Teager-Kaiser energetic trajectory for machine diagnosis purposes

Increased requirements regarding safety, reliability and early detection of failures of industrial machines stimulate the development of new methods and tools for purposes of condition monitoring. The paper presents original concept of the Teager-Kaiser energetic trajectory representing an examined signal on the Teager-Kaiser energy plane. The Teager-Kaiser energetic trajectory illustrates simultaneously changes of instantaneous values of the Teager-Kaiser energy indicator and the velocity of change of the energy indicator. At the beginning of the paper, the Teager-Kaiser energetic trajectory is presented and described followed by examples of trajectories of simulated signals. In the next section, the author presents and discusses the model of signal simulating occurrence of a failure in a gearbox. Finally, the paper presents representations of signals recorded form gearbox during fatigue tests. The paper concludes with the discussion on application options of the Teager-Kaiser energetic plane in condition monitoring of rotation machinery

Multirate Frequency Transformations: Wideband AM-FM Demodulation with Applications to Signal Processing and Communications

The AM-FM (amplitude & frequency modulation) signal model finds numerous applications in image processing, communications, and speech processing. The traditional approaches towards demodulation of signals in this category are the analytic signal approach, frequency tracking, or the energy operator approach. These approaches however, assume that the amplitude and frequency components are slowly time-varying, e.g., narrowband and incur significant demodulation error in the wideband scenarios. In this thesis, we extend a two-stage approach towards wideband AM-FM demodulation that combines multirate frequency transformations (MFT) enacted through a combination of multirate systems with traditional demodulation techniques, e.g., the Teager-Kasiser energy operator demodulation (ESA) approach to large wideband to narrowband conversion factors. The MFT module comprises of multirate interpolation and heterodyning and converts the wideband AM-FM signal into a narrowband signal, while the demodulation module such as ESA demodulates the narrowband signal into constituent amplitude and frequency components that are then transformed back to yield estimates for the wideband signal. This MFT-ESA approach is then applied to the various problems of: (a) wideband image demodulation and fingerprint demodulation, where multidimensional energy separation is employed, (b) wideband first-formant demodulation in vowels, and (c) wideband CPM demodulation with partial response signaling, to demonstrate its validity in both monocomponent and multicomponent scenarios as an effective multicomponent AM-FM signal demodulation and analysis technique for image processing, speech processing, and communications based applications

Development of new fault detection methods for rotating machines (roller bearings)

Abstract Early fault diagnosis of roller bearings is extremely important for rotating machines, especially for high speed, automatic and precise machines. Many research efforts have been focused on fault diagnosis and detection of roller bearings, since they constitute one the most important elements of rotating machinery. In this study a combination method is proposed for early damage detection of roller bearing. Wavelet packet transform (WPT) is applied to the collected data for denoising and the resulting clean data are break-down into some elementary components called Intrinsic mode functions (IMFs) using Ensemble empirical mode decomposition (EEMD) method. The normalized energy of three first IMFs are used as input for Support vector machine (SVM) to recognize whether signals are sorting out from healthy or faulty bearings. Then, since there is no robust guide to determine amplitude of added noise in EEMD technique, a new Performance improved EEMD (PIEEMD) is proposed to determine the appropriate value of added noise. A novel feature extraction method is also proposed for detecting small size defect using Teager-Kaiser energy operator (TKEO). TKEO is applied to IMFs obtained to create new feature vectors as input data for one-class SVM. The results of applying the method to acceleration signals collected from an experimental bearing test rig demonstrated that the method can be successfully used for early damage detection of roller bearings. Most of the diagnostic methods that have been developed up to now can be applied for the case stationary working conditions only (constant speed and load). However, bearings often work at time-varying conditions such as wind turbine supporting bearings, mining excavator bearings, vehicles, robots and all processes with run-up and run-down transients. Damage identification for bearings working under non-stationary operating conditions, especially for early/small defects, requires the use of appropriate techniques, which are generally different from those used for the case of stationary conditions, in order to extract fault-sensitive features which are at the same time insensitive to operational condition variations. Some methods have been proposed for damage detection of bearings working under time-varying speed conditions. However, their application might increase the instrumentation cost because of providing a phase reference signal. Furthermore, some methods such as order tracking methods still can be applied when the speed variation is limited. In this study, a novel combined method based on cointegration is proposed for the development of fault features which are sensitive to the presence of defects while in the same time they are insensitive to changes in the operational conditions. It does not require any additional measurements and can identify defects even for considerable speed variations. The signals acquired during run-up condition are decomposed into IMFs using the performance improved EEMD method. Then, the cointegration method is applied to the intrinsic mode functions to extract stationary residuals. The feature vectors are created by applying the Teager-Kaiser energy operator to the obtained stationary residuals. Finally, the feature vectors of the healthy bearing signals are utilized to construct a separating hyperplane using one-class support vector machine. Eventually the proposed method was applied to vibration signals measured on an experimental bearing test rig. The results verified that the method can successfully distinguish between healthy and faulty bearings even if the shaft speed changes dramatically

A wide area system for power transmission security enhancement using a process systems approach

Imperial Users onl

Hypernasal Speech Analysis via Emperical Mode Decomposition and the Teager-Kasiser Energy Operator

In the area of speech science, one particular problem of importance has been to develop a clear method for detecting hypernasality in speech. For speech pathologists, hypernsality is a critical diagnostic used for judging the severity of velopharyngeal (nasal cavity/mouth separation) inadequacy in children with a cleft lip or cleft palate condition. For physicians and particularly neurologists, these same velopharyngeal inadequacies are believed to be linked to nervous system disorders such as Alzheimers disease and particularly Parkinson\u27s disease. One can therefore envision the need to not only find a reliable method for detecting hypernasality, but to also quantify the level (severity) of hypernasality as well. An integral component in the study of speech is the analysis of speech formants, i.e., vocal tract resonances. Traditional acoustical analysis methods of using a linear source model follow the premise that differences between normal and hypernasal speech can be distinguished by shifts or power changes in the formant frequencies and/or the widening (or narrowing) of the formant bandwidths. Such a premise, however, has not been validated with consistency. Part of the reason is that traditional acoustical analysis methods such as one-third octave band, LPC (Linear Predictive Coding), and cepstral analysis are ill-equipped to deal with the nonlinear, non-stationary, and wideband characteristics of normal and nasal speech signals. Relatively newer DSP methods that employ group delay or energy separation overcome some of these problems, but have their own issues such as possible mode mixing, noise, and the aforementioned wideband problem. However, initial investigations into energy separation methods show promise as long as these issues can be resolved. This thesis evaluates the success of a novel acoustical energy approach which deals with the mode mixing and wideband problems where: (1) a DSP sifting algorithm known as the EMD (Empirical Mode Decomposition) is first implemented to decompose the voice signal into a number of IMFs (Intrinsic Mode Functions). (2) Energy analysis is performed on each IMF via the Teager-Kaiser Energy Operator. The proposed EMD energy approach is applied to voice samples taken from the American CLP Craniofacial database and is shown to produce a clear delineation between normal and nasal samples and between different levels of hypernasality.\u2

Audio-assisted movie dialogue detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptions, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported. © 2008 IEEE

Audio-assisted movie dialogue detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the crosscorrelation and the magnitude of the corresponding the crosspower spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported