Damage detection using enhanced multivariate statistical process control technique

Chaabane, Marwa; Hamida, Ahmed Ben; Mansouri, Majdi; Nounou, Hazem; Avcı, Onur

doi:10.1109/sta.2016.7952052

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…11 Non-parametric global damage detection methods rely on statistical approaches to identify damages from the measured signals, 12,13 while parametric methods detect structural damages by mapping the actual damage and the damage feature. 14,15 Machine learning (ML) approaches have been introduced in the field of structural property prediction and damage detection. [16][17][18][19][20][21][22][23] Damage detection using ML techniques often requires a procedure of feature extraction followed by damage classification.…”

Section: Introductionmentioning

confidence: 99%

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Parallel convolutional neural network toward high efficiency and robust structural damage identification

Cao²,

Liu

et al. 2023

Structural Health Monitoring

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Artificial intelligence has been implemented recently for processing and analyzing monitored data for damage detection and identification in the field of structural health monitoring (SHM). Existing machine learning methods such as convolutional neural networks (CNNs) usually rely on inputs from a single domain (time or frequency), which may only provide partial information for damage identification. To address this issue, this work proposes a parallel convolutional neural network (P-CNN) that extracts multidimensional features assisted by a computer vision technique. The proposed network comprises a one-dimensional (1D) CNN branch, a two-dimensional (2D) CNN branch, and several fully connected layers. The efficiency and robustness of the proposed network were validated by a public experimental dataset. Our results show that (1) the features extracted by the P-CNN were separated more easily compared with those by 1D-CNN or 2D-CNN; (2) when detecting structural damages, the accuracy of the P-CNN is above 99.4%; (3) the P-CNN exhibits a robust performance when subjected to a high (5 dB) signal-to-noise ratio of the original data; and (4) when compared with traditionally used methods such as GoogLenet and Resnet, the P-CNN outperforms on many aspects of damage identification. We envision that the proposed P-CNN can be integrated into advanced SHM systems with high fidelity and intelligence.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallel convolutional neural network toward high efficiency and robust structural damage identification

Cao²,

Liu

et al. 2023

Structural Health Monitoring

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“…In order to monitor the structural health condition, it is necessary to deploy a monitoring system on the monitored object. SHM systems have been applied in various engineering fields, such as mechanical and civil engineering [13][14][15]. The monitoring system conducts long-term measurements on the monitored object itself and its surrounding environment [16] and analyzes the data collected by sensors to locate, identify, and quantify damage [17].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight and Efficient Method of Structural Damage Detection Using Stochastic Configuration Network

Lu,

Wang,

Liu

et al. 2023

Sensors

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With the advancement of neural networks, more and more neural networks are being applied to structural health monitoring systems (SHMSs). When an SHMS requires the integration of numerous neural networks, high-performance and low-latency networks are favored. This paper focuses on damage detection based on vibration signals. In contrast to traditional neural network approaches, this study utilizes a stochastic configuration network (SCN). An SCN is an incrementally learning network that randomly configures appropriate neurons based on data and errors. It is an emerging neural network that does not require predefined network structures and is not based on gradient descent. While SCNs dynamically define the network structure, they essentially function as fully connected neural networks that fail to capture the temporal properties of monitoring data effectively. Moreover, they suffer from inference time and computational cost issues. To enable faster and more accurate operation within the monitoring system, this paper introduces a stochastic convolutional feature extraction approach that does not rely on backpropagation. Additionally, a random node deletion algorithm is proposed to automatically prune redundant neurons in SCNs, addressing the issue of network node redundancy. Experimental results demonstrate that the feature extraction method improves accuracy by 30% compared to the original SCN, and the random node deletion algorithm removes approximately 10% of neurons.

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“…While the condition assessment and health monitoring of rotating machinery have been an area of substantial research over a number of decades, there has been relatively more focus on early detection of damage with real-time methodologies. Vibration-based defect/anomaly detection utilizing acceleration data, as in many other applications [10][11][12][13][14][15][16], has been found to be an effective way to detect, locate, and quantify defects in rotating machinery. In addition to the existing model-based methods (involving classical physics) and data-driven methods [17][18][19][20][21] (involving feature extraction and classification) available in the literature, machine learning-based methods have been recently in use for bearing defect detection.…”

Section: Introduction and Overview Of One-dimensional Cnnsmentioning

confidence: 99%

One-Dimensional Convolutional Neural Networks for Real-Time Damage Detection of Rotating Machinery

Avcı

Abdeljaber

Kıranyaz

et al. 2021

Conference Proceedings of the Society for Experimental Mechanics Series

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This paper presents a novel real-time rotating machinery damage monitoring system. The system detects, quantifies, and localizes damage in ball bearings in a fast and accurate way using one-dimensional convolutional neural networks (1D-CNNs). The proposed method has been validated with experimental work not only for single damage but also for multiple damage cases introduced onto ball bearings in laboratory environment. The two 1D-CNNs (one set for the interior bearing ring and another set for the exterior bearing ring) were trained and tested under the same conditions for torque and speed. It is observed that the proposed system showed excellent performance even with the severe additive noise. The proposed method can be implemented in practical use for online defect detection, monitoring, and condition assessment of ball bearings and other rotatory machine elements.

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Damage detection using enhanced multivariate statistical process control technique

Cited by 12 publications

References 19 publications

Parallel convolutional neural network toward high efficiency and robust structural damage identification

Parallel convolutional neural network toward high efficiency and robust structural damage identification

A Lightweight and Efficient Method of Structural Damage Detection Using Stochastic Configuration Network

One-Dimensional Convolutional Neural Networks for Real-Time Damage Detection of Rotating Machinery

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