Search results for: weak signals

Authors: Alexander Kelíšek, Denisa Janasová, Veronika Mitašová

Abstract:

Weak signals using is often associated with early warning. It is possible to find a link between early warning, respectively early problems detection and risk management. The idea of early warning is very important in the context of crisis management because of the risk prevention possibility. Weak signals are likened to risk symptoms. Nowadays, their usefulness as a tool of proactive problems solving is emphasized. Based on it, it is possible to use weak signals not only in strategic planning, project management, or early warning system, but also as a subsidiary element in risk management. The main question is how to effectively integrate weak signals into risk management. The main aim of the paper is to point out the possibilities of weak signals using in small and medium enterprises risk management.

Keywords: early warning system, weak signals, risk management, small and medium enterprises (SMEs)

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Authors: H. Namazi, H. T. N. Kuan

Abstract:

Brain activity can be measured by acquiring and analyzing EEG signals from an individual. In fact, the human brain response to external and internal stimuli is mapped in his EEG signals. During years some methods such as Fourier transform, wavelet transform, empirical mode decomposition, etc. have been used to analyze the EEG signals in order to find the effect of stimuli, especially external stimuli. But each of these methods has some weak points in analysis of EEG signals. For instance, Fourier transform and wavelet transform methods are linear signal analysis methods which are not good to be used for analysis of EEG signals as nonlinear signals. In this research we analyze the brain response to auditory stimuli by extracting information in the form of various measures from EEG signals using a software developed by our research group. The used measures are Jeffrey’s measure, Fractal dimension and Hurst exponent. The results of these analyses are useful not only for fundamental understanding of brain response to auditory stimuli but provide us with very good recommendations for clinical purposes.

Keywords: auditory stimuli, brain response, EEG signal, fractal dimension, hurst exponent, Jeffrey’s measure

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Authors: H. Namazi, H. T. N. Kuan

Abstract:

Brain activity can be measured by acquiring and analyzing EEG signals from an individual. In fact, the human brain response to external and internal stimuli is mapped in his EEG signals. During years some methods such as Fourier transform, wavelet transform, empirical mode decomposition, etc. have been used to analyze the EEG signals in order to find the effect of stimuli, especially external stimuli. But each of these methods has some weak points in analysis of EEG signals. For instance, Fourier transform and wavelet transform methods are linear signal analysis methods which are not good to be used for analysis of EEG signals as nonlinear signals. In this research we analyze the brain response to visual stimuli by extracting information in the form of various measures from EEG signals using a software developed by our research group. The used measures are Jeffrey’s measure, Fractal dimension and Hurst exponent. The results of these analyses are useful not only for fundamental understanding of brain response to visual stimuli but provide us with very good recommendations for clinical purposes.

Keywords: visual stimuli, brain response, EEG signal, fractal dimension, hurst exponent, Jeffrey’s measure

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Authors: Weihao Wang, Zhulin Zong

Abstract:

Two-dimensional ordered statistical constant false alarm detection is a widely used method for detecting weak target signals in radar signal processing applications. The method is based on analyzing the statistical characteristics of the noise and clutter present in the radar signal and then using this information to set an appropriate detection threshold. In this approach, the reference cell of the unit to be detected is divided into several reference subunits. These subunits are used to estimate the noise level and adjust the detection threshold, with the aim of minimizing the false alarm rate. By using an ordered statistical approach, the method is able to effectively suppress the influence of clutter and noise, resulting in a low false alarm rate. The detection process involves a number of steps, including filtering the input radar signal to remove any noise or clutter, estimating the noise level based on the statistical characteristics of the reference subunits, and finally, setting the detection threshold based on the estimated noise level. One of the main advantages of two-dimensional ordered statistical constant false alarm detection is its ability to detect weak target signals in the presence of strong clutter and noise. This is achieved by carefully analyzing the statistical properties of the signal and using an ordered statistical approach to estimate the noise level and adjust the detection threshold. In conclusion, two-dimensional ordered statistical constant false alarm detection is a powerful technique for detecting weak target signals in radar signal processing applications. By dividing the reference cell into several subunits and using an ordered statistical approach to estimate the noise level and adjust the detection threshold, this method is able to effectively suppress the influence of clutter and noise and maintain a low false alarm rate.

