Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

Syahril Ramadhan Saufi,Meng Hee Lim,Mohd Salman Leong,Zair Asrar Bin Ahmad

doi:10.1109/access.2019.2938227

Syahril Ramadhan Saufi, Meng Hee Lim + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2938227

Copy DOI

Abstract

In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed.

Highlights

Safety and reliability are key factors in industrial operations
The most common deep learning models that have been extensively used in Fault detection and diagnosis (FDD) systems include convolutional neural network (CNN), stacked autoencoder (SAE), restricted Boltzmann machine (RBM), deep belief network (DBN) and deep neural network (DNN) [26]
The results showed that the proposed deep learning (DL) model outperformed the shallow machine learning (SML) model

Summary

INTRODUCTION

Safety and reliability are key factors in industrial operations. Rotating machinery is a vital component in many industries, and it is prone to failure due to harsh working conditions and long operational times [1], [2]. The advantages and disadvantages of every DL model used in FDD systems have been discussed in [35]. The most common deep learning models that have been extensively used in FDD systems include convolutional neural network (CNN), stacked autoencoder (SAE), restricted Boltzmann machine (RBM), deep belief network (DBN) and deep neural network (DNN) [26]. This review is intended to provide a brief discussion of challenges and future development of DL applications in FDD systems for rotating machinery. It does not describe each DL architecture, as such information has been

FAULT DETECTION AND DIAGNOSIS STAGES

DEVELOPMENT AND MODIFICATION OF DEEP LEARNING MODELS IN FDD SYSTEMS

Findings

CONCLUSION