Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

Cheng, Liangliang; Yaghoubi, Vahid; Paepegem, Wim Van; Kersemans, Mathias

doi:10.1177/1475921720979707

Cited by 8 publications

(11 citation statements)

References 29 publications

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“…The classification performance may be able to be improved by applying different signal processing techniques suited to the characteristics of data. In addition, some improved MTS methods such as MCS [15] and IMCS [16] have been developed, as described in Section 2. They can be evaluated with the traditional MD classifiers and the machine learning methods, as well.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the MTS still has the limitation of choosing optimal factors among all the variables [8,11], and so some studies integrated to MTS a feature selection such as genetic algorithm (GA) [12], particle swarm optimization [13], and ant colony optimization [14]. In particular, to improve the MTS process, Chen et al developed two-stage Mahalanobis classification system (MCS) [15] and the integrated MCS (IMCS) [16]. In this paper, we focused on traditional MDC and MTS methods as one-class classifiers to compare their performance with binary classifiers according to the varying imbalanced ratio in detecting the fault of rotating machines based on the preprocessed vibration data.…”

Section: Related Workmentioning

confidence: 99%

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Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System

2021

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Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed by analyzing signal data such as vibration and noise. In this study, to detect failures based on vibration data, preprocessing was performed using signal processing techniques such as the Hamming window and the cepstrum transform. After that, 10 statistical condition indicators were extracted to train the machine learning models. Specifically, two types of Mahalanobis distance (MD)-based one-class classification methods, the MD classifier and the Mahalanobis–Taguchi system, were evaluated in detecting the faults of rotating machinery. Their performance for fault detection on rotating machinery was evaluated with different imbalanced ratios of data by comparing with binary classification models, which included classical versions and imbalanced classification versions of support vector machine and random forest algorithms. The experimental results showed the MD-based classifiers became more effective than binary classifiers in cases in which there were much fewer defect data than normal data, which is often common in the real-world industrial field.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System

2021

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“…Although PCA and AE models are used in this study, other ML models can also be integrated into this framework, such as the Mahalanobis classification system (MCS). 31 In step 3.2, the physical distance between pairs of junctions is obtained by using Dijkstra’s 32 shortest pathfinding algorithm. Other shortest path algorithms could also be considered when dealing with different types of graphs, such as Floyd 33 –Warshall algorithm.…”

Section: Methodsmentioning

confidence: 99%

An innovative machine learning based framework for water distribution network leakage detection and localization

Fan

2021

Structural Health Monitoring

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Leakages in the underground water distribution networks (WDNs) waste over 1 billion gallon of water annually in the US and cause significant socio-economic loss to our communities. However, detecting and localization leakage in a WDN remains a challenging technical problem despite of significant progresses in this domain. The progresses in machine learning (ML) provides new ways to identify the leakage by data-driven methods. However, in-service WDNs are short of labeled data under leaking conditions, which makes it infeasible to use common ML models. This study proposed a novel machine learning (ML)-based framework for WDN leak detection and localization. This new framework, named clustering-then-localization semi-supervised learning (CtL-SSL), uses the topological relationship of WDN and its leakage characteristics for WDN partition and sensors placement, and subsequently utilizes the monitoring data for leakage detection and leakage localization. The CtL-SSL framework is applied to two testbed WDNs and achieves 95% leakage detection accuracy and around 83% final leakage localization accuracy by use of unbalanced data with less than 10% leaking data. The developed CtL-SSL framework advances the leak detection strategy by alleviating the data requirements, guiding optimal sensor placement, and locating leakage via WDN leakage zone partition. It features excellent scalability, extensibility, and upgradeability for applications to various types of WDNs. It will provide valuable a tool in sustainable management of the WDNs.

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“…The cooling channel in the middle can be seen in the transparent view. By using one actuator and two sensors, that is, Single Input Multiple Output SIMO, the amplitude of the frequency response function (|FRF|) has been collected from each blade in the range of [3,38] kHz at 11,253 frequency lines, see Figure 4. To create a database, 150 healthy and 79 defected blades have been measured.…”

Section: Application To First-stage Turbine Bladesmentioning

confidence: 99%

“…1 Although ML-based methods have become very popular among researchers, 1 their performances are dependent on not only the quality of vibration data but also the features extracted from the vibration data, the features selected to train the classifiers, and the classification methodology used for pattern recognition. Therefore, to improve the performance of these ML-based methods, one could either use proper feature selection techniques 2,3 or employ suitable pattern recognition methods. 4 However, it is well known that these methods cannot give high accuracies for all applications and datasets, depending on the level of outliers, noise, errors, nonlinearities, and data redundancy present in the data.…”

Section: Introductionmentioning

confidence: 99%

CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for vibration-based fault recognition

Yaghoubi

Cheng

Paepegem

et al. 2022

Structural Health Monitoring

Self Cite

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Nowadays, using vibration data in conjunction with pattern recognition methods is one of the most common fault detection strategies for structures. However, their performances depend on the features extracted from vibration data, the features selected to train the classifier, and the classifier used for pattern recognition. Deep learning facilitates the fault detection procedure by automating the feature extraction and selection, and classification procedure. Though, deep learning approaches have challenges in designing its structure and tuning its hyperparameters, which may result in a low generalization capability. Therefore, this study proposes an ensemble deep learning framework based on a convolutional neural network (CNN) and Dempster–Shafer theory (DST), called CNN-DST. In this framework, several CNNs with the proposed structure are first trained, and then, the outputs of the CNNs selected by the proposed technique are combined by using an improved DST-based method. To validate the proposed CNN-DST framework, it is applied to an experimental dataset created by the broadband vibrational responses of polycrystalline nickel alloy first-stage turbine blades with different types and severities of damage. Through statistical analysis, it is shown that the proposed CNN-DST framework classifies the turbine blades with an average prediction accuracy of 97.19%. The proposed CNN-DST framework is benchmarked with other state-of-the-art classification methods, demonstrating its high performance. The robustness of the proposed CNN-DST framework with respect to measurement noise is investigated, showing its high noise-resistance. Further, bandwidth analysis reveals that most of the required information for detecting faulty samples is available in a small frequency range.

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Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

Cited by 8 publications

References 29 publications

Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System

Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System

An innovative machine learning based framework for water distribution network leakage detection and localization

CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for vibration-based fault recognition

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