Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Abstract

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research.Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithmsFindings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated.Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015.

Predictive Maintenance with Linguistic Text Mining

“…Machine learning-based (or data-driven) (ML) systems [22][23][24][25], which utilize historical data, sensor readings, and other parameters to predict equipment failures and recommend maintenance actions, optimizing maintenance schedules. ML systems, being machine learning-based, may require a pre-training phase but do not analyze textual semantics; and condition monitoring systems (CMSs) [26][27][28], which continuously monitor machinery condition by collecting and analyzing real-time sensor data.…”

ML- and LSTM-Based Radiator Predictive Maintenance for Energy Saving in Compressed Air Systems

“…Studies on fault detection coupled with energy saving have been investigated [12][13][14][15][16][17]. Drakaki et al [12] surveyed recent work on machine learning (ML)-and Deep Learning (DL)-based induction motor predictive maintenance and then used power spectrum information as a feature for fault detection. Lee et al [13] presented an One-Versus-All (OVA) multi-class classification method for compressor faults, which shows high accuracy.…”

Enhanced Thermal Modeling of Electric Vehicle Motors Using a Multihead Attention Mechanism

“…Inspired by the deep learning methodologies discussed in the literature review [12][13][14] and seeking to address the existing research gap in motor temperature prediction methods, this study introduces an innovative approach utilizing a multihead attention mechanism. The multihead attention mechanism introduces a novel neural network architecture that relies exclusively on an attention mechanism [15].…”