Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline's integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

Pipeline In-Line Inspection Method, Instrumentation and Data Management.

Depth resolved pulse compression favourable frequency modulated thermal wave imaging for quantitative characterization of glass fibre reinforced polymer

On the application of an optimized frequency-phase modulated waveform for enhanced infrared thermal wave radar imaging of composites

The miniaturized electronic products require not only high density, but also high reliability. Defect inspection for the high-density package becomes challenging gradually. In this article, active thermography technology is used to detect the solder balls in flip-chip (FC) packages. A single-layer FC model is constructed and heat conduction in the FC package is simulated by using the finite-element analysis code of COMSOL. Experimental investigation is also carried out to inspect the solder defects by using the laser excitation thermography test system. Infrared images of the SFA1 package are captured, and the spatial adaptive filtering algorithm is used to remove the thermal noise. The hot spots of the solder balls are segmented from the filtered infrared image, and the representative features are extracted for each hot spot. The modified learning vector quantization (LVQ) neural network is used for the classification of the solder balls. The missing solder balls are identified accurately. The simulation and experimental results prove that the proposed approach using active thermography and LVQ algorithm is effective for defect inspection in high-density electronic devices.

Simulation and Experimental Investigation on Active Thermography Test of the Solder Balls

A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.

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Deep Temporal Convolution Network for Time Series Classification

Halogen optical referred pulse-compression thermography for defect detection of CFRP

DeftectNet: Joint loss structured deep adversarial network for thermography defect detecting system

Structured iterative alternating sparse matrix decomposition for thermal imaging diagnostic system

A design of multi-mode excitation source for optical thermography nondestructive sensing

In this paper, a novel joint loss GAN is proposed in Thermography Nondestructive testing. The proposed joint loss function incorporates both the modified CGAN loss and the penalty loss where this strategy significantly improves performance of defect detection. The results show that the proposed method can better capture the defect features to improve the detection accuracy. In order to verify the effectiveness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped CFRP specimens. A comparative analysis has been undertaken between the proposed method and the state-of-the-art deep semantic segmentation algorithms. It is being proved to be suitable for the end-to-end detection Thermography diagnosis system.

Shichun Wu

Papers

Halogen optical referred pulse-compression thermography for defect detection of CFRP

DeftectNet: Joint loss structured deep adversarial network for thermography defect detecting system

Structured iterative alternating sparse matrix decomposition for thermal imaging diagnostic system

A design of multi-mode excitation source for optical thermography nondestructive sensing

Deep Adversarial Network for CFRP Thermal Imaging Debond Diagnosis