Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review

Nasiri, Sara; Khosravani, Mohammad Reza; Weinberg, Kerstin

doi:10.1016/j.engfailanal.2017.07.011

Cited by 139 publications

(80 citation statements)

References 105 publications

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“…Various studies in engineering and production management reveal the capability of CBR in comparison with other AI methods (e.g. for managing order-picking operations in warehouses (Poon et al 2009), estimation of maintenance time for complex engineered-to-order products (Mourtzis, Boli, and Fotia 2017), detection of mechanical faults (Nasiri, Khosravani, and Weinberg 2017), and decision support on diagnosis and maintenance in the aircraft domain (Reuss et al 2018)). In particular, PriMa gains benefits from the systematic approach for knowledge engineering and implementing the CBR cycle presented in (Reuss et al 2018).…”

Section: Description Of the Modelmentioning

confidence: 99%

PriMa: a prescriptive maintenance model for cyber-physical production systems

Ansari

Grepl

Nemeth

2019

International Journal of Computer Integrated Manufacturing

134

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Cyber-physical production systems (CPPS), as an emerging Industry 4.0's technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined. ARTICLE HISTORY

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Section: Description Of the Modelmentioning

confidence: 99%

PriMa: a prescriptive maintenance model for cyber-physical production systems

Ansari

Grepl

Nemeth

2019

International Journal of Computer Integrated Manufacturing

134

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“…Structural health monitoring, condition monitoring, damage and fault detection are popular topics in engineering [1] and [2]. The early detection of a failure or a fault can be taken as a synonym for the improved maintenance, safety and reliability of a mechanical system or a structure.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Highlights• Gaussian-mixture-model-based classification was applied to the bearing-fault classification. • To discriminate the faulty from non-faulty bearings only simple statistics from vibrational data was used. • Two different datasets, the Case Western Rice University dataset and Bearing vibration data collected under time-varying rotational speed conditions dataset are used. • The Gaussian-mixture-model-based classification method showed to be a competitive and efficient method.

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“…Earlier Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was used to model the damage and then the measured frequencies were compared to the predicted frequencies to determine the damage [24]. It was found that there are a lots of Artificial Intelligence (AI) techniques and approaches are available in mechanical engineering and some of the AI methods which are used in the field of fracture mechanics are Bayesian Network (BN), Fuzzy Logic (FL), Genetic Algorithm (GA), Artificial Neural Network (ANN) and Case-Based Reasoning (CBR) [25]. In the present work, the fatigue tests on adhesively bonded joints were conducted at R ratio of 0.1 and a frequency of 10 Hz.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Fatigue Crack Propagation in Adhesively Bonded Joints Using Fatigue Testing, Finite Element Analysis and Neural Networks

Gaur¹,

Prasad²

2019

Applied Engineering Letters

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The current paper presents research aiming at characterizing the fatigue behaviour of adhesively bonded joints. In this study, a new mathematical model to predict fatigue crack propagation rates for adhesively bonded joints has been investigated and presented. The proposed method uses fatigue test data and stiffness data obtained from finite element model of bonded joints. T-peel and single lap shear bonded joints were prepared using aluminium alloy AA5754 and Betamate epoxy adhesive 4601. The fatigue tests were conducted using constant amplitude loading using an R ratio of 0.1 at a frequency of 10 Hz. The FE models used in this work were developed using fracture mechanics tools in Abaqus. The results were post processed to extract energy release rates in form of J Integrals and stress intensity factors. The stiffness results obtained from both experimental testing and numerical studies were combined using appropriate curve fitting models proposed in the literature to estimate the fatigue crack propagation rates and obtained the de-bond curves in the Paris regime for such joints. The crack growth rates were further modelled and validated using neural network technique in MATLAB. ARTICLE HISTORY

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Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review

Cited by 139 publications

References 105 publications

PriMa: a prescriptive maintenance model for cyber-physical production systems

PriMa: a prescriptive maintenance model for cyber-physical production systems

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Investigation of Fatigue Crack Propagation in Adhesively Bonded Joints Using Fatigue Testing, Finite Element Analysis and Neural Networks

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