Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

Abstract

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

“…(a) Cluster #0 is labelled 'vibration response [56]'. It contains important terms such as 'vibration response, spectral kurtosis [57], nuisance attribute projection [58], outlier detection [59]'. These terms have relevance in the field of signal processing, particularly in vibration signal analysis and structural health monitoring.…”

Unsupervised Outlier Detection for Time-Series Data of Indoor Air Quality Using LSTM Autoencoder with Ensemble Method

“…Then, we built a sub-algorithm based on the previously obtained normal clusters. In an actual industrial environment, only normal classes can be used as initial knowledge; therefore, a one-class classification (OCC)-based approach is required (Arellano-Espitia et al, 2021). A one-class support vector machine (OC-SVM) is a special case of the support vector machine (SVM) algorithm used to detect outliers, producing the smallest hyperplane containing training samples based on the premise that most data are normal data (Mourão-Miranda et al, 2011).…”

A Convolutional Autoencoder Based Fault Diagnosis Method for a Hydraulic Solenoid Valve Considering Unknown Faults

“…Generally, autoencoders are designed with an encoder and a decoder. The encoder network maps the input data into latent features, while the decoder network reconstructs the input from the latent features [ 21 , 22 , 23 ]. Thus, parameters of an autoencoder are trained by minimizing the difference between the input and the output of the decoder without using label information.…”