Intelligent Fault Diagnosis for Rotating Machines Using Deep Learning

Jorge Chuya Sumba, Israel Ruiz Quinde, Luis Escajeda Ochoa, Juan Carlos Tudón Martínez, Antonio J. Vallejo Guevara, Ruben Morales-Menendez

Research output: Contribution to journalArticlepeer-review

Abstract

The diagnosis of failures in high-speed machining centers and other rotary machines is critical in manufacturing systems, because early detection can save a representative amount of time and cost. Fault diagnosis systems generally have two blocks: feature extraction and classification. Feature extraction affects the performance of the prediction model, and essential information is extracted by identifying high-level abstract and representative characteristics. Deep learning (DL) provides an effective way to extract the characteristics of raw data without prior knowledge, compared with traditional machine learning (ML) methods. A feature learning approach was applied using one-dimensional (1-D) convolutional neural networks (CNN) that works directly with raw vibration signals. The network structure consists of small convolutional kernels to perform a nonlinear mapping and extract features; the classifier is a softmax layer. The method has achieved satisfactory performance in terms of prediction accuracy that reaches ∼99 % and ∼97 % using a standard bearings database: the processing time is suitable for real-time applications with ∼8 ms per signal, and the repeatability has a low standard deviation <2 % and achieves an acceptable network generalization capability.

Original languageEnglish
Pages (from-to)27-40
Number of pages14
JournalSmart and Sustainable Manufacturing Systems
Volume3
Issue number2
DOIs
Publication statusPublished - 1 Feb 2019

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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