Keywords: two-dimensional, ordered statistical, constant false alarm, detection, weak target signals

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Authors: Y. Kumru, K. Enhos, H. Köymen

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In this paper, we report the experimental results on using complementary Golay coded signals at 7.5 MHz to detect breast microcalcifications of 50 µm size. Simulations using complementary Golay coded signals show perfect consistence with the experimental results, confirming the improved signal to noise ratio for complementary Golay coded signals. For improving the success on detecting the microcalcifications, orthogonal complementary Golay sequences having cross-correlation for minimum interference are used as coded signals and compared to tone burst pulse of equal energy in terms of resolution under weak signal conditions. The measurements are conducted using an experimental ultrasound research scanner, Digital Phased Array System (DiPhAS) having 256 channels, a phased array transducer with 7.5 MHz center frequency and the results obtained through experiments are validated by Field-II simulation software. In addition, to investigate the superiority of coded signals in terms of resolution, multipurpose tissue equivalent phantom containing series of monofilament nylon targets, 240 µm in diameter, and cyst-like objects with attenuation of 0.5 dB/[MHz x cm] is used in the experiments. We obtained ultrasound images of monofilament nylon targets for the evaluation of resolution. Simulation and experimental results show that it is possible to differentiate closely positioned small targets with increased success by using coded excitation in very weak signal conditions.

Keywords: coded excitation, complementary golay codes, DiPhAS, medical ultrasound

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Authors: Beldjilali Bilal, Benadda Belkacem, Kahlouche Salem

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Due to the Doppler effect caused by the high velocity of satellite and in some case receivers, the frequency of the Global Positioning System (GPS) signals are transformed into a new ones. Several acquisition algorithms frequency of the Global Positioning System (GPS) signals are transformed can be used to estimate the new frequency and phase shifts values. Numerous algorithms are based on the frequencies domain calculation. Our developed algorithm is a new approach dedicated to the Global Positioning System signal acquisition based on the fast Fourier transform. Our proposed new algorithm is easier to implement and has fast execution time compared with elder ones.

Keywords: global positioning system, acquisition, FFT, GPS/L1, software receiver, weak signal

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Authors: Marsden Jacques, Dennis Wong

Abstract:

A weak order is a way to rank n objects where ties are allowed. In this talk, we present a recursive framework to generate Gray codes for weak orders. We then describe a simple algorithm based on the framework that generates 2-Gray codes for weak orders in constant amortized time per string. This framework can easily be modified to generate other Gray codes for weak orders. We provide an example on using the framework to generate the first Shift Gray code for weak orders, also in constant amortized time, where consecutive strings differ by a shift or a symbol change.

Keywords: weak order, Cayley permutation, Gray code, shift Gray code

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Authors: Manoj Kumar Patel

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In this paper, we introduce and give various properties of weak* Rad-plus-supplemented and cofinitely weak* Rad-plus-supplemented modules over some special kinds of rings, in particular, artinian serial ring and semiperfect ring. Also prove that ring R is artinian serial if and only if every right and left R-module is weak* Rad-plus-supplemented. We provide the counter example which proves that weak* Rad-plus-supplemented module is the generalization of plus-supplemented and Rad-plus-supplemented modules. Furthermore, as an application of above finding results of this research article, our main focus is to characterized the semisimple ring, artinian principal ideal ring, semilocal ring, semiperfect ring, perfect ring, commutative noetherian ring and Dedekind domain in terms of weak* Rad-plus-supplemented module.

Keywords: cofinitely weak* Rad-plus-supplemented module , Dedekind domain, Rad-plus-supplemented module, semiperfect ring

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Authors: Launcelot McGrath

Abstract:

A comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Bio signal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. Understanding which biological signals are most important during anaesthesia is critically important. It is important that the anaesthesiologist understand both the signals themselves and the limitations introduced by the processes of acquisition. In this article, we provide an overview of different types of biological signals as well as the mechanisms applied to acquire them.

Keywords: biological signals, signal acquisition, anaesthesiology, patient monitoring

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Authors: Boukari Nassim

Abstract:

This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

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Authors: Jing Qing-feng, Liu Danmei

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Based on the optimum detection, signal projection and Maximum A Posteriori (MAP) rule, Proakis has deduced the theoretical BER equation of Gray coded MPSK signals. Proakis analyzed the BER theoretical equations mainly based on the projection of signals, which is difficult to be understood. This article solve the same problem based on the signal space, which explains the vectors relations among the sending signals, received signals and noises. The more explicit and easy-deduced process is illustrated in this article based on the signal space, which can illustrated the relations among the signals and noises clearly. This kind of deduction has a univocal geometry meaning. It can explain the correlation between the production and calculation of BER in vector level.

