Vibration signal–based fault diagnosis in complex structures: A beam-like structure approach

Abstract

Sensor network–based data-driven fault diagnosis in complex structures using limited prior knowledge is an interesting and hot topic in the literature. In this study, an integrated feature characterization and fuzzy decision method is developed based on a novel beam-like structure approach for fault diagnosis using available limited prior knowledge. Complex structures embedded with vibration sensors can be regarded as some virtual beam-like structures by considering the vibration transmission path from vibration sources to each sensor. The dynamic response of the structures in this vibration transmission path can demonstrate obvious fault features if there is a fault (typically, for example, cracks in connecting rods or around bolts, and bolt-loosening). These fault features can be effectively characterized and efficiently captured and utilized for fault diagnosis with a fuzzy decision method based on the virtual beam-like structure concept. This novel virtual beam-like structure approach needs only limited prior knowledge of the faults and does not require stationary response data. It is also not with respect to a specific structure design and is easy to implement within a sensor network. The effectiveness of the proposed method is well validated in the experiments for fault diagnosis including loosening bolts or cracks around bolts of complex structures such as bolted-base hanging structures in satellites.

Fault diagnosis of bolt loosening in structures with a novel second-order output spectrum–based method

“…1. A more general and practical MDOF model of a beam-like structure following a sensor chain [33][34][35] is built to derive the properties of the SOOS transmissibility. 2.…”

“…More results about the virtual beam method can be referred to Wang and Jing. [33][34][35] This study supposes that the sensor chain is already chosen appropriately which can reflect the dynamic feature of the potential bolt loosening faults of the structure in the chosen area.…”

Wavelet Package Energy Transmissibility Function and Its Application to Wind Turbine Blade Fault Detection

“…Machine/deep learning methods are applied to vibration-based online rotational blade fault detection [13], [14]. Signal processing techniques, such as frequency domain analysis, time domain analysis, and frequency-time analysis, are used to abstract blade damage signatures from vibrational responses [15], [16]. Support vector machines (SVM), hidden Markov Models (HMM), finite element method (FEM) are also used as the vibrational signal processing tools to identify blade damage [17], [18].…”

Analysis on transformer vibration signal recognition based on convolutional neural network

“…Sampling Layer: This layer is used in the Convolutional Neural Network for intermediate sampling of output features provided by the convolutional layer. Feature information, which is obtained by convolution layer to feature mapping requires by sampling, the convolution output layer neurons as sampling the input layer, similar to the process of convolution layer, layer of sample has multiple features in the process of mapping, in the sample layer each feature mapping plane all the neurons have the same weight, sampling for the input image in convolution layer in convolution summation, and bias, after a sigmoid function which is to get the output of the sample layer [31]. In feature mapping, the excitation function Sigmoid with small kernel of influence function is used to obtain the mapping feature with constant displacement.…”