Time and frequency domain nonlinear system characterization for mechanical fault identification

Abstract

Mechanical systems are often nonlinear with nonlinear components and nonlinear connections, and mechanical damage frequently causes changes in the nonlinear characteristics of mechanical systems, e.g. loosening of bolts increases Coulomb friction nonlinearity. Consequently, methods which characterize the nonlinear behavior of mechanical systems are wellsuited to detect such damage. This paper presents passive time and frequency domain methods that exploit the changes in the nonlinear behavior of a mechanical system to identify damage.In the time domain, fundamental mechanics models are used to generate restoring forces, which characterize the nonlinear nature of internal forces in system components under loading. The onset of nonlinear damage results in changes to the restoring forces, which can be used as indicators of damage. Analogously, in the frequency domain, transmissibility (output-only) versions of auto-regressive exogenous input (ARX) models are used to locate and characterize the degree to which faults change the nonlinear correlations present in the response data. First, it is shown that damage causes changes in the restoring force characteristics, which can be used to detect damage. Second, it is shown that damage also alters the nonlinear correlations in the data that can be used to locate and track the progress of damage. Both restoring forces and auto-regressive transmissibility methods utilize operational response data for damage identification. Mechanical faults in ground vehicle suspension systems, e.g. loosening of bolts, are identified using experimental data.

Technological challenges of developing wireless health and usage monitoring systems

“…This method attempts to obtain a minimum-effort diagnosis of damage and despite requiring significant numbers of sensors, similar approaches could be considered in the future. For instance, progress was made towards detecting damage by analyzing the non-linear response of an automobile suspension system to entirely unknown inputs using a relatively simple model of the overall system 22 .…”

Automatic damage type classification and severity quantification using signal based and nonlinear model based damage sensitive features

“…There have been some works in that direction, but never for damage classification or quantification purposes. In a linear framework, some authors [19,20] have shown that a nonlinear damage will impact the transmissibility functions (i.e. the frequency domain ratio between two different outputs of the system) and they used such information to detect and locate the damage.…”

A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis

“…1 In the early study, research of mechanical fault diagnosis is mainly focused on time domain analysis and frequency domain analysis. 2 The time domain analysis is based on statistical characteristics and dimensionless parameters, such as maximum value, peak-to-peak value, kurtosis factor, and so on. 3 Because of colossal uncorrelated signals' interference, such as sensor noise and the external disturbances, mechanical fault signals cannot be observed directly through time domain analysis.…”