Keywords: MPSK, MAP, signal space, BER

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Authors: Launcelot.McGrath

Abstract:

A comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Biosignal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. Understanding which biological signals are most important during anaesthesia is critically important. It is important that the anaesthesiologist understand both the signals themselves and the limitations introduced by the processes of acquisition. In this article, we provide an overview of different types of biological signals as well as the mechanisms applied to acquire them.

Keywords: general biosignals, anaesthesia, biological, electroencephalogram

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Authors: Launcelot McGrath, Xiaoxiao Liu, Colin Flanagan

Abstract:

It is widely recognised that a comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Bio signal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. There are tremendous biological signals during anaesthesia, and not all of them are important, which to choose to observe is a significant decision. It is important that the anaesthesiologist understand both the signals themselves, and the limitations introduced by the processes of acquisition. In this article, we provide an all-sided overview of different types of biological signals as well as the mechanisms applied to acquire them.

Keywords: general biosignals, anaesthesia, biological, electroencephalogram

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Authors: Nema M. Salem, Hanan A. Altukhaifi, Amal Mukhtar, Reemaz K. Hetaimish

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Recently, BCI field of research has gained a lot of interest. Apart from motor neuroprosthetics, many studies showed the possibility of controlling a virtual environment of a videogame using the acquired electroencephalogram signals (EEG) from the gamer. In addition, another study had successfully moved a farm tractor using the human’s EEG signals. This article utilizes the use of EEG signals, as a source of technology, in controlling a Parallax Boe-Bot robot. The commercial Emotive Epoc headset has been used in acquiring the EEG signals from rested subjects. Because the human's visual cortex can successfully differentiate between different colors, the red and green colors are used as visual stimuli for generating EEG signals using the Epoc. Arduino and Labview are used to translate the virtually pressed keys into instructions controlling the motion and rotation of the robot. Optimistic results have been achieved except for minor delay and accuracy in the robot’s response.

Keywords: BCI, Emotiv Epoc headset, EEG, Labview, Arduino applications, robot

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Authors: Maciej Zaręba, Sławomir Lasota

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Understanding the dependencies between the input signal, that controls the production system and signals, that capture its output, is of a great importance in intelligent systems. The method for identification of the relationship between signals in continuous monitoring of production systems is described in the paper. The method discovers the correlation between changes in the states derived from input signals and resulting changes in the states of output signals of the production system. The method is able to handle system inertia, which determines the time shift of the relationship between the input and output.

Keywords: manufacturing operation management, signal relationship, continuous monitoring, production systems

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Authors: Valentina Narvaez Gaitan, Laura Valentina Rodriguez Leguizamon, Ruben Dario Hernandez B.

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This work describes a physiological signal emulation system that uses electromyography (EMG) signals obtained from muscle sensors in the first instance. These signals are used to extract their characteristics to model and emulate specific arm movements. The main objective of this effort is to develop a new biomedical software system capable of generating physiological signals through the use of embedded systems by establishing the characteristics of the acquired signals. The acquisition system used was Biosignals, which contains two EMG electrodes used to acquire signals from the forearm muscles placed on the extensor and flexor muscles. Processing algorithms were implemented to classify the signals generated by the arm muscles when performing specific movements such as wrist flexion extension, palmar grip, and wrist pronation-supination. Matlab software was used to condition and preprocess the signals for subsequent classification. Subsequently, the mathematical modeling of each signal is performed to be generated by the embedded system, with a validation of the accuracy of the obtained signal using the percentage of cross-correlation, obtaining a precision of 96%. The equations are then discretized to be emulated in the embedded system, obtaining a system capable of generating physiological signals according to the characteristics of medical analysis.

Keywords: classification, electromyography, embedded system, emulation, physiological signals

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: R. Biondi, G. Dys, G. Ferone, T. Renard, M. Zysman

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This paper describes an automated implementable system for impulsive signals detection and recognition. The system uses a Digital Signal Processing device for the detection and identification process. Here the system analyses the signals in real time in order to produce a particular response if needed. The system analyses the signals in real time in order to produce a specific output if needed. Detection is achieved through normalizing the inputs and comparing the read signals to a dynamic threshold and thus avoiding detections linked to loud or fluctuating environing noise. Identification is done through neuronal network algorithms. As a setup our system can receive signals to “learn” certain patterns. Through “learning” the system can recognize signals faster, inducing flexibility to new patterns similar to those known. Sound is captured through a simple jack input, and could be changed for an enhanced recording surface such as a wide-area recorder. Furthermore a communication module can be added to the apparatus to send alerts to another interface if needed.

