Machine health surveillance system by using deep learning sparse autoencoder

Faizan Ullah,Fasee Ullah,Masood Ahmad,Abdullah Alharbi,Atif Khan,Wael Alosaimi,Abdu Salam,Muhammad Abrar

doi:10.1007/s00500-022-06755-z

Abstract

Deep learning is a rapidly growing research area having state of art achievement in various applications including but not limited to speech recognition, object recognition, machine translation, and image segmentation. In the current modern industrial manufacturing system, Machine Health Surveillance System (MHSS) is achieving increasing popularity because of the widespread availability of low cost sensors internet connectivity. Deep learning architecture gives useful tools to analyze and process these vast amounts of machinery data. In this paper, we review the latest deep learning techniques and their variant used for MHSS. We used Gearbox Fault Diagnosis dataset in this paper that contains the sets of vibration attributes recorded by SpectraQuest’s Gearbox Fault Diagnostics Simulator. In addition, we used the variant of auto encoders for feature extraction to achieve higher accuracy in machine health surveillance. The results showed that the bagging ensemble classifier based on voting techniques achieved 99% accuracy.

Highlights

The deep learning is the promising area of research in artificial intelligence
Deep learning is a subcategory of machine learning that uses the neural network to design a highly accurate system
There are a number of successful implementations of supervised and unsupervised based deep learning techniques in computer vision and natural language processing

Summary

Introduction

The deep learning is the promising area of research in artificial intelligence. In classification of an image-processing task, the deep learning algorithm grabs pixel value in the input layer and allocates label value to the object in the output layer. Among these two layers, there are a number of internal layers, known as hidden layers, that assembles successive higher-order features (LeCun, Haffner, Bottou, & Bengio, 1999). There is no standard number of layers, but most research in this area considers at least more than two layers must be present. In conventional machine learning algorithms, feature engineering is the task of choosing relevant features compulsory for the algorithm to work efficiently. Four deep learning algorithms i.e. Auto-Encoders (AE), Restricted Boltzmann Machine (RBM), Convolutional Neural Network (CNN), and Recurrent

Methods

Results

Conclusion