Sandeep Kumar Yadav

A Compact Koch Fractal UWB MIMO Antenna With WLAN Band-Rejection

In this letter, a compact octagonal shaped fractal ultrawideband multiple-input-multiple-output antenna is presented, and its characteristics are investigated. In order to achieve the desired miniaturization and wideband phenomena, self-similar and space filling properties of Koch fractal geometry are used in the antenna design. These fractal monopoles are placed orthogonal to each other for good isolation. Moreover, grounded stubs are used in the geometry to provide further improvement in the isolation. The band rejection phenomenon in wireless local area network band is achieved by etching a C-shaped slot from the monopole of the antenna. The proposed antenna has compact dimensions of 45 mm × 45 mm and exhibits quasi-omnidirectional radiation pattern. In addition, it shows an impedance bandwidth (S11 <; -10 dB ) from 2 to 10.6 GHz with isolation better than 17 dB over the entire ultra-wideband range. Diversity performance is also evaluated in terms of envelope correlation coefficient and capacity loss. The measured results show good agreement with the simulated ones.

Audio Signature-Based Condition Monitoring of Internal Combustion Engine Using FFT and Correlation Approach

This work proposes a novel prototype-based engine fault classification scheme employing the audio signature of engines. In this scheme, Fourier transform and correlation methods have been used. Notably, automated audio classification has immense significance in the present times, used in both audio-based content retrieval and audio indexing in multimedia industry. Likewise, it is also becoming increasingly important in automobile industries. It has been observed that real world automobile engine audio data are contaminated with substantial noise and out fliers. Hence, it is challenging to categorize different fault types in different engines. Accordingly, the present paper discusses a methodology where a set of algorithms checks the state of an unknown engine as either healthy or faulty. Fault categorizing algorithm is based on its cross- and autocorrelation coefficient values. Appropriately, in this study, the engine amplitude-frequency values of fast Fourier transform are calculated and subdivided into bands to calculate the correlation coefficient matrix. The correlation coefficient matrix for the unknown engine is then calculated and matched with this “prototype” engine matrix to categorize it into a single or multiple fault(s). It is worth mentioning here that although a rank-based maximum close scheme is adopted for finding the unknown engines fault, the work can be extended to any other parametric and neural network-based classification scheme. Keeping this background in mind, the present paper discusses the proposed methodology to find a prototype engine, unknown engine classification, implementation on real audio signal for single cylinder engine data, and its results.

Condition Monitoring of Internal Combustion Engine Using EMD and HMM

The acoustic signature of an internal combustion (IC) engine contains valuable information regarding the functioning of its components. It could be used to detect the incipient faults in the engine. Acoustics-based condition monitoring of systems precisely tries to handle the questions and in the process extracts the relevant information from the acoustic signal to identify the health of the system. In automobile industry, fault diagnosis of engines is generally done by a set of skilled workers who by merely listening to the sound produced by the engine, certify whether the engine is good or bad, primary owing to their excellent sensory skills and cognitive capabilities. It would indeed be a challenging task to mimic the capabilities of those individuals in a machine. In the fault diagnosis setup developed hereby, the acoustic signal emanated from the engine is first captured and recorded; subsequently the acoustic signal is transformed on to a domain where distinct patterns corresponding to the faults being investigated are visible. Traditionally, acoustic signals are mainly analyzed with spectral analysis, i.e., the Fourier transform, which is not a proper tool for the analysis of IC engine acoustic signals, as they are non-stationary and consist of many transient components. In the present work, Empirical Mode Decomposition (EMD) and Hidden Markov Model (HMM)- based approach for IC engine is proposed. EMD is a new time-frequency analyzing method for nonlinear and non-stationary signals. By using the EMD, a complicated signal can be decomposed into a number of intrinsic mode functions (IMFs) based on the local characteristics time scale of the signal. Treating these IMFs as feature vectors HMM is applied to classify the IC engine acoustic signal. Experimental results show that the proposed method can be used as a tool in intelligent autonomous system for condition monitoring and fault diagnosis of IC engine.

Fault diagnosis of internal combustion engine using Empirical Mode Decomposition

In this paper, a signal decomposition and feature extraction technique for the fault diagnosis of internal combustion engine, based on Empirical Mode Decomposition (EMD) is presented. Vibration signal measured from a faulty engine is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components embedded in the vibration signal. However, in our application we found that all the IMFs are not useful to reveal the vibration signal characteristics due to the effect of noise. Hence cumulative mode function (CMF) is presented. With CMF, the most representative IMFs are combined to obtain an oscillation mode representing signal features more accurately. Two criteria, the energy measure and correlation measure, are investigated to determine the most representative IMFs for extracting fault induced characteristics features out of vibration signal. Statistical parameters, shape factor, crest factor, etc., of the envelope spectrum of CMF are investigated as an indicator for the presence of the fault.

