Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery

At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data.National Natural Science Foundation of China, under Grant 51605406National Natural Science Foundation of China under Grant 7180104

Signal processing methods for defect detection in multi-wire helical waveguides using ultrasonic guided waves

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University LondonNon-Destructive Testing of industrial components carries vital importance, both financially and safety-wise. Among all Non-Destructive techniques, Long Range Ultrasonic Testing utilizing the guided wave phenomena is a young technology proven to be commercially valid. Owing to its well-documented analytical models, Ultrasonic Guided Waves has been successfully applied to cylindrical and plate-like structures. Its applications to complex structures such as multi-wire cables are fairly immature, mainly due to the high complexity of wave propagation. Research performed by the author approaches the long range inspection of overhead transmission line cables using ultrasonic guided waves. Existing studies focusing on guided wave application on power cables are extremely limited in inspection range, which dramatically degrades its chances of commercialization. This thesis consists of three main chapters, all of which approaches different problems associated with the inspection of power cables. In the first chapter, a thorough analysis of wave propagation in ACSR (most widely used power cable) cables is conducted. It is shown that high frequency guided waves, by concentrating the energy on the surface layers, can travel much further in the form of fundamental longitudinal wave mode, than previous studies have shown. Defect detection studies proved the system’s capability of detecting defects which introduce either increase or decrease in cross sectional area of the cable. Results of the chapter indicate the detectability of defects as small as 4.5% of the cross sectional area through a 26.5 meter long cable without any post-processing. In the second chapter, several algorithms are proposed to increase the inspection range and signal quality. Well-documented wavelet-denoising algorithm is optimized for power cables and up to 24% signal-to-noise ratio improvement is achieved. By introducing an attenuation correction framework, a theoretical inspection range of 75 meters is presented. A new framework combining dispersion compensation and attenuation correction is proposed and verified, which shows an inspection range of 130 meters and SNR improvement up to 8 dBs. Last chapter addresses the accurate localization of structural defects. Having proven the optimum excitation and related wave propagation in ACSR cables, a system having a more complex wave propagation characteristics is studied. A new algorithm combining pulse compression using Maximal Length Sequences and dispersion compensation is applied to multi-modal signals obtained from a solid aluminum rod. The algorithm proved to be able to improve signal quality and extract an accurate location for defects. Maximal Length Sequences are compared to chirp signals in terms of SNR improvement and localization, which produced favourable results for MLS in terms of localization and for chirp in terms of SNR improvement

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences

Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products

Computing Instantaneous Frequency by normalizing Hilbert Transform

This invention presents Normalized Amplitude Hilbert Transform (NAHT) and Normalized Hilbert Transform(NHT), both of which are new methods for computing Instantaneous Frequency. This method is designed specifically to circumvent the limitation set by the Bedorsian and Nuttal Theorems, and to provide a sharp local measure of error when the quadrature and the Hilbert Transform do not agree. Motivation for this method is that straightforward application of the Hilbert Transform followed by taking the derivative of the phase-angle as the Instantaneous Frequency (IF) leads to a common mistake made up to this date. In order to make the Hilbert Transform method work, the data has to obey certain restrictions

A psychoacoustic "NofM"-type speech coding strategy for cochlear implants

We describe a new signal processing technique for cochlear implants using a psychoacoustic-masking model. The technique is based on the principle of a so-called "NofM" strategy. These strategies stimulate fewer channels (N) per cycle than active electrodes (NofM; N < M). In "NofM" strategies such as ACE or SPEAK, only the N channels with higher amplitudes are stimulated. The new strategy is based on the ACE strategy but uses a psychoacoustic-masking model in order to determine the essential components of any given audio signal. This new strategy was tested on device users in an acute Study, with either 4 or 8 channels stimulated per cycle. For the first condition (4 channels), the mean improvement over the ACE strategy was 17%. For the second condition (8 channels), no significant difference was found between the two strategies

Community detection for correlation matrices

A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that tends to be intrinsically biased due to its inconsistency with the null hypotheses underlying the existing algorithms. Here we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anti-correlated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested sub-communities with `hard' cores and `soft' peripheries. We apply our techniques to several financial time series and identify mesoscopic groups of stocks which are irreducible to a standard, sectorial taxonomy, detect `soft stocks' that alternate between communities, and discuss implications for portfolio optimization and risk management.Comment: Final version, accepted for publication on PR