A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems

Abstract

The recent development of highly automated machinery and intelligent industrial plants has increasingly enabled the continuous monitoring of their efficiency and condition, with the aim of maintaining high production efficiency and minimal malfunctions. Typical condition monitoring and fault detection applications are often achieved using acoustic and vibrational techniques, but the availability of distributed electrical measurements opens new opportunities for industrial fault detection with minimal impact on electrical systems. Even if artificial intelligence-based approaches can be used to model industrial equipment by means of measures made on electrical systems to which they are connected, machine learning algorithms have been demonstrated to be particularly adequate for this purpose due to the huge amount of data produced by interconnected sensors and devices. In this context, the aim of this work is to propose a new unsupervised analysis methodology for detecting anomalies in industrial machinery using electrical current values and other parameters measured on the power grid. The proposed framework is aimed at incorporating the advantages of Machine Learning algorithms and those of traditional analysis, optimizing their operation to improve performance and execution time; this also incorporates a methodology for analyzing the temporal dynamics of the anomaly based on Short-time Fourier transform to strengthen the performance of the detection. The results obtained showed excellent performance, both compared to the evaluations of a technical expert and to other methodologies used in the literature, with zero false positives detected in all datasets tested and a negligible number of undetected outlier events, less than 4% of the total in the datasets.

Unsupervised novelty detection for time series using a deep learning approach

“…On the other hand, outlier detection-focused research has also advanced significantly [ 28 ]. developed a strategy based on deep learning and autoencoders, attaining a high accuracy rate of 96.77 % in spotting outliers, and [ 29 ] built a model with a 97.66 % accuracy rate for outlier identification.…”