Monitoring of engineered systems consists of characterizing the normal behavior of the system and tracking departures from it. Techniques to monitor a system can be split into two classes based on their use of inputs and outputs of the system. Systems-based monitoring refers to the case when both inputs and outputs of a system are available and utilized. Conversely, symptomatic monitoring refers to the case when only outputs of the system are available. This thesis extended symptomatic and systems-based monitoring of biomedical systems via the use of non-stationary signal processing and advanced monitoring methods. Monitoring of various systems of the human body is encumbered by several key hurdles. First, current biomedical knowledge may not fully comprehend the extent of inputs and outputs of a particular system. In addition, regardless of current knowledge, inputs may not be accessible and outputs may be, at best, indirect measurements of the underlying biological process. Finally, even if inputs and outputs are measurable, their relationship may be highly nonlinear and convoluted. These hurdles require the use of advanced signal processing and monitoring approaches. Regardless of the pursuit of symptomatic or system-based monitoring, the aforementioned hurdles can be partially overcome by using non-stationary signal analysis to reveal the way frequency content of biomedical signals change over time. Furthermore, the use of advanced classification and monitoring methods facilitated reliable differentiation between various conditions of the monitored system based on the information from non-stationary signal analysis. The human brain was targeted for advancement of symptomatic monitoring, as it is a system responding to a plethora internal and external stimuli. The complexity of the brain makes it unfeasible to realize system-based monitoring to utilize all the relevant inputs and outputs for the brain. Further, measurement of brain activity (outputs), in the indirect form of electroencephalogram (EEG), remains a workhorse of brain disorder diagnosis. In this thesis, advanced signal processing and pattern recognition methods are employed to devise and study an epilepsy detection and localization algorithm that outperforms those reported in literature. This thesis also extended systems-based monitoring of human biomedical systems via advanced input-output modeling and sophisticated monitoring techniques based on the information from non-stationary signal analysis. Explorations of system-based monitoring in the NMS system were driven by the fact that joint velocities and torques can be seen NMS responses to electrical inputs provided by the central nervous system (CNS) and the electromyograph (EMG) provides an indirect measurement of CNS excitations delivered to the muscles. Thus, both inputs and outputs of this system are more or less available and one can approach its monitoring via the use of system-based approaches. / text

This thesis deals with the topic of high resolution spectral analysis. In the first part, selected methods are presented and afterwards compared based on the Matlab implementations. The problematics of reduction of crossterms in quadratic time–frequency distributions is also covered. The second part is focused on the implementation and optimization of the algorithm for real-time computation of smoothed Wigner distribution function.

Brushless Direct Current (BLDC) motors are one of the motor types rapidly gaining popularity. BLDC motors are being increasingly used in critical high performance industries such as appliances, automotive, aerospace, consumer, medical, industrial automation equipment and instrumentation. Fault detection and condition monitoring of BLDC machines is therefore assuming a new importance. The objective of this research is to advance the field of rotor and load fault diagnosis in BLDC machines operating in a variety of operating conditions ranging from constant speed to continuous transient operation. This objective is addressed as three parts in this research. The first part experimentally characterizes the effects of rotor faults in the stator current and voltage of the BLDC motor. This helps in better understanding the behavior of rotor defects in BLDC motors. The second part develops methods to detect faults in loads coupled to BLDC motors by monitoring the stator current. As most BLDC applications involve non-stationary operating conditions, the diagnosis of rotor faults in non-stationary conditions forms the third and most important part of this research. Several signal processing techniques are reviewed to analyze non-stationary signals. Three new algorithms are proposed that can track and detect rotor faults in non-stationary or transient current signals.

