Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Abdeljaber, Osama; Avcı, Onur; Kıranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

doi:10.1016/j.jsv.2016.10.043

Cited by 982 publications

(505 citation statements)

References 28 publications

Supporting

Mentioning

501

Contrasting

Unclassified

Order By: Relevance

“…Since 2015, deep learning methodologies have been applied, with success, to diagnostics or classification tasks of rolling element signals [2,[16][17][18][19][20][21][22][23][24][25][26]. Wang et al [2] proposed the use of wavelet scalogram images as an input into a CNN to detect faults within a set of vibration data.…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al [17] used Case Western's bearing data set [4] and an adaptive deep CNN to accomplish fault diagnosis and severity. Abdeljaber et al [19] used a CNN for structural damage detection on a grandstand simulator. Janssens et al [21] incorporated shallow CNNs with the amplitudes of the discrete Fourier transform vector of the raw signal as an input.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

301

198

View full text Add to dashboard Cite

Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Timefrequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

301

198

View full text Add to dashboard Cite

show abstract

“…This simplified the architecture and minimized both training and processing time. Fault diagnosis for other mechanical systems was addressed by others: the logistic regression method for a real elevator door system [112]; a Naïve Bayes scheme and Bayes net approach to monoblock centrifugal pump systems [113]; SAE for rotating machines and hydraulic pumps [114,115]; PKFA for machine tools [116] and the framework, DNN for tidal turbine generators [117], the DL method for wind generators [118], and CNNs for a structural damage detection system [119]. In addition, these above-mentioned references were displayed several problem-solving revolutions of mechanical components.…”

Section: Other Mechanical Componentsmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

View full text Add to dashboard Cite

This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

show abstract

“…A vibration technology of combining advanced sensors with intelligent algorithms to continually interrogate structural health condition and provide real-time and instant damage detection can be achieved in structural health monitoring (SHM) [10]. The vibration technology can be analyzed using different approaches and parameters, i.e., natural frequency analysis, scale wavenumber, Fourier Spectral Method, mode and shape method [11][12][13][14][15][16][17][18]. This technique has potential benefits of improving safety, reliability, reducing lifecycle costs and assists in the design of composites structures.…”

Section: Related Literaturementioning

confidence: 99%

The Continuous Monitoring of the Health of Composite Structure

Masango

Philander

Msomi

2018

Journal of Engineering

View full text Add to dashboard Cite

Currently nondestructive testing techniques for composite aircraft structures are disadvantaged when compared to instant structural health monitoring (SHM) systems that monitor the structure while being in-service and give real-time data. This paper reports on the use of Polyvinylidene Fluoride (PVDF) sensors in sensing (or monitoring) and locating the defect of a flexible composite structure. The samples used for the tests were manufactured through the use of vacuum infusion process. The threepoint bending test was performed to determine the material properties. The vertical sample deflection was measured through the use of vertical height Vernier. It should be noted that the samples were analyzed as cantilever beam due to limited availability of test equipment. The sample health was monitored through the use of PVDF sensor. The sensor data was logged and recorded through the use of Fluke-View scopemeter. The 50 g mass pieces were used as a mode of subjecting the structure to the vertical load. The experiment was performed on samples with defects (drilled 3 mm hole) sample and no-defected sample. The deflection and output voltage from the PVDF sensor of all the samples were comparatively studied.

show abstract

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Cited by 982 publications

References 28 publications

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

The Continuous Monitoring of the Health of Composite Structure

Contact Info

Product

Resources

About