rig. The results show that the method reaches high accuracy both in training and testing process. [ABSTRACT FROM AUTHOR]

Envelope analysis is an effective and widely used method of detecting localized fault in rolling element bearings. However, this method requires selecting the optimum resonance-band to give accurate results. Resonance-band selection is not always easy and practical in the field. Therefore, this paper proposes a method based on kurtogram and correlated kurtosis. Correlated Kurtosis instead of spectral kurtosis is used to filter the raw vibration signal and then used to produce the kurtogram where the parameters are used to select optimal resonance-band for envelope analysis. Several cases of experimental bearing fault signals are used to evaluate the accuracy of the proposed method. The performance of the proposed method is also compared to other previous method, namely envelope spectrum based on spectral kurtosis. The results show that the proposed method is effective and robust in detecting local fault in rolling element bearings under heavy noise. [ABSTRACT FROM AUTHOR]

This study proposes a new method for fault diagnosis in ball bearings based on wavelet transform and principal component analysis (PCA) of the acquired vibration signals. The signals collected are pre-processed using a wavelet transform to decompose the signals into low (approximated) and high (detailed) frequency part where the high-frequency part are needed for fault diagnosis purposes. Eleven potential statistical features are then extracted from the high-frequency part coming from different bearing fault signals and those from healthy bearings as well. Four types of signals are proposed, they are outer race fault, inner race fault, ball fault and no-fault signals. The PCA is used to linearly transform and reduce multidimensional data resulted from statistical extraction down to a few dimensions for more straightforward analysis. Six principal components retaining more than 95% significance level are used for bearing fault detection and classification. By combining the wavelet transform, statistical features extraction and PCA, the proposed method successfully detected and classified fault types without knowledge of a bearing fault frequencies and analysis from experienced users. [ABSTRACT FROM AUTHOR]

River erosion is a serious and worrying problem. River erosion can lead to disasters such as landslides and damage to the river structures. The bedload material, a large sedimentary material that moves on the riverbed, is often carried away from the erosion process. The process of the bedload movement is called bedload transport. To anticipate the erosion problems, steps are taken to examine the characteristics and properties of vibrations on the bedload. The research was conducted in a laboratory using an open channel cross-sectional model, namely the multi-purpose teaching flume. The research was conducted using the frequency domain method. In this study, the bedload vibration signal will be recorded with the accelerometer sensors. The data recorded by the accelerometer will be extracted and analyzed. From the results of the accelerometer data extract, it can be concluded that the size and discharge of the bedload affect the properties and characteristics of the vibration of the bedload which will vary at each frequency. [ABSTRACT FROM AUTHOR]