This project attempts to quantify the integrity of the fixation of total hip arthro- T plasties (THAs) by observing vibration signal features. The aim of this thesis is, therefore, to find the signal differences between firm and loose prosthesis. These difference will be expressed in different transformed domains with the expectation that a certain domain will provide superior results. Once the signal differences have been determined they will be examined for their ability to quantify the looseness. Initially, a new definition of progressive, femoral component loosening was created, based on the application of mechanical fit, involving four general conditions. In order of increasing looseness the conditions (with their equivalent engineering associations) are listed as, firm (adherence), firm (interference), micro-loose (transition) and macro-loose (clearance). These conditions were then used to aid in the development and evaluation of a simple mathematical model based on an ordinary differential equation. Several possible parameters well suited to quantification such as gap displacement, cement/interface stiffness and apparent mass were the identified from the model. In addition, the development of this model provided a solution to the problem of unifying early and late loosening mentioned in the literature by Li et al. in 1995 and 1996. This unification permitted early (micro loose) and late (macro loose) loosening to be quantified, if necessary, with the same parameter. The quantification problem was posed as a detection problem by utilising a varying amplitude input. A set of detection techniques were developed to detect the quantity of a critical value, in this case a force. The detection techniques include deviation measures of the instantaneous frequency of the impulse response of the system (accuracy of 100%), linearity of the systems response to Gaussian input (total accuracy of 97.9% over all realisations) and observed resonant frequency linearity with respect to displacement magnitude (accuracy of 100%). Note, that as these techniques were developed with the model in mind their simulated performance was, therefore, considerably high. This critical value found by the detector was then fed into the model and a quantified output was calculated. The quantification techniques using the critical value approach include, ramped amplitude input resonant analysis (experimental accuracy of 94%) and ramped amplitude input stochastic analysis (experimental accuracy of 90%). These techniques were based on analysing the response of the system in the time-frequency domain and with respect to its short-time statistical moments to a ramping amplitude input force, respectively. In addition, other mechanically sound forms of analysis, were then applied to the output of the nonlinear model with the aim of quantifying the looseness or the integrity of fixation of the THA. The cement/interface stiffness and apparent mass techniques, inspired by the work of Chung et.al. in 1979, attempt to assess the integrity of fixation of the THA by tracking the mechanical behaviour of the components of the THA, using the frequency and magnitude of the raw transducer data. This technique has been developed fron the theory of Chung etal but with a differing perspective and provides accuracies of 82% in experimentation and 71% in simulation for the apparent mass and interface stiffness techniques, respectively. Theses techniques do not quantify all forms of clinical loosening, as clinical loosening can exist in many different forms, but they do quantify mechanical loosening or the mechanical functionality of the femoral component through related parameters that observe reduction in mechanical mass, stiffness and the amount of rattle generated by a select ghap betweent he bone/cement or prosthesis/cement interface. This form of mechanical loosening in currently extremely difficult to detect using radiographs. It is envisaged that a vibration test be used in conjunction with radiographs to provide a more complete picture of the integrity of fixation of the THA.

The quadratic class of time-frequency distributions (TFDs) forms a set of tools which allow to effectively extract important information from a nonstationary signal. To determine which TFD best represents the given signal, it is a common practice to visually compare different TFDs' time-frequency plots, and select as best the TFD with the most appealing plot. This visual comparison is not only subjective, but also difficult and unreliable especially when signal components are closely-spaced in the time-frequency plane. To objectively compare TFDs, a quantitative performance measure should be used. Several measures of concentration/complexity have been proposed in the literature. However, those measures by being derived with certain theoretical assumptions about TFDs are generally not suitable for the TFD selection problem encountered in practical applications. The non-existence of practically-valuable measures for TFDs' resolution comparison, and hence the non-existence of methodologies for the signal optimal TFD selection, has significantly limited the use of time-frequency tools in practice. In this thesis, by extending and complementing the concept of spectral resolution to the case of nonstationary signals, and by redefining the set of TFDs' properties desirable for practical applications, we define an objective measure to quantify the quality of TFDs. This local measure of TFDs' resolution performance combines all important signal time-varying parameters, along with TFDs' characteristics that influence their resolution. Methodologies for automatically selecting a TFD which best suits a given signal, including real-life signals, are also developed. The optimisation of the resolution performances of TFDs, by modifying their kernel filter parameters to enhance the TFDs' resolution capabilities, is an important prerequisite in satisfying any additional application-specific requirements by the TFDs. The resolution performance measure and the accompanying TFDs' comparison criteria allow to improve procedures for designing high-resolution quadratic TFDs for practical time-frequency analysis. The separable kernel TFDs, designed in this way, are shown to best resolve closely-spaced components for various classes of synthetic and real-life signals that we have analysed.