Bayesian topology identification of linear dynamic networks

In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper, we explore a Bayesian model selection method for identifying the connectivity of networks of transfer functions, without the need to estimate the dynamics. The algorithm employs a Bayesian measure and a forward-backward search algorithm. To obtain the Bayesian measure, the impulse responses of network modules are modeled as Gaussian processes, and the hyperparameters are estimated by marginal likelihood maximization using the expectation-maximization algorithm. Numerical results demonstrate the effectiveness of this method

Parallel Wiener-Hammerstein Time Series

A Parallel Wiener-Hammerstein system is a nonlinear dynamical system obtained by connecting multiple Wiener-Hammerstein systems in parallel. Each parallel branch contains a static nonlinearity that is sandwiched in between two linear time-invariant (LTI) blocks. The presence of the two LTI blocks, and the parallel branches results in a problem that is harder to identify. The LTI blocks are realized as active filters while the static nonlinearity is implemented as a diode-resistor electronic circuit. The provided data was part of a previously published Automatica paper available online at Sciencedirect or as an ArXiv preprint. The Parallel Wiener-Hammerstein system, the measurement setup, and the input signals used are detailed in Section 10 of the aforementioned paper. This zip-file contains multiple measured input-output time series: a multisine estimation/validation data set, and a multisine and increasing-amplitude test data set. The data is available in the .csv and .mat file format.

F-16 Aircraft Benchmark Based on Ground Vibration Test Data

The F-16 Ground Vibration Test benchmark features a high order system with clearance and friction nonlinearities at the mounting interface of the payloads. The experimental data made available here were acquired on a full-scale F-16 aircraft on the occasion of the Siemens LMS Ground Vibration Testing Master Class. During the test campaign, two dummy payloads were mounted at the wing tips to simulate the mass and inertia properties of real devices typically equipping an F-16 in flight. The aircraft structure was instrumented with accelerometers. One shaker was attached underneath the right wing to apply input signals. The dominant source of nonlinearity in the structural dynamics was expected to originate from the mounting interfaces of the two payloads. These interfaces consist of T-shaped connecting elements on the payload side, slid through a rail attached to the wing side. A preliminary investigation showed that the back connection of the right-wing-to-payload interface was the predominant source of nonlinear distortions in the aircraft dynamics, and is therefore the focus of this benchmark study. All the provided files and information together with a detailed description of the F-16 aircraft benchmark system are available for download here. This zip-file contains a detailed system description, the estimation and test data sets, and some pictures of the setup. The data is available in the .csv and .mat file format

Wiener-Hammerstein benchmark with process noise

The Wiener-Hammerstein system structure is a well-known block-oriented structure. It contains a static nonlinearity that is sandwiched in between two linear time-invariant (LTI) blocks. The presence of the two LTI blocks results in a problem that is harder to identify. The LTI blocks are realized as active filters while the static nonlinearity is implemented as a diode-resistor electronic circuit. The Wiener-Hammerstein system proposed here as a benchmark contains dominant process noise. The process noise enters the system before the static nonlinearity. Two much less significant noise sources are present in the measurement channels of the input and output. All the provided files, data and information on the Wiener-Hammerstein system are available for download here together with an in-depth description of the measured input-output time series. This zip-file contains a detailed system description, an example estimation data set, the test data sets, the datasets measured during the past measurement campaign(s), pictures of the measurement setup, and an indicative electrical circuit schematic of the system. It is possible that the actual implemented Wiener-Hammerstein system deviates at some points (resistor values, opamp type) from the electrical circuit provided here. The data is available in the .csv and .mat file format

Hysteretic Benchmark with a Dynamic Nonlinearity

Hysteresis is a dynamic nonlinearity commonly encountered in very diverse engineering and science disciplines, ranging from solid mechanics, electromagnetism and aerodynamics to biology, ecology and psychology. In particular, the Bouc-Wen model has been intensively exploited during the last decades to represent hysteretic effects in mechanical engineering, especially in the case of random vibrations. It is proposed as a benchmark problem to identify a Bouc-Wen system based on synthetic input-output data time-series. A detailed formulation of the identification problem can be downloaded here. All the provided files and information on the Bouc-Wen system are available for download. The zip-file contains a detailed system description with a signal generation guide and the test data sets. This benchmark requires MATLAB to run