A brief overview of speech enhancement with linear filtering

Abstract In this paper, we provide an overview of some recently introduced principles and ideas for speech enhancement with linear filtering and explore how these are related and how they can be used in various applications. This is done in a general framework where the speech enhancement problem is stated as a signal vector estimation problem, i.e., with a filter matrix, where the estimate is obtained by means of a matrix-vector product of the filter matrix and the noisy signal vector. In this framework, minimum distortion, minimum variance distortionless response (MVDR), tradeoff, maximum signal-to-noise ratio (SNR), and Wiener filters are derived from the conventional speech enhancement approach and the recently introduced orthogonal decomposition approach. For each of the filters, we derive their properties in terms of output SNR and speech distortion. We then demonstrate how the ideas can be applied to single- and multichannel noise reduction in both the time and frequency domains as well as binaural noise reduction.</jats:p

Fault detection and diagnosis of a multistage helical gearbox using magnitude and phase information from vibration signals

Vibration generated by a gearbox carries a great deal of information regarding its health condition. This research aims primarily on the detection and diagnosis of tooth defects in a multistage gearbox based on advanced vibration analysis. Time synchronised averaging (TSA) analysis is effective at removing noise but it is inefficient in implementation and in diagnosing different types of faults such as bearing defects other than gears. Conventional bispectrum (CB) can eliminate Gaussian noise while it preserves the signal’s phase information, however its overpopulated contents can still provide inaccurate information regarding to different types of gear faults. Recently developed modulation signal bispectrum (MSB) has the high potential to lead to the high accuracy of diagnostics of gearboxes as it more effectively characterises modulation signals such as gearbox vibrations. Therefore, the research takes MSB as the fundamental tool for analysing gearbox vibration signals and developing accurate diagnostic techniques. Firstly, it has realised that conventional techniques often ignore the effect of phase information in gearbox diagnostics. This thesis then focuses on developing CB and MSB based techniques for detecting and diagnosing of gearbox faults. Secondly, it has found that vibration responses from a multiple stage gearbox have high interferences between amplitude modulation (AM) and phase modulation (PM) which can be formalised from both gear faults and inherent manufacturing errors. However, the faults can induce wider bandwidth vibrations. Correspondingly, optimal component based schemes are also developed based on the use of MSB coherence results. Then the proposed MSB method allows an effective gearbox diagnosis using the signals in a narrower frequency band that is below twice the rotational frequency plus the highest meshing frequency amongst different gear transmission stages, being more suitable for wireless network condition monitoring systems. It has also found that the signals at resonance frequencies has a higher signal-to-noise ratio and more effective for obtaining accurate diagnosis. Also software encoder based TSA was found to be not robust and accurate due to the influences of noise and referencing components on obtaining a reliable phase signal for implementing TSA. Finally, the diagnostics carried out upon different fault cases using both CB and MSB have verified the proposed approaches can provide accurate diagnostic results, and with the new MSB based detector and estimator being more effective in differentiating between diffident fault locations for two local and one non-uniformly distributed tooth damages in a two stage helical gearbox

Blind Estimation of Multi-Path and Multi-User Spread Spectrum Channels and Jammer Excision via the Evolutionary Spectral Theory

Despite the significant advantages of direct sequence spreadspectrum communications, whenever the number of users increases orthe received signal is corrupted by an intentional jammer signal,it is necessary to model and estimate the channel effects in orderto equalize the received signal, as well as to excise the jammingsignals from it. Due to multi-path and Doppler effects in thetransmission channels, they are modeled as random, time-varyingsystems. Considering a wide sense stationary channel during thetransmission of a number of bits, a linear time-varying modelcharacterized by a random number of paths, each beingcharacterized by a delay, an attenuation factor and a Dopplerfrequency shift, is shown to be an appropriate channel model. Itis shown that the estimation of the parameters of such models ispossible by means of the spreading function, related to thetime-varying frequency response of the system and the associatedevolutionary kernels. Applying the time-frequency orfrequency-frequency discrete evolutionary transforms, we show thata blind estimation procedure is possible by computing thespreading function from the discrete evolutionary transform ofthe received signal. The estimation also requires the synchronizedpseudo-noise sequence for either of the users we are interestedin. The estimation procedure requires to adaptively implementingthe discrete evolutionary transform to estimate the spreadingfunction and determine the channel parameters. Once the number ofpaths, delays, Doppler frequencies and attenuations characterizingthe channel are found, a decision parameter can be obtained todetermine the transmitted bit. We will show also that ourestimation approach supports multiuser communication applicationssuch as uplink and downlink in wireless communicationtransmissions. In the case of an intentional jamming, common inmilitary applications, we consider a receiver based onnon-stationary Wiener masking that excises such jammer as well asinterference from other users. Both the mask and the optimalestimator are obtained from the discrete evolutionarytransformation. The estimated parameters from the computedspreading function, corresponding to the closest to the line ofsight signal path, provide an efficient detection scheme. Ourprocedures are illustrated with simulations, that display thebit-error rate for different levels of channel noise and jammersignals