Keywords: sound detection, impulsive signal, background noise, neural network

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Authors: Hassan Youssef Mohamed

Abstract:

In this paper, we show new wave equations, and by using the equations, we concluded that the strong force and the weak force are not fundamental, but they are quantum effects for electromagnetism. This result is different from the current scientific understanding about strong and weak interactions at all. So, we introduce three evidences for our theory. First, we prove the asymptotic freedom phenomenon in the strong force by using our model. Second, we derive the nuclear shell model as an approximation of our model. Third, we prove that the leptons do not participate in the strong interactions, and we prove the short ranges of weak and strong interactions. So, our model is consistent with the current understanding of physics. Finally, we introduce the electron-positron model as the basic ingredients for protons, neutrons, and all matters, so we can study all particles interactions and nuclear interaction as many-body problems of electrons and positrons. Also, we prove the violation of parity conservation in weak interaction as evidence of our theory in the weak interaction. Also, we calculate the average of the binding energy per nucleon.

Keywords: new wave equations, the strong force, the grand unification theory, hydrogen atom, weak force, the nuclear shell model, the asymptotic freedom, electron-positron model, the violation of parity conservation, the binding energy

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Authors: Mai Yen Nhi Doan, Viet Le, Chamindika Weerakoon

Abstract:

Social enterprises have emerged as sustainable organisations that deliver social achievement along with long-term financial advancement. However, recorded financial barriers have urged social enterprises to divert to other financing methods due to the misaligned ideology with traditional financing capitalists, in which crowdfunding can be a promising alternative. Previous studies in crowdfunding have inadequately addressed crowdfunding for social enterprises, with conflicting results due to the unsuitable analysis of signals in isolation rather than in combinations, using the data from platforms that do not support social enterprises. Extending the signalling theory, this study suggests that crowdfunding success results from the collaboration between costly and costless signals. The proposed conceptual framework enlightens the interaction between costly signals as “organisational information”, “social entrepreneur’s credibility,” and “third-party endorsement” and costless signals as various sub-signals under the “campaign preparedness” signal to achieve crowdfunding success. Using Qualitative Comparative Analysis, this study examined 45 crowdfunding campaigns run by Australian social enterprises on StartSomeGood and Chuffed. The analysis found that different combinations of costly and costless signals can lead to crowdfunding success, allowing social enterprises to adopt suitable combinations of signals to their context. Costless signal – campaign preparedness is fundamental for success, though different costless sub-signals under campaign preparedness can interact with different costly signals for the desired outcome. Third-party endorsement signal was found to be the necessary signal for crowdfunding success for Australian social enterprises.

Keywords: crowdfunding, qualitative comparative analysis (QCA), signalling theory, social enterprises

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Authors: Shuenn-Yih Chang, Chiu-Li Huang

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Three structure-dependent integration methods have been developed for solving equations of motion, which are second-order ordinary differential equations, for structural dynamics and earthquake engineering applications. Although they generally have the same numerical properties, such as explicit formulation, unconditional stability and second-order accuracy, a different performance is found in solving the free vibration response to either linear elastic or nonlinear systems with high frequency modes. The root cause of this different performance in the free vibration responses is analytically explored herein. As a result, it is verified that a weak instability is responsible for the different performance of the integration methods. In general, a weak instability will result in an inaccurate solution or even numerical instability in the free vibration responses of high frequency modes. As a result, a weak instability must be prohibited for time integration methods.

Keywords: dynamic analysis, high frequency, integration method, overshoot, weak instability

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Authors: Rodrigo Montufar-Chaveznava, Fernando Brambila-Paz, Ivette Caldelas

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In this paper, we present the advances corresponding to the denoising processing of magnetotelluric signals using several filters. In particular, we use the most common spatial domain filters such as median and mean, but we are also using the Fourier and wavelet transform for frequency domain filtering. We employ three datasets obtained at the different sampling rate (128, 4096 and 8192 bps) and evaluate the mean square error, signal-to-noise relation, and peak signal-to-noise relation to compare the kernels and determine the most suitable for each case. The magnetotelluric signals correspond to earth exploration when water is searched. The object is to find a denoising strategy different to the one included in the commercial equipment that is employed in this task.

Keywords: denoising, filtering, magnetotelluric signals, wavelet transform

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Authors: J. Jena, Monica Saxena

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In this paper, the propagation of weak nonlinear waves in non-equilibrium flow has been studied in detail using the perturbation method. The expansive action of receding piston undergoing infinite acceleration has been discussed. Central expansion fan, compression waves and shock fronts have been discussed and the solutions up to the first order in the characteristic plane and physical plane have been obtained.

