Fault Diagnosis of Rotary Machines using Deep Convolutional Neural Network with three axis signal input

TL;DR

This paper introduces a deep learning approach using three-axis accelerometer signals as images for automatic fault diagnosis in rotary machines, reducing the need for manual feature extraction and expert intervention.

Contribution

It proposes a novel deep convolutional neural network method that directly processes raw multi-axis signals for accurate fault classification in rotary machinery.

Findings

High classification accuracy achieved

Automatically extracts features from raw signals

Reduces reliance on manual feature engineering

Abstract

Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility of an early detection and diagnosis of the faulty condition can save both time and money. Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for expert knowledge and human work. The emergence of deep learning architectures in condition-based maintenance promises to ensure high performance fault diagnosis while lowering necessity for expert knowledge and human work. This paper presents developed technique for deep learning-based data-driven fault diagnosis of rotary machinery. The proposed technique input raw three axis accelerometer signal as high-definition image into deep learning layers which automatically extract signal features, enabling high classification accuracy.