Borescope Images Research Articles

Accelerated fixed-point iterative reconstruction for fiber borescope imaging.

Computational imaging systems with embedded processing have potential advantages in power consumption, computing speed, and cost. However, common processors in embedded vision systems have limited computing capacity and low level of parallelism. The widely used iterative algorithms for image reconstruction rely on floating-point processors to ensure calculation precision, which require more computing resources than fixed-point processors. Here we present a regularized Landweber fixed-point iterative solver for image reconstruction, implemented on a field programmable gated array (FPGA). Compared with floating-point embedded uniprocessors, iterative solvers implemented on the fixed-point FPGA gain 1 to 2 orders of magnitude acceleration, while achieving the same reconstruction accuracy in comparable number of effective iterations. Specifically, we have demonstrated the proposed fixed-point iterative solver in fiber borescope image reconstruction, successfully correcting the artifacts introduced by the lenses and fiber bundle.

A compact LWIR borescope sensor for 2D engine component surface temperature measurement

A compact long-wavelength infrared (LWIR) borescope imaging sensor operating in the wavelength range of 8–14 μm is designed and developed for non-contact two-dimensional (2D) surface temperature measurements in gas-turbine engines. The LWIR detection minimizes optical interferences from hot combustion gases and soot (emission within UV–mid IR region). Using simulations, the system is optimized for improving the signal collection efficiency and minimize image aberrations. The LWIR borescope probe is shielded by the custom-build compact water-cooled probe housing (outer diamter (OD) 19 mm, inner diameter (ID) 10 mm), which can sustain flame temperature up to 2400 K at a pressure of 50 bar. It facilitates long-term optical diagnostics inside the actual high-pressure combustion facilities where extreme thermal acoustic perturbation and intense heat fluxes are encountered. The design, construction, and characterization of the LWIR borescope sensor are discussed in detail. We present the 2D surface temperature measurements of a V-gutter bluff-body with simple nitrogen gas cooling in a laboratory testbed. The results indicate the sensor’s ability to accurately measure surface temperature with low background noise and to track transient cooling effect. The water-cooled probe housing was tested to verify the effectiveness of fast heat dissipation in high-temperature combustion environment to avoid unwanted thermal signals from the LWIR probe window. The developed LWIR sensor suite has promising applications in surface temperature measurements of engine components. The findings of this study may aid propulsion system engineers and researchers in designing thermal management systems and optimizing operation.

An Aero-Engine Damage Detection Method with Low-Energy Consumption Based on Multi-Layer Contrastive Learning

The health of aero-engines is pivotal to the safe operation of aircraft. With increasing service time, the internal components of the engine will be damaged by threats from different sources, so it is necessary to regularly detect the damage inside the engine. At present, most of the detection methods of major airlines rely on the internal images of the engine obtained by manual use of a borescope to detect damage or traditional machine learning methods, which consume high levels of human and computational resources but have low efficiency. Artificial intelligence in various fields can achieve better performance than traditional methods, but to achieve the industrialization standard of Green AI, we need further research. Accordingly, we introduce a multi-layer contrastive learning method to a lightweight target detection model design, which is applied to real aero-engine borescope images of complex components to accomplish real-time damage detection. We intensively conduct comparative experiments to evaluate the effectiveness of our method. The verification results demonstrate that the method can help our model perform excellently compared with other available baseline models.

Semantic Damage Segmentation for Aircraft Engine Borescope Videos

Due to extremely high temperatures, friction, and vibrations, aircraft engines tend to have various types of internal damage. To guarantee the safety of the aircraft, maintenance for aircraft engines is regularly performed by manual borescope inspection, which is time consuming and error prone. Existing studies adopted deep learning-based approaches to detect potential damage in borescope images to accelerate the process of engine maintenance. However, these approaches are designed for damage detection from static images and cannot be directly used to track damage in borescope videos due to the extremely high computational cost. To detect and track the damage in borescope videos in real time, we propose the deep fusion network (DFNET), which works along two parallel functional paths: i.e., the segmentation path and the spatial warping path. The segmentation path only runs on selected key frames to extract semantic features, which are then propagated to other frames through optical flows in the spatial warping path. The performance and efficiency of the DFNET are validated through extensive experiments using real borescope videos from a local air carrier.