Keywords: Characteristic wave front, weak non-linear waves, central expansion fan, compression waves

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Authors: Kapila Warnakulasuriya, Walimuni Janaka Mendis

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In this study, an artificial intelligence-based approach is developed to diagnose abnormal tissues in human or animal bodies by analyzing magnetic resonance signals. As opposed to the conventional method of generating an image from the magnetic resonance signals, which are then evaluated by a radiologist for the diagnosis of abnormalities, in the discussed approach, the magnetic resonance signals are analyzed by an artificial intelligence algorithm without having to generate or analyze an image. The AI-based program compares magnetic resonance signals with millions of possible magnetic resonance waveforms which can be generated from various types of normal tissues. Waveforms generated by abnormal tissues are then identified, and images of the abnormal tissues are generated with the possible location of them in the body for further diagnostic tests.

Keywords: magnetic resonance, artificial intelligence, magnetic waveform analysis, abnormal tissues

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Authors: Maciej Zaręba, Sławomir Lasota

Abstract:

Knowing the degree of dependencies between the sets of input signals and selected sets of indicators that measure a production system's effectiveness is of great importance in the industry. This paper introduces the SELM method that enables the selection of sets of input signals, which affects the most the selected subset of indicators that measures the effectiveness of a production system. For defined set of output indicators, the method quantifies the impact of input signals that are gathered in the continuous monitoring production system.

Keywords: manufacturing operation management, signal relationship, continuous monitoring, production systems

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

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Authors: Mauricio Quintero Angel, Andrés A. Duque Nivia, Carlos H. Fajardo Toro

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This paper analyzes two approaches of sustainability, the weak and strong, considering a case of study of oil palm production for an industry of biodegradable detergent. In this case, a company demand the oil palm as the active element for washing and through its trademark aims to supply 10% of the Colombian market of washing powders. Under each approach the economic and ecological implications of the palm oil production and especially the implications for crop management are described. The crop production under the weak sustainability implies plantations, intensive use of agrochemicals and the inclusion of new areas of cultivation as the market grows. Under the strong sustainability the production system is limited by the productive vocation of the ecosystem, so that new approaches and creativity for making viable the nature conservancy and the business development are require.

Keywords: agriculture, environmental impacts, oil palm, strong sustainability, weak sustainability

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Authors: Syed Irfan Sohail, Syed Muzahir Abbas

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This paper presents theoretical transmission response analysis of RF/Microwave signals propagating through a single- and multi-layer float glass panels. These signals are GSM cellular/mobile, GPS, PCS, VHF/UHF and mobile broadband signals that lie in 0-2 GHz range. In theoretical analysis and the experiments conducted, significant transmission response with minimum attenuation was found in all the panels for those signals. Through detailed parametric study, it was further observed that these panels can be further optimized to transmit the desired frequency signals while blocking the others. Moreover, with number of practical investigations, it was also found that the pass-band ripples, cut-off frequency and in-band attenuation of different transmission bands can be determined by the number of panels used and the gap kept between those panels. This allows the designers to optimize the outdoor glass-window-panels to meet some specific wireless cellular and mobile communication needs. Presented here are the predicted and measured results of a single-, dual- and a triple-layer float glass panel with a detailed discussion on the propagation phenomenon of RF/Microwave signals for each of the three float glass-panel.

Keywords: cutt-off, energy-saving glass, frequency, microwave, pass-band, RF

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Authors: A. Nabhan, Mohamed R. El-Sharkawy, A. Rashed

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The main aim of this paper is to dedicate the belt drive system faults like cogs missing, misalignment and belt worm using vibration analysis technique. Experimentally, the belt drive test-rig is equipped to measure vibrations signals under different operating conditions. Finite element 3D model of belt drive system is created and vibration response analyzed using commercial finite element software ABAQUS/CAE.  Root mean square (RMS) and Crest Factor will serve as indicators of average amplitude of envelope analysis signals. The vibration signals pattern obtained from the simulation model and experimental data have the same characteristics. It can be concluded that each case of the RMS is more effective in detecting the defect for acceleration response. While Crest Factor parameter has a response with the displacement and velocity of vibration signals. Also it can be noticed that the model has difficulty in completing the solution when the misalignment angle is higher than 1 degree.

Keywords: simulation model, misalignment, cogs missing, vibration analysis

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Authors: O. Ikpotokin

Abstract:

In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables.

Keywords: bootstrap control limit, p-value method, out-of-control signals, p-value, quality characteristics

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