Gearbox Fault Diagnostics Using Deep Learning with Simulated Data

Gecgel, Ozhan; Ekwaro-Osire, Stephen; Dias, João Paulo; Serwadda, Abdul; Alemayehu, Fisseha M.; Nispel, Abraham

doi:10.1109/icphm.2019.8819423

Cited by 23 publications

(20 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of this, Data Transformation is an important pre-processing step in SHM and much literature dedicates to investigating it. Analyses in the Frequency domain are usually applied for stationary signals and can be obtained with Fast Fourier Transform (FFT) or bispectrum analysis [46][47][48]. Time-frequency or Wavelet domain analyses are convenient for nonstationary signals, and their methods and aspects are reviewed by Taha et al [49].…”

Section: Structural Health Monitoring By Machine Learningmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

View full text Add to dashboard Cite

The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD&V and guides the reader to an advanced understanding of progress and prospects in the field.

show abstract

Section: Structural Health Monitoring By Machine Learningmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Although data on the healthy state of a system is generally available in sufficient amounts, data on different defective states can be limited or even completely unavailable due to the high cost associated with running the machinery in the presence of defects. To make up for the dearth of failure data from different defective states of expensive machinery, computer simulations can be employed to generate a sufficient amount of healthy and faulty data using simplified mathematical models of the actual machinery [29]. However, there are gaps between the data from simulations and actual systems and a labor-intensive process is required to identify the parameters of the actual system and tune those parameters to bring the response characteristics of the simulation model closer to those of the actual system [30].…”

Section: Introductionmentioning

confidence: 99%

Damage Detection and Isolation from Limited Experimental Data Using Simple Simulations and Knowledge Transfer

Khan

Kim

2021

Mathematics

View full text Add to dashboard Cite

A simulation model can provide insight into the characteristic behaviors of different health states of an actual system; however, such a simulation cannot account for all complexities in the system. This work proposes a transfer learning strategy that employs simple computer simulations for fault diagnosis in an actual system. A simple shaft-disk system was used to generate a substantial set of source data for three health states of a rotor system, and that data was used to train, validate, and test a customized deep neural network. The deep learning model, pretrained on simulation data, was used as a domain and class invariant generalized feature extractor, and the extracted features were processed with traditional machine learning algorithms. The experimental data sets of an RK4 rotor kit and a machinery fault simulator (MFS) were employed to assess the effectiveness of the proposed approach. The proposed method was also validated by comparing its performance with the pre-existing deep learning models of GoogleNet, VGG16, ResNet18, AlexNet, and SqueezeNet in terms of feature extraction, generalizability, computational cost, and size and parameters of the networks.

show abstract

“…They extract the data from its principal component and adopt the wavelet neural network to carry out a diagnosis and analysis on the feature data sample set. The image processing problem solved by CNN can also be applied for fault detection and get the ideal result [19]. T. Benkedjouh et al [20] describe a Short-Time Fourier Transform (STFT) to process the original data sent by the aeroengine, and make use of CNNs to learn it and determine its fault information.…”

Section: Introductionmentioning

confidence: 99%

SVM-BiLSTM: A Fault Detection Method for the Gas Station IoT System Based on Deep Learning

et al. 2020

View full text Add to dashboard Cite

In this paper, a bi-directional long-short term memory (BiLSTM) network algorithm combined with a support vector machine (SVM), SVM-BiLSTM, is proposed to detect faults in the Gas Station Internet of Things (GS-IoT) system. The operational process data in the GS-IoT System, which is collected from the edge of the IoT gateways, is compared with the human emotional reaction behavioral mechanism data. A word segmentation method is invented to map the collected data to a low dimensional space, which makes the data processing relatively easier while retaining the intrinsic information of the data. In order to deal with a certain correlation among the data of the GS-IoT system, the BiLSTM algorithm is used to analyze the abnormal data and find types of faults. Since the structure of the BiLSTM is complex and its calculation is slow, we design a novel method which leverages SVM to increase the detection efficiency. We also compare the performance of the proposed algorithm with Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Knowledge-Based System (KBS), pure SVM and BiLSTM. The results show that the proposed algorithm is able to detect faults with more efficiency and accuracy in the GS-IoT system.

show abstract

Gearbox Fault Diagnostics Using Deep Learning with Simulated Data

Cited by 23 publications

References 27 publications

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Damage Detection and Isolation from Limited Experimental Data Using Simple Simulations and Knowledge Transfer

SVM-BiLSTM: A Fault Detection Method for the Gas Station IoT System Based on Deep Learning

Contact Info

Product

Resources

About