An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

Abstract

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

A Review of Fault Diagnosing Methods in Power Transmission Systems

“…A theoretical foundation can be found in [88]. SVM structure is shown in Figure 8 [89]. SVM classifiers find optimal hyperplane which maximizes the margin between two entities.…”

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

“…The sets were then preprocessed and various features extracted, similar to [36], to achieve a simpler classifier. The extracted features and their properties are represented in Appendix B in Tab.…”

“…Variable name Description data preprocessing look_back the history window length look_ahead the gap between last historical sample and predicted one; in presented experiments look_back=1 (predict sample immediately following the history) in_grid the number of input data quantization levels out_grid the number of output data quantization levels in_algorithm the input data quantization algorithm, can be static, adaptive (used in presented experiments) or none (no quantization) out_algorithm analogical to in_algorithm, but for output data samples_percentage the percentage of total available data the model is trained on; it is determined during preprocessing and all following model and analyzer instances are using the same subset model cells the number of GRU model cells; presented experiments utilized only single GRU layer, but it is possible to specify more analyzer length threshold † an anomaly candidate length (in samples) that qualifies it as an anomaly maximum amplitude threshold † an anomaly candidate maximum amplitude (measured as a distance between real and predicted sample quantization bin middles) that qualifies it as an anomaly cumulative amplitude threshold † sum of anomaly candidate amplitudes that qualifies it as an anomaly † various threshold values can be combined, creating a set of rules allowing to determine if an anomaly candidate is an anomaly Table B.12: Feature extractors used for OC-SVM [36,37]. The input signal is denoted as x[n], where n = {1, 2, .…”

Fault Detection and Classification in Cantilever Beams Through Vibration Signal Analysis and Higher-Order Statistics

“…Singular spectrum analysis (SSA), a special type of principal component analysis (PCA), was implemented in Garcia and Trendafilova (2014) for structural vibration analysis and monitoring. Support vector machines (SVMs) were designed to estimate the states of various rotating components in agro-industrial machinery in Ruiz-Gonzalez et al (2014).…”