Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning

Khan, Asif; Khalid, Salman; Raouf, Izaz; Sohn, Jung-Woo; Kim, Heung-Soo

doi:10.3390/s21186239

Cited by 21 publications

(3 citation statements)

References 51 publications

(54 reference statements)

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“…The presence of a defect in laminated composites, such as delamination, affects the local stiffness/ damping, and can be simulated by inserting Teflon film between the plies of the structure to avoid localized regions between plies sticking during the curing process. 53 Another option is to alter the local properties (stiffness/damping) through the addition of local masses. 54 In the current study, the latter was employed, where the local defect was simulated via industrial adhesive putty glued to the surface of the plate at the location shown in Figure 2.…”

Section: Description Of Experimental Datamentioning

confidence: 99%

Damage assessment of laminated composites using unsupervised autonomous features

Khan,

Kim

2023

Journal of Thermoplastic Composite Materials

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This article proposes a framework for the damage assessment of and effect of temperature variations in laminated composites using Lamb waves and unsupervised autonomous features. A network of piezoelectric transducers is employed to generate data for 18 health states of a laminated composite plate. The data is processed with sparse autoencoder (SAE) for unsupervised autonomous features. The discriminative capabilities of the extracted features are confirmed by processing the feature space in the supervised and unsupervised frameworks of machine learning. The confusion matrices of supervised learning provided physical insights into the problem. The feature space was also visualized in two dimensions in an unsupervised manner through principal component analysis (PCA), which revealed physically consistent results for the effect of temperature variations, damage of different severity levels, and the undamaged paths between the actuator and sensors. The healthy state data and information on the paths between the actuator and sensors was processed via SAE for damage localization. The proposed approach can be employed for the autonomous assessment of composite structures for the presence of damage and variations of operating temperatures while using both supervised and unsupervised machine learning algorithms.

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Section: Description Of Experimental Datamentioning

confidence: 99%

Damage assessment of laminated composites using unsupervised autonomous features

Khan,

Kim

2023

Journal of Thermoplastic Composite Materials

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“…A fault is an undesirable deviation from the normal state of a system's characteristic, property, or parameter [36,37]. The fault can be categorized into sensor, actuator, and component-level faults [38].…”

Section: Sensor Malfunctioning In Avsmentioning

confidence: 99%

Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey

et al. 2022

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Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers. Improvement of passenger safety is one of the key factors for evolving AVs. An automated system provided by the ADAS in autonomous vehicles is a salient feature for passenger safety in modern vehicles. With an increasing number of electronic control units and a combination of multiple sensors, there are now sufficient computing aptitudes in the car to support ADAS deployment. An ADAS is composed of various sensors: radio detection and ranging (RADAR), cameras, ultrasonic sensors, and LiDAR. However, continual use of multiple sensors and actuators of the ADAS can lead to failure of AV sensors. Thus, prognostic health management (PHM) of ADAS is important for smooth and continuous operation of AVs. The PHM of AVs has recently been introduced and is still progressing. There is a lack of surveys available related to sensor-based PHM of AVs in the literature. Therefore, the objective of the current study was to identify sensor-based PHM, emphasizing different fault identification and isolation (FDI) techniques with challenges and gaps existing in this field.

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“…Prognostic health monitoring is a mechanism of preventive measures to deliver comprehensive and tailored solutions for the industrial health system, management and prediction. 1,2 In civil engineering, structural health monitoring (SHM) systems are often applied to largescale civil infrastructures, especially large span bridges, to measure environmental factors, external loads and structural responses. [3][4][5] Based on measurements from SHM systems, anomaly detection for large span bridges has been broadly studied all over the world within recent decades.…”

Section: Introductionmentioning

confidence: 99%

Multi-index probabilistic anomaly detection for large span bridges using Bayesian estimation and evidential reasoning

Forde

Ren

et al. 2022

Structural Health Monitoring

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To measure uncertainties within anomaly detection and distinguish sensor faults from anomalous events, a multi-index probabilistic anomaly detection approach is proposed for large span bridges based on Bayesian estimation and evidential reasoning. To avoid false detection raised by signal spikes, an energy index is first defined and extracted from pre-processed measurements, including missing data recovery and thermal response separation. Then, a probabilistic index, namely, certainty degree, is derived from probability density functions of detection triggers – extreme values predicted by using Bayesian estimation of the generalized Pareto distribution. To distinguish sensor faults from anomalous scenarios, evidential reasoning is applied to incorporate multiple certainty degrees into a joint one under the assumption that the probability of multi-sensor failing simultaneously is extremely low. Specifically, a large joint certainty degree indicates a high occurrence probability of anomalous scenarios, while a small one together with a large individual certainty degree depicts a high probability of sensor faults. Finally, the effectiveness of the proposed anomaly detection method is validated through structural health monitoring data from the Nanjing Dashengguan Yangtze River Bridge. Measurements from four sensors, that is, three cable forces and one deflection, are selected to detect anomalies based on their high pair-wise correlations. Two case studies are presented, namely, sensor fault detection and snow disaster detection. The sensor fault is detected through a certainty degree of almost 100% for the individual index and a joint certainty degree of nearly 0. The snowstorm is detected by a joint certainty degree of 36.82%.

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Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning

Cited by 21 publications

References 51 publications

Damage assessment of laminated composites using unsupervised autonomous features

Damage assessment of laminated composites using unsupervised autonomous features

Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey

Multi-index probabilistic anomaly detection for large span bridges using Bayesian estimation and evidential reasoning

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