706 research outputs found

    Signature analysis of mechanical watch movements.

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    Su, Shuang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 100-106).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Objective --- p.2Chapter 1.3 --- Methodology --- p.3Chapter Chapter 2 --- Literature Survey --- p.5Chapter 2.1 --- The Escapement --- p.5Chapter 2.2 --- Signature Analysis of Mechanical Watches -- Traditional Methods and Existing Systems --- p.10Chapter 2.2.1 --- Estimating Rate Deviation --- p.10Chapter 2.2.2 --- Measuring Beat Error --- p.11Chapter 2.2.3 --- Error Detection with a Graphical Diagram --- p.12Chapter 2.2.4 --- Analyzing Watch Ticks --- p.13Chapter 2.3 --- Time-Frequency Distributions and Reassignment --- p.14Chapter 2.3.1 --- Time-Frequency Distributions --- p.14Chapter 2.3.2 --- Reassignment Method --- p.18Chapter 2.4 --- Finite Element Analysis --- p.19Chapter Chapter 3 --- Signature Analysis of Mechanical Watch Movement --- p.21Chapter 3.1 --- Time-Domain Analysis: Endpoint Detection --- p.21Chapter 3.2 --- Time-Domain Analysis: Error Detection with a Graphical Chart --- p.27Chapter 3.3 --- Analyzing Ticks: from Time-Domain Analysis to Time-Frequency Analysis --- p.31Chapter Chapter 4 --- Reassigned Time-Frequency Distributions --- p.34Chapter 4.1 --- Spectrogram --- p.34Chapter 4.2 --- Morlet Scalogram --- p.35Chapter 4.3 --- Smoothed Pseudo-Wigner-Ville Distribution --- p.36Chapter 4.4 --- Reassignment principle --- p.37Chapter 4.5 --- Reassigned Spectrogram (RSP) --- p.39Chapter 4.6 --- Reassigned Morlet Scalogram --- p.40Chapter 4.7 --- Reassigned SPWV --- p.40Chapter 4.8 --- Performance Evaluation of Time-frequency Distributions --- p.41Chapter Chapter 5 --- Modal analysis and simulation results --- p.47Chapter 5.1 --- FEA Eigensystems --- p.47Chapter 5.2 --- Modal Analysis in ANSYS --- p.48Chapter 5.3 --- Transient Dynamic Analysis of Watch Parts in ANSYS --- p.50Chapter Chapter 6 --- Fault Detection Examples --- p.60Chapter 6.1 --- Example I --- p.60Chapter 6.2 --- Example II --- p.64Chapter Chapter 7 --- System Development --- p.69Chapter Chapter 8 --- Conclusions --- p.74Appendix I --- p.77Chapter 1. --- GUI Layout of the CUHK-IPE Watch Signature Analyzer (WTimer.fig) : --- p.77Chapter 2. --- Main Function of CUHK-IPE Watch Signature Analyzer (WTimer.m): --- p.78Chapter 3. --- Other Functions Called by the Main Function: --- p.85Chapter 3.1 --- Function for Split Signal up into (Overlapping) Frames (enframe.m):…… --- p.86Chapter 3.2 --- Function for Detecting BPH of the Watch (bph´ؤdetection.m): --- p.86Chapter 3.3 --- Function for Calculation the Rate Deviation and Beat Error of the Watch (rate4_6.m): --- p.89Chapter 3.4 --- Function for Calculating the RSP of the Signal (tfrrsp.m): --- p.95Chapter 3.5 --- Window Generation Function (tftb_window.m): --- p.97References --- p.10

    HRV and ECG signal analysis of smokers and non-smokers

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    The current study deals with the study of Heart Rate Variability and wavelet-based ECG signal analysis of thirty-two volunteers, who were divided into two groups of smokers and non-smokers. Although the preliminary results of frequency domain analysis of HRV showed some dominance towards the sympathetic nervous system activity in smokers, they were not found to be statistically significant. Hence the bias of results towards the increase of sympathetic activity might be attributed to the masking affect of some other factors, apart from smoking, which were not included in our experiment. The wavelet decomposition of the ECG signal was done using the Daubechies (Db 6) wavelet family. No significant difference was observed between the smokers and non-smokers which apparently suggested that HRV does not affect the conduction pathway of heart