Transmission line protection scheme using Wavelet based alienation coefficients

This paper presents a Wavelet based alienation technique to detect and classify various faults on transmission line. The proposed scheme analyses the absolute values of three phase current signals over a half cycle to obtain detail coefficients. These detail coefficients of half a cycle are compared with those of previous half cycle to compute alienation coefficients which are further utilized to detect and classify the faults. The proposed technique was able to discriminate non-fault transients such as capacitance, inductance and load switching, from fault transients. The increase in the sensitivity of protection scheme, due to utilization of wavelet based detail decomposition, has been established by case studies. The proposed algorithm is tested for different locations and various types of faults. The algorithm is proved to be successful in detecting and classifying various types of faults in a half cycle.

Condition monitoring of Internal Combustion engines using empirical mode decomposition and Morlet wavelet

The process of detecting deterioration in the performance of any system termed as condition monitoring and fault diagnosis is at the heart of the condition monitoring procedure. Use of acoustic signatures of Internal Combustion (IC) engines for the condition monitoring procedure is the basic motivation of this paper. Acoustic signatures of IC engines always carry relevant information. However, in many cases, these acoustic signatures might be corrupted by the surrounding noise resulting in a low signal-to-noise-ratio (SNR). Extracting features from the signals having low SNR becomes highly difficult. Therefore, those signals corrupted by noise should be preprocessed before extracting features from them. In this paper, a denoising method based on empirical mode decomposition (EMD) and Morlet wavelet is presented. This denoising method is an advanced version of ldquosoft thresholding denoising methodrdquo proposed by Donoho and Johnstone and ldquogeneralized soft thresholding methodrdquo proposed by Jing Lin. Morlet wavelet based denoising eliminates the noise and improves the SNR significantly and Back Propagation (BP) is used further for classification of faulty and healthy IC engines. Results obtained by using these techniques for condition monitoring of IC engines are promising.

PCA and feature correlation for fault detection and classification

A unique technique is proposed using Principal Component Analysis (PCA) and softmax regression for automated fault detection and classification using vibration and acoustic signals generated from the IC engines. This technique uses the PCA for feature extraction and dimensionality reduction of the frequency spectrum data of noisy acoustic or vibration signals. These feature vectors are then used to get correlation coefficients of training, and testing data. These correlation coefficient vectors are used by soft-max regression based classifier for classification of the engine into different classes. The proposed technique does not require any hand-engineered feature extraction, as usually done and no pre-filtering is required on noisy industrial data. The proposed technique is independently tested on two different types of data sets from the simulator and industrial environment. It has the performance of more than 98% on vibration data from mechanical fault simulator for five different types of faults. The performance of this technique for acoustic data from industrial IC engine is more than 99% for five different fault classes. In a typical case of industrial IC engines, for 216 test data sets, the classification performance is 99.54% with only 72 training data sets.

Hexagonal shaped fractal UWB antenna

We will discuss a small novel hexagonal shaped geometry for ultra wideband (UWB) monopole antenna with notch characteristics. The self-similar nature of this fractal like geometry provides higher effective antenna length and wide operating bandwidth because of the onset of multiple resonances. Notch characteristics are introduced using a rectangular slot in the ground plane, which enhances the reflection coefficient over the entire UWB frequency operating range for the proposed device. The optimized 19 mm × 15 mm dimensions, for the proposed antenna are the smallest among such geometries, exhibiting a large bandwidth from 3.1 GHz to 11.7 GHz with VSWR <; 2 over the entire frequency range. The proposed antenna demonstrates nearly omnidirectional radiation pattern, proper impedance matching, and good return loss over the entire UWB frequency range.

Fault Diagnosis of Internal Combustion Engine Using Empirical Mode Decomposition and Artificial Neural Networks

In this paper, a signal decomposition and feature extraction technique for the fault diagnosis of internal combustion engine, based on empirical mode decomposition (EMD) is presented. Vibration signal measured from a faulty engine is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components embedded in the vibration signal. However, in our application we found that all the IMFs are not useful to reveal the vibration signal characteristics due to the effect of noise. Hence cumulative mode function (CMF) is presented. With CMF, the most representative IMFs are combined to obtain an oscillation mode representing signal features more accurately. Two criteria, the energy measure and correlation measure, are investigated to determine the most representative IMFs for extracting fault induced characteristics features out of vibration signal. Statistical parameters, shape factor, crest factor, etc., of the envelope spectrum of CMF are investigated as an indicator for the presence of the fault.

Modulation scheme detection of blind signal using constellation graphical representation

Modulation scheme detection of blind signal is vital area of research now a days because of the requirement in security and communication applications. In this paper, modulation class is identified based on the constellation graphical representation. Carrier frequency, symbol rate and phase offset are the essential parameters required for the extraction of constellation diagram, which are calculated efficiently. Efficient way of Classification between ASK, PSK and QAM is proposed in this paper. ASK and pool of PSK and QAM is separated using linear regression, and further classification between PSK and QAM has done using circle fitting.