Multichannel deconvolution of vibrational signals: A state-space inverse filtering approach.

Deconvolution of noisy measurements, especially when they are multichannel, has always been a challenging problem. The processing techniques developed range from simple Fourier methods to more sophisticated model-based parametric methodologies based on the underlying acoustics of the problem at hand. Methods relying on multichannel mean-squared error processors (Wiener filters) have evolved over long periods from the seminal efforts in seismic processing. However, when more is known about the acoustics, then model-based state-space techniques incorporating the underlying process physics can improve the processing significantly. The problems of interest are the vibrational response of tightly coupled acoustic test objects excited by an out-of-the-ordinary transient, potentially impairing their operational performance. Employing a multiple input/multiple output structural model of the test objects under investigation enables the development of an inverse filter by applying subspace identification techniques during initial calibration measurements. Feasibility applications based on a mass transport experiment and test object calibration test demonstrate the ability of the processor to extract the excitations successfully.

Transient recovery problem in acoustics: A multichannel model-based deconvolution approach.

Critical acoustical systems operating in complex environments contaminated with disturbances and noise offer an extreme challenge when excited by out-of-the-ordinary, impulsive, transient events that can be undetected and seriously affect their overall performance. Transient impulse excitations must be detected, extracted, and evaluated to determine any potential system damage that could have been imposed; therefore, the problem of recovering the excitation in an uncertain measurement environment becomes one of multichannel deconvolution. Recovering a transient and its initial energy has not been solved satisfactorily, especially when the measurement has been truncated and only a small segment of response data is available. The development of multichannel deconvolution techniques for both complete and incomplete excitation data is discussed, employing a model-based approach based on the state-space representation of an identified acoustical system coupled to a forward modeling solution and a Kalman-type processor for enhancement and extraction. Synthesized data are utilized to assess the feasibility of the various approaches, demonstrating that reasonable performance can be achieved even in noisy environments.

Multichannel spectral estimation in acoustics: A state-space approach.

Spectral estimation is a necessary methodology to analyze the frequency content of noisy data sets especially in acoustic applications. Many spectral techniques have evolved starting with the classical Fourier transform methods based on the well-known Wiener-Khintchine relationship relating the covariance-to-spectral density as a transform pair culminating with more elegant model-based parametric techniques that apply prior knowledge of the data to produce a high-resolution spectral estimate. Multichannel spectral representations are a class of both nonparametric, as well as parametric, estimators that provide improved spectral estimates. In any case, classical nonparametric multichannel techniques can provide reasonable estimates when coupled with peak-peaking methods as long as the signal levels are reasonably high. Parametric multichannel methods can perform quite well in low signal level environments even when applying simple peak-picking techniques. In this paper, the performance of both nonparametric (periodogram) and parametric (state-space) multichannel spectral estimation methods are investigated when applied to both synthesized noisy structural vibration data as well as data obtained from a sounding rocket flight. It is demonstrated that for the multichannel problem, state-space techniques provide improved performance, offering a parametric alternative compared to classical methods.

Vibrational processing of a dynamic structural flight system: A multichannel spectral estimation approach.

Dynamic testing of large flight vehicles (rockets) is not only complex, but also can be very costly. These flights are infrequent and can lead to disastrous effects if something were to fail during the flight. The development of sensors coupled to internal components offers a great challenge in reducing their size, yet still maintaining their precision. Sounding rockets provide both a viable and convenient alternative to the more costly vehicular flights. Some of the major objectives are to test various types of sensors for monitoring components of high interest as well as investigating real-time processing techniques. Signal processing presents an extreme challenge in this noisy multichannel environment. The estimation and tracking of modal frequencies from vibrating structures is an important set of features that can provide information about the components under test; therefore, high resolution multichannel spectral processing is required. The application of both single channel and multichannel techniques capable of producing reliable modal frequency estimates of a vibrating structure from uncertain accelerometer measurements is discussed.

Multichannel processing of vibrational measurements: A constrained subspace application.

Monitoring mechanical systems operating in uncertain environments contaminated with both environmental disturbances and noise lead directly to low signal-to-noise-ratios, creating an extremely challenging processing problem, especially in real-time. In order to estimate the performance of a particular system from uncertain vibrational data, it is necessary to identify its unique resonant (modal) frequency signature. The monitoring of structural modes to determine the condition of a device under investigation is essential, especially if it is a critical entity of an operational system. The development of a model-based scheme capable of the on-line tracking of the inherent structural modal frequencies by applying both constrained subspace identification techniques to extract the modal frequencies and state estimation methods to track the evolution is discussed. An application of this approach to a cylindrical structural device (pipe-in-air) is analyzed based on theoretical simulations along with controlled validation experiments, including injected anomalies illustrate the approach and performance. Statistics are gathered to bound potential processors for real-time performance employing these constrained techniques.

Anomaly detection for a vibrating structure: A subspace identification/tracking approach.

