Physical insights, characteristics and diagnosis of structural freeplay nonlinearity in transonic aeroelastic systems: a system identification based approach

The Next Generation of aircraft sustainment is based on an emerging paradigm known as Prognostics and Health Management. PHM encompasses numerous innovative concepts which shape the future of air asset sustainment according to pre-emptive condition-based maintenance, intelligence-based individual aircraft tracking, and damage/fault prognosis. Smart Diagnostics is an integral component of the SPHM paradigm, and refers to the detection, localisation and tracking of nonlinear structural anomalies that occur in various forms across the airframe structure or within mechanical interfaces. Control surface damage/ failure scenarios, such as, nonlinear hinge stiffness, backlash, and structural freeplay, are a class of structural anomaly which plague modern aircraft and introduce a range of dangerous nonlinear dynamic behaviours, such as, chaotic response and limit cycle oscillation. As a result, the freeplay structural anomaly can reduce fatigue life and is problematic for the stakeholder on many levels, including the management of structural health, maintenance practices, asset availability, mission capability, and sustainment provisions. The traditional approach to handling freeplay-type nonlinear events is based on avoidance and pre-emptive repetitive maintenance practices which, despite being over-conservative, inefficient and expensive, have remained unchanged for more than half a century. As the aerospace sector begins to adopt modern aircraft design and sustainment practices, including the realisation of SPHM-based technologies, there is an urgent requirement for contemporary solutions towards the diagnosis and tracking of structural freeplay anomalies. The research presented in this thesis is pursued with the global objective of contributing towards contemporary structural health monitoring technology through a nonlinear system identification framework for rapid control surface freeplay diagnostics. The proposed framework is driven by the fundamental assumption that all information pertaining to the freeplay event is contained within the time-histories extracted from an aircraft¿s sensory network. It is shown that through careful adaptation of well-established nonlinear system identification methods, namely the Higher-Order Spectra (HOS) and Hilbert-Huang Transform (HHT), rapid detection, localisation and magnitude tracking of the freeplay event is realisable, through a truly data-driven framework, with no inherent dependency of knowledge of the airframe structure, the flight parameters, the aerodynamic condition, or uncertainties. A novel and systematic approach is used to characterise the freeplay event, where nonlinear aeroelastic predictions (numerical aeroelastic models of increasing complexity) are considered to study the isolated physical freeplay mechanism in a nonlinear system identification setting, to understand how its physical action on an aeroelastic system can be exploited for diagnostics purposes. The findings are adapted to formulate temporal and spectral characteristic signatures, then implemented as a basis for the data-driven diagnostics strategy. A flight test case study is used to show that the signature-based diagnostics framework which is formulated using numerical cases with well-defined parameters, remains valid when diagnosing freeplay in a real-world aircraft system. The freeplay is detected and isolated, then a single tuned algorithm is shown to efficiently track the freeplay magnitude over the course of three years with several maintenance/ repair cycles, using a sensor with significant spatial discrepancy to the freeplay source. It is shown that rapid actionable diagnostics information can be extracted with a high level of robustness, demonstrated and verified by making consistent predictions despite: i) a large deviation in Mach number and angle-of-attack (with high angle manoeuvres), ii) highly nonlinear aerodynamic conditions, iii) no knowledge of uncertainty bounds, iv) mixture between stationary nonstationary response, and iv) little information available pertaining to the aircraft structural properties or geometry (a single geometric vector is used). In developing the diagnostics framework, numerous freeplay induced nonlinear phenomena are revisited, providing a new understanding of the structural freeplay physical mechanism. Several freeplay-induced nonlinear phenomena are defined, quantified and related according to a consolidated underlying nonlinear mechanism, founded upon empirically derived correlations. In showing that data-driven signature-based diagnostics is feasible for freeplay, this research makes a significant contribution towards the fields of nonlinear system identification, applied nonlinear dynamics and aircraft structural health monitoring. This provides a clear pathway to extend this signature-based system identification diagnostics strategy to capture other discrete nonlinear mechanisms in aircraft systems, or any relevant mechanical systems across the engineering disciplines. Requirements and limiting aspects of the data-driven approach are thoroughly discussed, predominantly related to sensory network requirements, and recommendations on how to address the limitations and progress with this research are clearly outlined

