Crack Detection Zones: Computation and Validation

Pfingstl, Simon; Steiner, Martin; Tusch, Olaf; Zimmermann, M. B.

doi:10.3390/s20092568

Cited by 7 publications

(11 citation statements)

References 37 publications

(39 reference statements)

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“…, 2020). Moreover, researchers developed methods that use single sensor data to discover errors in numerous spots, reducing the overall inspection work in defect identification (Pfingstl et al. , 2020).…”

Section: Rq1: What Are the Domains That Use Anomaly Detection For Pre...mentioning

confidence: 99%

Review of fault detection techniques for predictive maintenance

Divya

Bhasi

Kumar

2022

JQME

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PurposeThis study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.Design/methodology/approachFor conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.FindingsBased on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.Originality/valuePapers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

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Section: Rq1: What Are the Domains That Use Anomaly Detection For Pre...mentioning

confidence: 99%

Review of fault detection techniques for predictive maintenance

Divya

Bhasi

Kumar

2022

JQME

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“…Anomaly detection has been applied to various application fields such as detecting cracks in aircraft structures [25,26], fraud detection in commercial organizations [27], or finding anomalies in biological data [28], to name only a few. Often, researchers used Gaussian models in order to detect anomalous behavior.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

Trauer

Pfingstl

Finsterer³

et al. 2021

Sustainability

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Industry 4.0, cyber-physical systems, and digital twins are generating ever more data. This opens new opportunities for companies, as they can monitor development and production processes, improve their products, and offer additional services. However, companies are often overwhelmed by Big Data, as they cannot handle its volume, velocity, and variety. Additionally, they mostly do not follow a strategy in the collection and usage of data, which leads to unexploited business potentials. This paper presents the implementation of a Digital Twin module in an industrial case study, applying a concept for guiding companies on their way from data to value. A standardized use case template and a procedure model support the companies in (1) formulating a value proposition, (2) analyzing the current process, and (3) conceptualizing a target process. The presented use case entails an anomaly detection algorithm based on Gaussian processes to detect defective products in real-time for the extrusion process of aluminum profiles. The module was initially tested in a relevant environment; however, full implementation is still missing. Therefore, technology readiness level 6 (TRL6) was reached. Furthermore, the effect of the target process on production efficiency is evaluated, leading to significant cost reduction, energy savings, and quality improvements.

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“…To apply models on a prognostic health monitoring example, a sequential data set, which shows the deterioration of the structure, has to be collected in advance. Methods for sensor placing can be found, for example, in [18,19,20] and examples for finding the most appropriate sensors for the condition indicator in [21,22]. It is possible to combine measurement data into a single condition indicator, a so-called damage index.…”

Section: Sequential Data Of Crack Growthmentioning

confidence: 99%

Predicting Crack Growth and Fatigue Life with Surrogate Models

Pfingstl¹,

Rios²,

Baier³

et al. 2020

Preprint

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Fatigue-induced damage is still one of the most uncertain failures in structural systems. Prognostic health monitoring together with surrogate models can help to predict the fatigue life of a structure. This paper demonstrates how to combine data from previously observed crack evolutions with data from the currently observed structure in order to predict crack growth and the total fatigue life. We show the application of one physics-based model, which is based on Paris' law, and four mathematical surrogate models: recurrent neural networks, Gaussian process regression, k-nearest neighbors, and support vector regression. For a coupon test, we predict the time to failure and the crack growth with confidence intervals. Moreover, we compare the performance of all proposed models by the mean absolute error, coefficient of determination, mean of log-likelihood, and their confidence intervals. The results show that the best mathematical surrogate models are Gaussian process regression and recurrent neural networks. Furthermore, this paper shows that the mathematical surrogate models tend to have conservative confidence intervals, whereas the physics-based model exhibits overly optimistic (too small) confidence intervals.

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Crack Detection Zones: Computation and Validation

Cited by 7 publications

References 37 publications

Review of fault detection techniques for predictive maintenance

Review of fault detection techniques for predictive maintenance

Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

Predicting Crack Growth and Fatigue Life with Surrogate Models

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