Mechanical devices operating in noisy environments lead to low signal-to-noise ratios creating a challenging signal processing problem to monitor the vibrational signature of the device in real-time. To detect/classify a particular type of device from noisy vibration data, it is necessary to identify signatures that make it unique. Resonant (modal) frequencies emitted offer a signature characterizing its operation. The monitoring of structural modes to determine the condition of a device under investigation is essential, especially if it is a critical entity of an operational system. The development of a model-based scheme capable of the on-line tracking of structural modal frequencies by applying both system identification methods to extract a modal model and state estimation methods to track their evolution is discussed along with the development of an on-line monitor capable of detecting anomalies in real-time. An application of this approach to an unknown structural device is discussed illustrating the approach and evaluating its performance.

Environmentally adaptive processing for shallow ocean applications: A sequential Bayesian approach.

The shallow ocean is a changing environment primarily due to temperature variations in its upper layers directly affecting sound propagation throughout. The need to develop processors capable of tracking these changes implies a stochastic as well as an environmentally adaptive design. Bayesian techniques have evolved to enable a class of processors capable of performing in such an uncertain, nonstationary (varying statistics), non-Gaussian, variable shallow ocean environment. A solution to this problem is addressed by developing a sequential Bayesian processor capable of providing a joint solution to the modal function tracking and environmental adaptivity problem. Here, the focus is on the development of both a particle filter and an unscented Kalman filter capable of providing reasonable performance for this problem. These processors are applied to hydrophone measurements obtained from a vertical array. The adaptivity problem is attacked by allowing the modal coefficients and/or wavenumbers to be jointly estimated from the noisy measurement data along with tracking of the modal functions while simultaneously enhancing the noisy pressure-field measurements.

Model-based failure detection for cylindrical shells from noisy vibration measurements.

Model-based processing is a theoretically sound methodology to address difficult objectives in complex physical problems involving multi-channel sensor measurement systems. It involves the incorporation of analytical models of both physical phenomenology (complex vibrating structures, noisy operating environment, etc.) and the measurement processes (sensor networks and including noise) into the processor to extract the desired information. In this paper, a model-based methodology is developed to accomplish the task of online failure monitoring of a vibrating cylindrical shell externally excited by controlled excitations. A model-based processor is formulated to monitor system performance and detect potential failure conditions. The objective of this paper is to develop a real-time, model-based monitoring scheme for online diagnostics in a representative structural vibrational system based on controlled experimental data.

Processing of prosthetic heart valve sounds for single leg separation classification.

People with serious heart conditions have had their expected life span extended considerably with the development of the prosthetic heart valve especially with the great strides made in valve design. Even though the designs are extremely reliable, the valves are mechanical and operating continuously over a long period; therefore structural failures can occur due to fatigue. In this paper acoustical signal processing techniques developed to process noisy heart valve sounds measured by a sensitive, surface contact microphone are discussed. Measuring heart sounds noninvasively in a noisy environment puts more demands on the signal processing to extract the desired signals from the noise. Heart valve sounds are short-duration (10-20 ms) transients and therefore nonstationary, requiring more sophisticated processing algorithms to achieve the desired signal-to-noise ratios. In this paper the preclassification signal processing is concentrated on exclusively. That is, the signal processing operations performed on the heart valve sounds prior to classification are discussed--a subject that will be developed in a future paper. Efforts are concentrated on the sounds corresponding to the heart valve opening cycle. Valve opening and closing acoustics present additional information about the outlet strut condition--the structural component implicated in valve failure. The importance of the opening sound for single leg separation detection/classification is based on the fact that as the valve opens, the disk passively hits the outlet strut. The opening sounds thus yield direct information about outlet strut condition with minimal amount of disturbance caused by the energy radiated from the disk. Hence the opening sound is a very desirable acoustic signal to extract. Unfortunately, the opening sounds have much lower signal levels relative to the closing sounds and therefore noise plays a more significant role than during the closing event. Because of this it is necessary to screen the sounds for outliers in order to insure a high sensitivity of classification. Because of the sharp resonances appearing in the corresponding spectrum, a parametric processing approach is developed based on an autoregressive model which was selected to characterize the sounds emitted by the Bjork-Shiley convexo-concave (BSCC) valve during opening cycle. First the basic signals and the extraction process used to create an ensemble of heart valve sounds are briefly discussed. Next, a beat monitor capable of rejecting beats that fail to meet an acceptance criteria based on their spectral content is developed.(ABSTRACT TRUNCATED AT 400 WORDS)

Classification of prosthetic heart valve sounds: a parametric approach.

People with heart problems have had their lives extended considerably with the development of the prosthetic heart valve. Great strides have been made in the development of the valves through the use of improved materials as well as efficient mechanical designs. However, since the valves operate continuously over a long period, structural failures can occur--even though they are relatively uncommon. Here the development of techniques to classify the valve either as having intact struts or as having a separated strut, commonly called single leg separation, is discussed. In this paper the signal processing techniques employed to extract the required signals/parameters are briefly reviewed and then it is shown how they can be used to simulate a synthetic heart valve database for eventual Monte Carlo testing. Next, the optimal classifier is developed under assumed conditions and its performance is compared to that of an adaptive-type classifier implemented with a probabilistic neural network. Finally, the adaptive classifier is applied to a data set and its performance is analyzed. Based on synthetic data it is shown that excellent performance of the classifiers can be achieved implying a potentially robust solution to this classification problem.