時間と周波数領域情報に基づいたシステムモデリングとその応用

System modeling is required to deal with the time-varying system dynamics or the experimental data with insufficient information. However, the existing methods cannot construct satisfactory models for rapidly varying systems or severely band-limited signals. This thesis focuses on the new approaches to solve such system modeling problems based on time and frequency-domain information and illustrates their applications in time-varying channel identification and localization system. For the rapid time-varying systems, parameters can be approximated by the cosine series using virtual even periodic functions. Following the orthogonality of the trigonometric functions, the parameter estimation is recursively implemented by estimating the coefficients of each degree of the cosine harmonic term. For the localization system with insufficient frequency components, the spectral characteristics including phase information in frequency domain and the information evaluation in time domain are applied to improve the convergence performance. Numerical simulations demonstrate the effectiveness of the new approaches.北九州市立大

Electrical Signature Analysis of Synchronous Motors Under Some Mechanical Anomalies

Electrical Signature Analysis (ESA) has been introduced for some time to investigate the electrical anomalies of electric machines, especially for induction motors. More recently hints of using such an approach to analyze mechanical anomalies have appeared in the literature. Among them, some articles cover synchronous motors usually being employed to improve the power factor, drive green vehicles and reciprocating compressors or pumps with higher efficiency. Similarly with induction motors, the common mechanical anomalies of synchronous motor being analyzed using the ESA are air-gap eccentricity and single point bearing defects. However torsional effects, which are usually induced by torsional vibration of rotors and by generalized roughness bearing defects, have seldom been investigated using the ESA. This work presents an analytical method for ESA of rotor torsional vibration and an experimentally demonstrated approach for ESA of generalized roughness bearing defects. The torsional vibration of a shaft assembly usually induces rotor speed fluctuations resulting from the excitations in the electromagnetic (EM) or load torque. Actually, there is strong coupling within the system which is dynamically dependent on the interactions between the electromagnetic air-gap torque of the synchronous machine and the rotordynamics of the rotor shaft assembly. Typically this problem is solved as a one-way coupling by the unidirectional load transfer method, which is based on predetermined or assumed EM or load profile. It ignores the two-way interactions, especially during a start-up transient. In this work, a coupled equivalent circuit method is applied to reflect this coupling, and the simulation results show the significance of the proposed method by the practical case study of Electric Submersible Pump (ESP) system. The generalized roughness bearing anomaly is linked to load torque ripples which can cause speed oscillations, while being related to current signature by phase modulation. Considering that the induced characteristic signature is usually subtle broadband changes in the current spectrum, this signature is easily affected by input power quality variations, machine manufacturing imperfections and the interaction of both. A signal segmentation technique is introduced to isolate the influence of these disturbances and improve the effectiveness of applying the ESA for this kind of bearing defects. Furthermore, an improved experimental procedure is employed to closely resemble naturally occurring degradation of bearing, while isolating the influence of shaft currents on the signature of bearing defects during the experiments. The results show that the proposed method is effective in analyzing the generalized roughness bearing anomaly in synchronous motors