The Optimization of Vibration Data Analysis for the Detection and Diagnosis of Incipient Faults in Roller Bearings

The rolling element bearing is a key component of many machines. Accurate and timely diagnosis of its faults is critical for proactive predictive maintenance. The research described in this thesis focuses on the development of techniques for detecting and diagnosing incipient bearing faults. These techniques are based on improved dynamic models and enhanced signal processing algorithms. Various common fault detection techniques for rolling element bearings are reviewed in this work and a detailed experimental investigation is described for several selected methods. Envelope analysis is widely used to obtain the bearing defect harmonics from the spectrum of the envelope of a vibration signal. This enables the detection and diagnosis of faults, and has shown good results in identifying incipient faults occurring on the different parts of a bearing. However, a critical step in implementing envelope analysis is to determine the frequency band that contains the signal component corresponding to the bearing fault (the one with highest signal to noise ratio). The choice of filter band is conventionally made via manual inspection of the spectrum to identify the resonant frequency where the largest change has occurred. In this work, a spectral kurtosis (SK) method is enhanced to enable determination of the optimum envelope analysis parameters, including the filter band and centre frequency, through a short time Fourier transform (STFT). The results show that the maximum amplitude of the kurtogram indicates the optimal parameters of band pass filter that allows both outer race and inner race faults to be determined from the optimised envelope spectrum. A performance evaluation is carried out on the kurtogram and the fast kurtogram, based on a simulated impact signal masked by different noise levels. This shows that as the signal to noise ratio (SNR) reaches as low as -5dB the STFT-based kurtogram is more effective at identifying periodic components due to bearing faults, and is less influenced by irregular noise pulses than the wavelet based fast kurtogram. A study of the accuracy of rolling-bearing diagnostic features in detecting bearing wear processes and monitoring fault sizes is presented for a range of radial clearances. Subsequently, a nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics. Simulation results indicate that the vibrations at fault characteristic frequencies exhibit significant variability for increasing clearances. An increased vibration level is detected at the bearing characteristic frequency for an outer race fault, whereas a decreased vibration level is found for an inner race fault. Outcomes of laboratory experiments on several bearing clearance grades, with different local defects, are used herein for model validation purposes. The experimental validation indicates that the envelope spectrum is not accurate enough to quantify the rolling element bearing fault severity adequately. To improve the results, a new method has been developed by combining a conventional bispectrum (CB) and modulation signal bispectrum (MSB) with envelope analysis. This suppresses the inevitable noise in the envelope signal, and hence provides more accurate diagnostic features. Both the simulation and the experimental results show that MSB extracts small changes from a faulty bearing more reliably, enabling more accurate and reliable fault severity diagnosis. Moreover, the vibration amplitudes at the fault characteristic frequencies exhibit significant changes with increasing clearance. However, the vibration amplitude tends to increase with the severity of an outer race fault and decrease with the severity of an inner race fault. It is therefore necessary to take these effects into account when diagnosing the size of a defect

Enhanced information extraction from noisy vibration data for machinery fault detection and diagnosis

As key mechanical components, bearings and gearboxes are employed in most machines. To maintain efficient and safe operations in modern industries, their condition monitoring has received massive attention in recent years. This thesis focuses on the improvement of signal processing approaches to enhance the performance of vibration based monitoring techniques taking into account various data mechanisms and their associated periodic, impulsive, modulating, nonlinear coupling characteristics along with noise contamination. Through in-depth modelling, extensive simulations and experimental verifications upon different and combined faults that often occur in the bearings and gears of representative industrial gearbox systems, the thesis has made following main conclusions in acquiring accurate diagnostic information based on improved signal processing techniques: 1) Among a wide range of advanced approaches investigated, such as adaptive line enhancer (ALE), wavelet transforms, time synchronous averaging (TSA), Kurtogram analysis, and bispectrum representations, the modulation signal bispectrum based sideband estimator (MSB-SE) is regarded as the most powerful tool to enhance the periodic fault signatures as it has the unique property of simultaneous demodulation and noise reduction along with ease of implementation. 2) The proposed MSB-SE based robust detector can achieve optimal band selection and envelope spectrum analysis simultaneously and show more reliable results for bearing fault detection and diagnosis, compared with the popular Kurtogram analysis which highlights too much on localised impulses. 3) The proposed residual sideband analysis yields accurate and consistent diagnostic results of planetary gearboxes across wide operating conditions. This is because that the residual sidebands are much less influenced by inherent gear errors and can be enhanced by MSB analysis. 4) Combined faults in bearings and gears can be detected and separated by MSB analysis. To make the results more reliable, multiple slices of MSB-SE can be averaged to minimise redundant interferences and improve the diagnostic performance

Compound Fault Diagnosis of Centrifugal Pumps Using Vibration Analysis Techniques