Intelligent Damage Detection for Aircraft Engine with Context Encoder Neural Networks

It is vital and essential to accurately and timely detect various damages of aircraft engines in civil aviation. Currently, aircraft engines are manually inspected via borescope images by aircraft maintenance technicians. This process is time-consuming and prone to error due to human factors. The aim of this paper is to automate the aircraft engine inspection, and this work presents a deep learning framework with a context encoder neural network structure such that the damaged structures can be accurately segmented from borescope images. Moreover, the proposed network structure is further optimized through an orthogonal-array-based method. With the real borescope images collected from a commercial airline company, the proposed framework is compared with existing deep-learning-based methods from various aspects. The experimental results validate that various damages can be automatically detected and recognized with high accuracy and efficiency by the proposed solution.

The impact of intake pressure on high exhaust gas recirculation low-temperature compression ignition engine combustion using borescopic imaging

In diesel engines, high levels of exhaust gas recirculation can be used to achieve low-temperature combustion, resulting in low emission levels of both nitrogen oxides (NO x) and particulate matter. This work studied the effects of varying the intake manifold pressure on in-cylinder combustion processes and engine-out emissions from a light-duty single cylinder diesel engine under conventional and high exhaust gas recirculation low-temperature combustion regimes. The work was conducted at a part-load cruise condition of 1500 r/min and at an indicated mean effective pressure of approximately 600 kPa. Exhaust gas recirculation rates were varied between 0% and 65% at absolute intake pressures of 100–150 kPa. Very low NO x emissions were achieved (<10 ppm, ∼0.05 g/kW h) for intake oxygen mass fractions below about 11%, independent of boost pressure. Smoke emission levels were lower than for non–exhaust gas recirculation combustion at oxygen mass fractions below ∼9%, depending on the boost pressure. High intake pressures reduced fuel consumption by 15% and combustion by-product emissions by 50%–60% compared to low boost. For the low intake boost case, little visible flame was apparent through borescope imaging. At higher boost pressures, intense flame luminosity was observed within the piston bowl early in the expansion stroke. Spatially averaged soot luminosity based on photomultiplier tube data showed that peak soot luminosity was five times greater and occurred 8 °CA earlier for high boost. This work demonstrates how the combination of appropriate boost pressures and exhaust gas recirculation rates can be used to mitigate the emissions and thermal efficiency penalties of high-dilution low-temperature combustion to achieve near-zero NO x operation.

Cookoff experiments of a melt cast explosive (Comp-B3)

Validated models of melt cast explosives exposed to accidental fires are essential for safety analysis. In the current work, we provide several experiments that can be used to develop and validate cookoff models of melt cast explosives such as Comp-B3 composed of 60:40 wt% RDX:TNT. We present several vented and sealed experiments from 2.5 mg to 4.2 kg of Comp-B3 in several configurations. We measured pressure, spatial temperature, and ignition time. Some experiments included borescope images obtained during both vented and sealed decomposition. We observed the TNT melt, the suspension of RDX particles in the melt, bubble formation caused by RDX decomposition, and bubble-induced mixing of the suspension. The RDX suspension did not completely dissolve, even as temperatures approached ignition. Our results contrast with published measurements of RDX solubility in hot TNT that suggest RDX would be completely dissolved at these high temperatures. These different observations are attributed to sample purity. We did not observe significant movement of the two-phase mixture until decomposition gases formed bubbles. Bubble generation was inhibited in our sealed experiments and suppressed mixing.

Deep Neural Networks Analysis of Borescope Images

This paper presents results on applying deep neural networks to analysis of images from borescope inspections of large turbofan engines, carried out in the field. Such inspections are done as a part of routine monitoring and maintenance as well as an initial, investigative response to alerts from automatic monitoring systems, pilots or engineers. Across GE’s commercial engines fleet, a substantial number of images have been gathered this way. The deep learning techniques that have come out of computer vision and machine learning research in the last decade offer new possibilities for analyzing and mining such data collections. This paper presents initial results on separating borescope images from images created with regular digital cameras, as well as classifying images containing various engine parts, with average accuracy of 95% and 77%, respectively, on unseen validation data.

Effect of fuel injector deposit on spray characteristics, gaseous emissions and particulate matter in a gasoline direct injection engine

For modern gasoline direct injection (GDI) engines, injector deposit is a concern because it can cause changes to the spray characteristics and lead to deterioration in fuel economy and exhaust emissions. In this study, in order to examine the link between spray variation and engine emissions deterioration due to injector deposit accumulation, 8 new injectors were installed on a GDI engine and run through a deposit accumulation process which included 6 cold starts and a 30-h steady state engine test at a speed of 2000rpm and load of 5bar break mean effective pressure (BMEP). One representative injector was examined before and after the deposit accumulation tests in order to understand the impact of deposit on the spray. Results showed that, at the end of the deposit accumulation test, the pulse width of the injectors stabilized at a level which was about 1.5% higher than at the start and the fuel consumption remained almost identical. High magnification and borescope imaging indicated that a significant amount of deposit had formed on the outer surface of the injector tip. However, Scan Electronic Microscope (SEM) imaging of the injector hole showed that, at this level of fouling, some deposit was present on the counterbore, while the nozzle hole was nearly completely unaffected. The deposit on the counterbore caused a 2.21% drop of the injector fuel flow rate at 150bar injection pressure. Penetration lengths and mean droplet sizes of all jets increased significantly. As for the impacts of the varied spray characteristics on the engine emissions, unburnt hydrocarbons (HC) and particulate matter (PM) emissions significantly increased while other gaseous emissions (e.g. CO, NOx, CO2) only changed slightly.