Damage localization based on symbolic time series analysis

Copyright © 2014 John Wiley & Sons, Ltd. The objective of this paper is to localize damage in a single or multiple state at early stages of development on the basis of the principles of symbolic dynamics. Symbolic time series analysis (STSA) of noise-contaminated responses is used for feature extraction to detect and localize a gradually evolving deterioration in the structure according to the changes in the statistical behaviour of symbol sequences. Basically, in STSA, statistical features of the symbol sequence can be used to describe the dynamic status of the system. Symbolic dynamics has some useful characteristics making it highly demanded for implementation in real-time observation application such as SHM. First, it significantly reduces the dimension of information and provides information-rich representation of the underlying data. Second, symbolic dynamics and the set of statistical measures built upon it represent a solid framework to address the main challenges of the analysis of nonstationary time data. Finally, STSA often allows capturing the main features of the underlying system whilst alleviating the effects of harmful noise. The method presented in this paper consists of four primary steps: (i) acquisition of the time series data; (ii) creating the symbol space to produce symbol sequences on the basis of the wavelet transformed version of time series data; (iii) developing the symbol probability vectors to achieve anomaly measures; and (iv) localizing damage on the basis of any sudden variation in anomaly measure of different locations. The method was applied on a flexural beam and a 2-D planar truss bridge subjected to varying Gaussian excitation in presence of 2% white noise to examine the efficiency and limitations of the method. Simulation results under various damage conditions confi rmed the efficiency of the proposed approach for localization of gradually evolving deterioration in the structure; however, for the future work, the method needs to be verified by experimental data

Tracking Rhythmicity in Biomedical Signals using Sequential Monte Carlo methods

Cyclical patterns are common in signals that originate from natural systems such as the human body and man-made machinery. Often these cyclical patterns are not perfectly periodic. In that case, the signals are called pseudo-periodic or quasi-periodic and can be modeled as a sum of time-varying sinusoids, whose frequencies, phases, and amplitudes change slowly over time. Each time-varying sinusoid represents an individual rhythmical component, called a partial, that can be characterized by three parameters: frequency, phase, and amplitude. Quasi-periodic signals often contain multiple partials that are harmonically related. In that case, the frequencies of other partials become exact integer multiples of that of the slowest partial. These signals are referred to as multi-harmonic signals. Examples of such signals are electrocardiogram (ECG), arterial blood pressure (ABP), and human voice. A Markov process is a mathematical model for a random system whose future and past states are independent conditional on the present state. Multi-harmonic signals can be modeled as a stochastic process with the Markov property. The Markovian representation of multi-harmonic signals enables us to use state-space tracking methods to continuously estimate the frequencies, phases, and amplitudes of the partials. Several research groups have proposed various signal analysis methods such as hidden Markov Models (HMM), short time Fourier transform (STFT), and Wigner-Ville distribution to solve this problem. Recently, a few groups of researchers have proposed Monte Carlo methods which estimate the posterior distribution of the fundamental frequency in multi-harmonic signals sequentially. However, multi-harmonic tracking is more challenging than single-frequency tracking, though the reason for this has not been well understood. The main objectives of this dissertation are to elucidate the fundamental obstacles to multi-harmonic tracking and to develop a reliable multi-harmonic tracker that can track cyclical patterns in multi-harmonic signals

Orthonormal-Basis Partitioning And Time-Frequency Representation of Non-Stationary Signals

Spectral analysis is important in many fields, such as speech, radar and biomedicine. Many signals encountered in these areas possess time-varying spectral characteristics. The power spectrum indicates what frequencies exist in the signal but it does not show when those frequencies occur. Time-frequency analysisprovides this missing information. A time-frequency representation of the signal shows the intensities of the frequencies in the signal at the times they occur, and thus reveals if and how the frequencies of a signal are changing over time.Time-dependent spectral analysis of beat-to-beat variations of cardiac rhythm, or heart rate variability (HRV), represents a major challenge due to the structure of the signal. A number oftime-frequency representations have been proposed for the estimation of the time-dependent spectra. However, time-frequency analysis of multicomponent physiological signals such as cardiac rhythm is complicated by the presence of numerous, ill-structured frequency elements. We sought to develop a simple method for 1)detecting changes in the structure of the HRV signal, 2)segmenting the signal into pseudo-stationary portions, and 3)exposing characteristic patterns of the changes in thetime-frequency plane. The method, referred to as Orthonormal-Basis Partitioning and Time-Frequency Representation (OPTR), is validated on simulated signals and HRV data. Unlike the traditional time-frequency HRV representations, which are usuallyapplied to short segments of signals recorded in controlled conditions, OPTR can be applied to long and "content-rich" ambulatory signals to obtain the signal representation along withits time-varying spectrum. Thus, the proposed approach extends the scope of applications of the time-frequency analysis to all types of HRV signals and to other physiological data