Centrifugal pumps are widely used in many different industrial processes, such as power generation stations, chemical processing plants, and petroleum industries. The problem of failures in centrifugal pumps is a large concern due to its significant influence on such critical industries. Particularly, as the core, parts of a pump, bearings and the impellers are subject to different corrosions and their faults can cause major degradation of pump performances and lead to the breakdown of production. Therefore, an early detection of these types of faults would provide information to take timely preventive actions. This research investigates more effective techniques for diagnosing common faults of impellers and bearings with advanced signal analysis of surface vibration. As overall vibration responses contain a high level of broadband noises due to fluid cavities and turbulences, noise reduction is critical to developing reliable and accurate features. However, considering the modulation effect between the rotating shaft, vane passing components and any structural resonances, a modulation signal bispectrum (MSB) method is mainly used to extract these deterministic characteristics of modulations, which differs from previous researches in that the broadband vibration is often characterised with statistical methods, high frequency demodulation along spectrum analysis. Both theoretical analysis and experimental evaluation show that the diagnostic features developed by MSB allow impellers with inlet vane damages and exit vane faults to be identified under different operating conditions. It starts with an in-depth examination of the vibration excitation mechanisms associated with each type of common pump faults including impeller leakages, impeller blockages, bearing inner race defects and bearing outrace defects. Subsequently, fault diagnosis was carried out using popular spectrum and envelope analysis, and more advanced kurtogram and MSB analysis. These methods all can successfully provide correct detection and diagnosis of the faults, which are induced manually to the test pump. Envelope analysis in a bands optimised with Kurtogram produces outstanding detection results for bearing faults but not the impeller faults in a frequency range as high as several thousand hertz (about 7.5kHz). In addition, it cannot provide satisfactory diagnostic results in separating the faults across different flow rates, especially when the compound faults were evaluated. This deficiency is because they do not have the capability of suppressing the random noises. Meanwhile, it has found that the MSB analysis allows both impeller and bearing faults to be detected and diagnosed. Especially, when the pump operated with compound faults both the fault types and severity can be attained by the analysis with acceptable accuracy for different flow rates. This high performance of diagnosis is due to that MSB has the unique capability of noise reduction and nonlinearity demodulation. Moreover, MSB diagnosis can be a frequency range lower than 2 times of the blade pass frequency (<1kHz), meaning that it can be more cost-effective as it demands lower performance measurement systems. In addition, the study also found that one accelerometer mounted on the pump housing is sufficient to monitor the faults on both the impeller and the bearing as it uses a lower frequency vibration which propagates far away from the bearing to the housing, rather than another accelerometer on the bearing pedestal directly

Radar target classification by micro-Doppler contributions

This thesis studies non-cooperative automatic radar target classification. Recent developments in silicon-germanium and monolithic microwave integrated circuit technologies allows to build cheap and powerful continuous wave radars. Availability of radars opens new applications in different areas. One of these applications is security. Radars could be used for surveillance of huge areas and detect unwanted moving objects. Determination of the type of the target is essential for such systems. Microwave radars use high frequencies that reflect from objects of millimetre size. The micro-Doppler signature of a target is a time-varying frequency modulated contribution that arose in radar backscattering and caused by the relative movement of separate parts of the target. The micro-Doppler phenomenon allows to classify non-rigid moving objects by analysing their signatures. This thesis is focused on designing of automatic target classification systems based on analysis of micro-Doppler signatures. Analysis of micro-Doppler radar signatures is usually performed by second-order statistics, i.e. common energy-based power spectra and spectrogram. However, the information about phase coupling content in backscattering is totally lost in these energy-based statistics. This useful phase coupling content can be extracted by higher-order spectral techniques. We show that this content is useful for radar target classification in terms of improved robustness to various corruption factors. A problem of unmanned aerial vehicle (UAV) classification using continuous wave radar is covered in the thesis. All steps of processing required to make a decision out of the raw radar data are considered. A novel feature extraction method is introduced. It is based on eigenpairs extracted from the correlation matrix of the signature. Different classes of UAVs are successfully separated in feature space by support vector machine. Within experiments or real radar data, achieved high classification accuracy proves the efficiency of the proposed solutions. Thesis also covers several applications of the automotive radar due to very high growth in technologies for intelligent vehicle radar systems. Such radars are already build-in in the vehicle and ready for new applications. We consider two novel applications. First application is a multi-sensor fusion of video camera and radar for more efficient vehicle-to-vehicle video transmission. Second application is a frequency band invariant pedestrian classification by an automotive radar. This system allows us to use the same signal processing hardware/software for different countries where regulations vary and radars with different operating frequency are required. We consider different radar applications: ground moving target classification, aerial target classification, unmanned aerial vehicles classification, pedestrian classification. The highest priority is given to verification of proposed methods on real radar data collected with frequencies equal to 9.5, 10, 16.8, 24 and 33 GHz