Modeling the measured effect of a nitroplasticizer (BDNPA/F) on cookoff of a plastic bonded explosive (PBX 9501)

We have used a modified version of the Sandia Instrumented Thermal Ignition (SITI) experiment to develop a pressure-dependent, five-step ignition model for a plastic bonded explosive (PBX 9501) consisting of 95wt% octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazoncine (HMX), 2.5wt% Estane® 5703 (a polyurethane thermoplastic), and 2.5wt% of a nitroplasticizer (NP): BDNPA/F, a 50/50wt% eutectic mixture bis(2,2-dinitropropyl)-acetal (BDNPA) and bis(2,2-dinitropropyl)-formal (BDNPF). The five steps include desorption of water, decomposition of the NP to form NO2, reaction of the NO2 with Estane® and HMX, and decomposition of HMX. The model was fit using our experiments and successfully validated with experiments from five other laboratories with scales ranging from about 2g to more than 2.5kg of PBX. Our experimental variables included density, confinement, free gas volume, and temperature. We measured internal temperatures, confinement pressure, and ignition time. In some of our experiments, we used a borescope to visually observe the decomposing PBX. Our observations included the endothermic β–δ phase change of the HMX, a small exothermic temperature excursion in low-density unconfined experiments, and runaway ignition. We hypothesize that the temperature excursion in these low density experiments was associated with the NP decomposing exothermically within the PBX sample. This reactant-limited temperature excursion was not observed with our thermocouples in the high-density experiments. For these experiments, we believe the binder diffused to the edges of our high density samples and decomposed next to the highly conductive wall as confirmed by our borescope images.

Depth-of-Field Model for Size Measurement Using a Borescope Imaging Technique under High Object Concentrations

The direct imaging technique has been a common technique for size distribution measurement in multiphase systems. A statistical reconstruction method was developed in a previous study to determine the actual size distribution from image size measurements. The method accounts for the change in magnification scale within the depth-of-field (DOF) of the imaging device. It is anticipated that the DOF will reduce at high object concentrations. Since accurate DOF information is necessary for the application of the reconstruction method, the effect of object concentration on the DOF is examined in this study. A DOF model is developed to predict the DOF under various object concentrations. The applicability of the DOF model in the statistical reconstruction method is verified experimentally. A theoretical analysis is also performed to discuss the effect of having objects whose image sizes are larger than the maximum viewable limit of the imaging device.

Recognition of Crack Damages Based on Wavelet Transforms

To recognize whether there exist crack damages in the inner structure of aircraft engine based on the borescope images, a method is presented by estimating the coarse feature of edges extracted from borescope images. The method first decomposes an edge into the approximation and details using wavelet transform at all of the possible scales. For the coefficients of the wavelet transform, the logarithm entropy is presented to represent the respective strength of the approximation and details, and the ratio of the two logarithm entropies is defined as coarseness factor. The coarseness factor can be used to estimate the coarseness of an edge. The greater the coarseness factor is, the coarser the edge is. Experiment results show that using coarseness factor as recognition feature of borescope images, the crack damages of the inner structure of aircraft engine can be recognized successfully.

Thermochromic liquid crystal temperature measurements through a borescope imaging system

Thermochromic liquid crystals (TLCs) have proven to be a valuable tool for the collection of full-field, high-resolution heat transfer data. This paper presents an extension of previously developed calibration techniques to a simplified transonic linear cascade for a highly cambered turbine blade geometry. This required the introduction of miniature periscopes to image the measurement surfaces. The procedures and equipment used to ensure high-accuracy wide-band TLC measurements are presented. These included a geometry-matched calibration device, mechanisms to accurately position the borescope imaging optics, an algorithm to automatically divide the imaging region into a large number of calibration subregions (termed as cells), and algorithms to correct for geometric and optical image distortions. The cell calibration approach is shown to halve calibration times and dramatically reduce memory requirements when compared to a pixel-by-pixel calibration. The results of an extensive validation study are presented.