Search

Demodulation of vibration signal to detect faults in machinery has been a prominent prevalent technique that is discussed by a number of authors. This paper deals with the demodulation of the current signal of an induction motor driving a multistage gearbox for its fault detection. This multistage gearbox has three gear ratios, and thus, three rotating shafts and their corresponding gear mesh frequencies (GMFs). The gearbox is loaded electrically by a generator feeding an electrical resistance bank. Amplitude demodulation and frequency demodulation are applied to the current drawn by the induction motor for detecting the rotating shaft frequencies and GMFs, respectively. Discrete wavelet transform is applied to the demodulated current signal for denoising and removing the intervening neighboring features. Spectrum of a particular level, which comprises the GMFs, is used for gear fault detection. Index Terms--Current, demodulation, fault diagnosis, gears, wavelet transforms.

Highlights • Time delay estimated by EMD ML in low SNR and reverberant underwater environment. • In EMD ML method, estimation of impulse response function is been avoided. • Among TDE methods EMD ML is suitable in low signal strength, low frequency source. Abstract In recent times, time delay estimation (TDE) has received significant practical importance in sonar, radar, GPS, and various other fields. In a passive sonar system, estimation of time delay for low frequency and low signal to noise (SNR) acoustic source is a difficult task. If the source and receivers are kept inside the reverberation environment, time delay estimation becomes more difficult because of sound source echo's. This research work proposes a new TDE approach named empirical mode decomposition maximum likelihood time delay estimation (EMD ML TDE) method, for the low-frequency and low SNR underwater machinery acoustic signal in a reverberant environment. EMD ML TDE method is based on maximum likelihood (ML) method, and it is using empirical mode decomposition (EMD) denoising technique to estimate acoustic sound signal and noise from the noisy reverberant signal. The experimental results are provided that this new approach is better to estimate time delay in reverberant environments. [ABSTRACT FROM AUTHOR]

Detection of spark ignition engine firing is an important parameter to understand proper and improper combustion inside the engine cylinder. The engine firing can be detected by direct measurement of in-cylinder pressure by using piezo-electric sensor. However, the cost of the sensor is really high. Therefore, in this paper, engine firing has been detected by analysis of instantaneous angular speed (IAS) signal from output shaft measured by cheap device like rotary encoder. The direct short time Fourier transform (STFT) is unable to detect engine firing clearly as the signal contains various frequencies (rotational frequency and its harmonics) other than engine firing frequency. Therefore, an algorithm has been developed based on complementary ensemble empirical mode decomposition (CEEMD) and followed by STFT and applied on IAS signal to detect engine firing. Moreover, a comparative study has been performed between laser vibrometer signal and IAS signal to understand the efficacy of these signals. [ABSTRACT FROM AUTHOR]

06-0909 Kar, C., and A.R. Mohanty. Multistage gearbox condition monitoring using motor current signature analysis and Kolmogorov-Smirnov test. Journal of Sound and Vibration (United Kingdom) 290(1,2):337-368, Feb. 21, 2006 (www. [...]

Kar, C., and A.R. Mohanty. Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing (United Kingdom) 20(1):158-187, Jan. 2006 (mssp@harcourt.com). KEYWORDS: gearboxes, gears, [...]

This paper describes the interpretations of underwater noise spectra of rainfall on water bodies. It uses a simple methodology of measurement of bubbles of various sizes up to 10 mm in diameter and the comparison of resonating frequencies of the above bubbles with theoretical calculations. The underwater noise of rainfall is a combined effect of drop impacts and bubble bursting. The rain drop size during actual rainfall varies between 0.8 and 5 mm in diameter and the underwater bubble sizes can vary from 0.5 mm to 10 mm in diameter. The detailed description of the experimental results of the underwater spectrum of natural light rain and artificially generated rain are presented. By analyzing the resonating frequencies of underwater bubbles for different type of rainfall, it is plausible to interpret the underwater noise spectrum of rainfall on water bodies and then predict the amount of rainfall. [ABSTRACT FROM AUTHOR]

A number of techniques for detection of faults in rolling element bearing using frequency domain approach exist today. For analysing non-stationary signals arising out of defective rolling element bearings, use of conventional discrete Fourier transform (DFT) has been known to be less efficient. One of the most suited time–frequency approach, wavelet transform (WT) has inherent problems of large computational time and fixed-scale frequency resolution. In view of such constraints, the Hilbert–Huang Transform (HHT) technique provides multi-resolution in various frequency scales and takes the signal''s frequency content and their variation into consideration. HHT analyses the vibration signal using intrinsic mode functions (IMFs), which are extracted using the process of empirical mode decomposition (EMD). However, use of Hilbert transform (HT)-based time domain approach in HHT for analysis of bearing vibration signature leads to scope for subjective error in calculation of characteristic defect frequencies (CDF) of the rolling element bearings. The time resolution significantly affects the calculation of corresponding frequency content of the signal. In the present work, FFT of IMFs from HHT process has been incorporated to utilise efficiency of HT in frequency domain. The comparative analysis presented in this paper indicates the effectiveness of using frequency domain approach in HHT and its efficiency as one of the best-suited techniques for bearing fault diagnosis (BFD). [Copyright &y& Elsevier]

A new signal processing technique; i.e., a corrected multi‐resolution Fourier transform (MFT), is proposed for health monitoring of a multistage gearbox. It is inferred from the study that MFT coefficients of vibration and current signals can predict a consistent trend in the energy level possessed by the gear mesh frequencies with an increase in the severity of defects and, hence, can be useful in gearbox health monitoring.

Faults in a rotor-bearing system due to bearings and unbalance have been classified using support vector machines (SVMs). Vibration signals on a rotor-bearing system were measured simultaneously at five different rotating speeds using seven transducers. The most sensitive feature of the vibration signals has been determined using compensation distance evaluation technique. Multi-class SVMs classification algorithm was then implemented for classification of the faults by considering SVMs created by the possible combinations of the most two sensitive features for each type of fault. By using optimal SVM parameters, the effective location of transducer among seven transducers for best classification of the faults has been investigated and found that any transducer provides a classification of 75% or better and this classification rate increases when more transducers are considered. This paper provides a robust SVM based technique using only time domain data without any additional preprocessing for classifying bearing and unbalance faults. [ABSTRACT FROM AUTHOR]

Abstract: Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations. [Copyright &y& Elsevier]