    Exploration of the possibility of acoustic emission technique in detection and diagnosis of bubble formation and collapse in valves

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    The application of acoustic emission (AE) technique in detection and monitoring of bubble formation and collapse in valves are presented in this review. The generation of AE signals and the basic compositions of AE detection system are briefly explained. The applications of AE technique in valves are focused on condition monitoring and detection bubble formation (bubble cavitation), and leakage of water through valves. All results prove that the AE technique works well for detection and diagnosis of failures during valves

    A data analytics approach to gas turbine prognostics and health management

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    As a consequence of the recent deregulation in the electrical power production industry, there has been a shift in the traditional ownership of power plants and the way they are operated. To hedge their business risks, the many new private entrepreneurs enter into long-term service agreement (LTSA) with third parties for their operation and maintenance activities. As the major LTSA providers, original equipment manufacturers have invested huge amounts of money to develop preventive maintenance strategies to minimize the occurrence of costly unplanned outages resulting from failures of the equipments covered under LTSA contracts. As a matter of fact, a recent study by the Electric Power Research Institute estimates the cost benefit of preventing a failure of a General Electric 7FA or 9FA technology compressor at 10to10 to 20 million. Therefore, in this dissertation, a two-phase data analytics approach is proposed to use the existing monitoring gas path and vibration sensors data to first develop a proactive strategy that systematically detects and validates catastrophic failure precursors so as to avoid the failure; and secondly to estimate the residual time to failure of the unhealthy items. For the first part of this work, the time-frequency technique of the wavelet packet transforms is used to de-noise the noisy sensor data. Next, the time-series signal of each sensor is decomposed to perform a multi-resolution analysis to extract its features. After that, the probabilistic principal component analysis is applied as a data fusion technique to reduce the number of the potentially correlated multi-sensors measurement into a few uncorrelated principal components. The last step of the failure precursor detection methodology, the anomaly detection decision, is in itself a multi-stage process. The obtained principal components from the data fusion step are first combined into a one-dimensional reconstructed signal representing the overall health assessment of the monitored systems. Then, two damage indicators of the reconstructed signal are defined and monitored for defect using a statistical process control approach. Finally, the Bayesian evaluation method for hypothesis testing is applied to a computed threshold to test for deviations from the healthy band. To model the residual time to failure, the anomaly severity index and the anomaly duration index are defined as defects characteristics. Two modeling techniques are investigated for the prognostication of the survival time after an anomaly is detected: the deterministic regression approach, and parametric approximation of the non-parametric Kaplan-Meier plot estimator. It is established that the deterministic regression provides poor prediction estimation. The non parametric survival data analysis technique of the Kaplan-Meier estimator provides the empirical survivor function of the data set comprised of both non-censored and right censored data. Though powerful because no a-priori predefined lifetime distribution is made, the Kaplan-Meier result lacks the flexibility to be transplanted to other units of a given fleet. The parametric analysis of survival data is performed with two popular failure analysis distributions: the exponential distribution and the Weibull distribution. The conclusion from the parametric analysis of the Kaplan-Meier plot is that the larger the data set, the more accurate is the prognostication ability of the residual time to failure model.PhDCommittee Chair: Mavris, Dimitri; Committee Member: Jiang, Xiaomo; Committee Member: Kumar, Virendra; Committee Member: Saleh, Joseph; Committee Member: Vittal, Sameer; Committee Member: Volovoi, Vital

    Diagnosis of Bearing Fault Using Morphological Features Extraction and Entropy Deconvolution Method

