An acoustic emission based structural health monitoring approach to damage development in solid railway axles

Carboni, M.; Crivelli, Davide

doi:10.1016/j.ijfatigue.2020.105753

Cited by 41 publications

(27 citation statements)

References 23 publications

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“…The analysis highlighted, as relevant features for the present case, the amplitude, the energy, the rise time, the duration, the counts and the frequency centroid (Fig. 3, [17]). Other considered features did not prove to be significant for the classification of this particular case.…”

Section: Experimental Set-upmentioning

confidence: 61%

Acoustic emission-based monitoring of fatigue damage in CFRP-CFRP adhesively bonded joints

Carboni

Bernasconi

2022

insight

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Adhesively bonded joints are being increasingly applied in modern structures. However, manufacturing defects and particularly harsh operative conditions may cause local debonding and catastrophic failures. Structural health monitoring (SHM) and non-destructive testing (NDT) procedures are then needed to evaluate the in-service structural integrity of adhesively bonded joints. In this research, an adhesively bonded single-lap joint, both adherends of which are manufactured using a carbon fibre-reinforced polymer (CFRP) composite, is subjected to constant amplitude fatigue loading. During such a test, the integrity and damage condition of the joint is continuously monitored using acoustic emission (AE), while the test itself is periodically interrupted in order to apply micro-computed tomography (μCT) to the specimen, with the aim of investigating the real features of the developing fatigue damage. The results show that, after suitable elaboration and filtering by means of pattern recognition algorithms, acoustic emission-based monitoring allows for effective identification and characterisation of the development of fatigue damage in adhesively bonded joints.

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Section: Experimental Set-upmentioning

confidence: 61%

Acoustic emission-based monitoring of fatigue damage in CFRP-CFRP adhesively bonded joints

Carboni

Bernasconi

2022

insight

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“…Lucero et al [6] proposed an axle crack detection method combining vibration signal characteristic index and RF algorithm, and studied the influence of sensor position on crack recognition rate. Carboni and Crivelli [7] diagnosed axle fretting fatigue cracks based on the k-SOM automatic classifier and wheelset acoustic emission signals.…”

Section: Introductionmentioning

confidence: 99%

Multi-fault Classification of Train Wheelset System

Xiong

Jiang

et al. 2022

J. Phys.: Conf. Ser.

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The condition monitoring and faults diagnosis of wheelset are of great significance for the safe and stable operation of trains. Aiming at the axle crack, wheel flat, wheel out-of-roundness and their coupling faults in wheelset system, this paper proposes a method to diagnose these multiple faults simultaneously. Firstly, the dynamic model of a wheelset system with flat and out-of-roundness is established in SIMPACK software to analyze the fault characteristics of these two typical wheel damages. And the wheel flat and out-of-roundness are equivalent to displacement excitation and then loaded into the finite element (FE) model of the cracked wheelset. The vibration responses of the cracked wheelset with flat and out-of-roundness are solved in ABAQUS software. Then, the simulated multi-fault vibration data of wheelset are input into a Light-GBM model to realize the faults classification of axle crack, wheel flat, wheel out-of-roundness and their coupling faults. Finally, the wheelset vibration signals under the conditions of health, axle crack, wheel flat, and crack-flat coupling fault are collected from an experimental bench to verify the proposed method. This work can provide a basis for multi-fault classification and on-line health monitoring of trains.

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“…Most PHM studies focus on critical machinery components and infrastructures including bearings, 4,5 gears, 6,7 batteries, 8,9 bridges, 10,11 and railway. 12 However, these studies are mainly developed based on conventional machine learning models with shallow configurations. Achieving a satisfactory performance may require large amounts of time and expertise as domain knowledge to manually design features.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties

Caceres

González

Zhou

et al. 2021

Struct Control Health Monit

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Deep learning-based approach has emerged as a promising solution to handle big machinery data from multi-sensor suites in complex physical assets and predict their remaining useful life (RUL). However, most recent deep learningbased approaches deliver a single-point estimate of RUL as these models represent the weights of a neural network as a deterministic value and hence cannot convey uncertainty in the RUL prediction. This practice usually provides overly confident predictions that might cause severe consequences in safetycritical industries. To address this issue, this paper proposes a probabilistic Bayesian recurrent neural network (RNN) for RUL prognostics considering epistemic and aleatory uncertainties. The epistemic uncertainty is handled by Bayesian RNN layers as extensions from the Frequentist RNN layers using the Flipout method. The aleatory uncertainty is covered by a probabilistic output that follows a Gaussian distribution parameterized by the two neurons in the output layer. The network is trained using Bayes by backprop with the Flipout method. The proposed model is demonstrated by the open-access Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of turbofan engines and a comparative study of the Frequentist RNN counterparts, the Monte Carlo Dropout-based RNN, and the state-of-the-art models for C-MAPSS datasets. The results demonstrate the promising performance and robustness of the proposed model in RUL prognostics.

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An acoustic emission based structural health monitoring approach to damage development in solid railway axles

Cited by 41 publications

References 23 publications

Acoustic emission-based monitoring of fatigue damage in CFRP-CFRP adhesively bonded joints

Acoustic emission-based monitoring of fatigue damage in CFRP-CFRP adhesively bonded joints

Multi-fault Classification of Train Wheelset System

A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties

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