Blind adaptation with appropriate objective function results in enhancement of signal of interest. Skewness is chosen as a measure of impulsiveness for blind adaptation to enhance impacting sources arising from defective rolling bearings. Such impacting sources can be modelled with harmonically related sinusoids which leads to discovery of harmonic content with unknown fundamental frequency by skewness maximization. Interfering components that do not possess harmonic relation are simultaneously suppressed with proposed method. An experimental example on rolling bearing fault detection is given to illustrate the ability of skewness maximization in uncovering harmonic content.

Maximizing the skewness of a measured signal by adaptive filtering to reveal hidden periodic impulses is proposed as a pre-processing method. Periodic impulsive signals are modelled by harmonically related sinusoids to prove that amplitude and phase distortion from a transfer function, effects of sinusoidal interferences and noise can be compensated for by a linear filter. The convergence behaviour of the skewness maximization algorithm is analysed to show that it is possible to recover the original harmonic structure with an unknown fundamental frequency by achieving maximum skewness in the given signal. It is shown that maximizing the skewness always results in a sub-space containing only a single harmonic family. Defect detection in rolling element bearings is presented as an application example and as a comparative study against kurtosis maximization.

Enhacement of material impulse response buried in reverberating ultrasonic echoes from thin layered materials can be exploited in order to be able to detect possible flaws. One of the methods presented in this study is to enhance the impulse response of a material by training an adaptive filter that promotes and appropriate statistical characteristic such as asymmetry. The other approach is to employ a parametric linear model of reverberations that utilizes Maximum Likelihood Estimation on its parameters, to later suppress the reverberations and reveal possible flaws. Both approaches are investigated and shown to succeeed under certain conditions and supported with experiments.

Pulse compression on overlapping echoes without knowledge of the pulse shape, transducer and propagation path impulse response is examined to provide valuable information about the sample structure in ultrasonic testing. A comparison against previous research is presented on two different levels of overlap severity with simulated signals. By exploiting the knowledge on the statistical characteristics of the signal of interest, an appropriate measure of merit, such as skewness, is maximized to promote impulsive occurrences to both extract the excitation signal and to enhance the impulse response of a material under test.

Rolling element bearings are used in rotating machinery in various industry branches. Their health status must be monitored continuously in order to establish proper operational conditions in a production process. Numerous approaches, which can be investigated under the subject of ``Condition Based Maintenance", have been studied within mechanical engineering and signal processing to be able to detect and classify possible faults on rolling bearings.Periodic impulsive signals can emerge from defected bearings within rotating machinery. As the signal is distorted by an unknown transfer function, noise and severe interference, the challenge becomes to reduce these effects as much as possible to extract valuable and reliable information about the rolling bearings' health status. Without any observation of the source signal, a scale-invariant higher order moment, skewness, can be used as a tool to characterize statistical properties to enhance the desired signal. It is the impulsiveness, thus asymmetry of the signal that will be promoted. To assess the performance of skewness, a signal model that consists of harmonically related sinusoids representing an impulsive source is built. Depending on such a model, surface characteristics of skewness are investigated. In relation to harmonic content, the ability of skewness in discovering such harmonic relation is studied. It has been observed that the optimization process converges to a setting where all harmonics are preserved, while any component that does not possess such a harmonic relation is suppressed. In the case of multiple mutually inharmonic source signals with harmonic support, it is shown that skewness maximization results in a setting where only the harmonic set with highest skewness remains. Finally, experimental examples are provided to support theoretical findings.

The scale invariant third order moment, skewness, is analysed as an objective function to an adaptive gradient ascent algorithm. The purpose is to achieve a spectrum at the filter output that can enable identification of possible bearing defect signatures which are impulsive and periodic. Harmonically related sinusoids are used to represent such signatures and to build a signal model allowing characterization of the objective surface of skewness, providing insight to its convergent behaviour. The results are supported with an experiment from an industry setting. Robustness of the proposed algorithm is demonstrated by examining the frequency spectrum resulting from the signal model.

In ultrasound examination of thin multi-layered materials, the received signal is, in both through-transmission and pulse-echo configurations, a superposition of multiple reflections from inside the sample. If the layer thicknesses are small compared to the duration of the emitted ultrasound pulse, the received signal will be a sum of overlapping ultrasound pulses. In such scenarios, estimation of the layer thicknesses is challenging. Previous work has adopted model-based decomposition of the overlapping echoes, or various pulse compression or deconvolution schemes, in order to better reveal the arrival times of each individual echo.