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    It is observed that the bearing failure of rotating machinery is a pulse in the vibration signal, but it is mostly immersed in noise. In order to effectively eliminate this noise and detect pulses, a novel an image fusion technology based on morphological operators inference is proposed. The correctness of morphological operators lies in the correct selection of structural elements (SE). This report presents an effective algorithm for SE selection based on kurtosis, which makes the analysis free empirical method. When analyzing three different groups of faults, the results show that this method effectively and robustly generates impulse. It enables the algorithm to detect early faults too. Recently, minimum entropy deconvolution (MED) was introduced to the machine in the field of condition monitoring, to enhance the detection of rolling bearing and gear failures. MED analysis helps to extract these pulses and diagnose their source, namely defects bearing components. In this research, MED will be reviewed and reintroduced, Application in fault detection and diagnosis of rolling bearings. MED parameters are selected and its combination with pre-whitening. Test cases are presented to illustrate benefits of MED technology. The simulation has been done on MATLAB and a graphical user interface has been created for analysis of bearing and detection of bearing faults using morphological features

    Signal Processing of Electroencephalogram for the Detection of Attentiveness towards Short Training Videos

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    This research has developed a novel method which uses an easy to deploy single dry electrode wireless electroencephalogram (EEG) collection device as an input to an automated system that measures indicators of a participant’s attentiveness while they are watching a short training video. The results are promising, including 85% or better accuracy in identifying whether a participant is watching a segment of video from a boring scene or lecture, versus a segment of video from an attentiveness inducing active lesson or memory quiz. In addition, the final system produces an ensemble average of attentiveness across many participants, pinpointing areas in the training videos that induce peak attentiveness. Qualitative analysis of the results of this research is also very promising. The system produces attentiveness graphs for individual participants and these triangulate well with the thoughts and feelings those participants had during different parts of the videos, as described in their own words. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. This research was motivated by this interest, as well as recent advances in electronic and computer engineering’s use of biometric signal analysis for the detection of affective (emotional) response. Signal processing of EEG has proven useful in measuring alertness, emotional state, and even towards very specific applications such as whether or not participants will recall television commercials days after they have seen them. This research extended these advances by creating an automated system which measures attentiveness towards short training videos. The bulk of the research was focused on electrical and computer engineering, specifically the optimization of signal processing algorithms for this particular application. A review of existing methods of EEG signal processing and feature extraction methods shows that there is a common subdivision of the steps that are used in different EEG applications. These steps include hardware sensing filtering and digitizing, noise removal, chopping the continuous EEG data into windows for processing, normalization, transformation to extract frequency or scale information, treatment of phase or shift information, and additional post-transformation noise reduction techniques. A large degree of variation exists in most of these steps within the currently documented state of the art. This research connected these varied methods into a single holistic model that allows for comparison and selection of optimal algorithms for this application. The research described herein provided for such a structured and orderly comparison of individual signal analysis and feature extraction methods. This study created a concise algorithmic approach in examining all the aforementioned steps. In doing so, the study provided the framework for a systematic approach which followed a rigorous participant cross validation so that options could be tested, compared and optimized. Novel signal analysis methods were also developed, using new techniques to choose parameters, which greatly improved performance. The research also utilizes machine learning to automatically categorize extracted features into measures of attentiveness. The research improved existing machine learning with novel methods, including a method of using per-participant baselines with kNN machine learning. This provided an optimal solution to extend current EEG signal analysis methods that were used in other applications, and refined them for use in the measurement of attentiveness towards short training videos. These algorithms are proven to be best via selection of optimal signal analysis and optimal machine learning steps identified through both n-fold and participant cross validation. The creation of this new system which uses signal processing of EEG for the detection of attentiveness towards short training videos has created a significant advance in the field of attentiveness measuring towards short training videos

    Technological form defects identification using discrete cosine transform method

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    12 pagesAbstract Manufacturers need precise tools to simulate, validate, or improve a process plan for given tolerances. Some simulation methods calculating position and orientation defect of manufactured surfaces have already been developed. A lack in these methods is the integration of form defect of surfaces. Indeed, many methods do not study manufactured surfaces, but nominal models associated to these surfaces. The method developed in this article proposes a tool describing precisely form error in order to take it into account. The work is based on a method of the literature, using discrete cosine transformation, completed by a method for identification of classical defects composing global form error and quantification of their contribution to this defect. The method is validated on simulation examples and then applied on a milled plane
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