Stuart Crichton

State Detection of Bond Wires in IGBT Modules Using Eddy Current Pulsed Thermography

Insulated gate bipolar transistor (IGBT) modules are important safety critical components in electrical power systems. Bond wire lift-off, a plastic deformation between wire bond and adjacent layers of a device caused by repeated power/thermal cycles, is the most common failure mechanism in IGBT modules. For the early detection and characterization of such failures, it is important to constantly detect or monitor the health state of IGBT modules, and the state of bond wires in particular. This paper introduces eddy current pulsed thermography (ECPT), a nondestructive evaluation technique, for the state detection and characterization of bond wire lift-off in IGBT modules. After the introduction of the experimental ECPT system, numerical simulation work is reported. The presented simulations are based on the 3-D electromagnetic-thermal coupling finite-element method and analyze transient temperature distribution within the bond wires. This paper illustrates the thermal patterns of bond wires using inductive heating with different wire statuses (lifted-off or well bonded) under two excitation conditions: nonuniform and uniform magnetic field excitations. Experimental results show that uniform excitation of healthy bonding wires, using a Helmholtz coil, provides the same eddy currents on each, while different eddy currents are seen on faulty wires. Both experimental and numerical results show that ECPT can be used for the detection and characterization of bond wires in power semiconductors through the analysis of the transient heating patterns of the wires. The main impact of this paper is that it is the first time electromagnetic induction thermography, so-called ECPT, has been employed on power/electronic devices. Because of its capability of contactless inspection of multiple wires in a single pass, and as such it opens a wide field of investigation in power/electronic devices for failure detection, performance characterization, and health monitoring.

Chromatic Illumination Discrimination Ability Reveals that Human Colour Constancy Is Optimised for Blue Daylight Illuminations

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed.

Effect of maturation and freezing on quality and drying kinetics of beef

ABSTRACT The quality of dried meat products and their drying kinetics significantly depends on the status of the raw material going into the drying process. The aim of this study was to determine the impact of meat status (fresh, mature, frozen–thawed) on the drying kinetics and the resulting quality in terms of color changes and spectrally deductible information. Drying tests were conducted using meat from organically raised bulls. In the fresh meat, freezing leads to a decrease in the drying rate, while for matured meat, the opposite is true. Aging and freezing have little effect on the end product quality in terms of final product color. However, water content can be detected hyperspectrally and resolved spatially for all stages of the process. With regard to water content prediction, the Monte-Carlo uninformative variable elimination-partial least square method performs best for the fresh and fresh frozen–thawed version with seven wavelengths, an r2 of 0.97 and 0.88, and RMSE (Root mean squared error) of 0.15 and 0.17 for the test set, respectively.

On calculating metamer sets for spectrally tunable LED illuminators

Solid state lighting is becoming a popular light source for color vision experiments. One of the advantages of light emitting diodes (LEDs) is the possibility to shape the target light spectrum according to the experimenters needs. In this paper, we present a method for creating metameric lights with an LED-based spectrally tunable illuminator. The equipment we use consists of six Gamma Scientific RS-5B lamps, each containing nine different LEDs and a 1 m integrating sphere. We provide a method for describing the (almost) entire set of illuminant metamers. It will be shown that the main difficulty in describing this set arises as the result of the intensity dependent peak-wavelength shift, which is manifested by the majority of the LEDs used by the illuminators of this type. We define the normalized metamer set describing all illuminator spectra that colorimetrically match a given chromaticity. Finally, we describe a method for choosing the smoothest or least smooth metamer from the entire set.

Drying behavior and quality parameters of dried beef (biltong) subjected to different pre-treatments and maturation stages

ABSTRACT The drying behavior of fresh, matured, and frozen beef, marinated with 0.5% salt, 1% salt, salt and vinegar, and blind samples, dried at 70°C, was investigated. Weight and color (CIELAB) were measured and images of the samples were created with a hyperspectral imaging camera. Results show that the marinade and the type of beef influences the drying behavior of beef, but not the final color. Results from the hyperspectral imaging show that it is possible to build good fitting prediction models resulting in high R2 (min. 0.81, max. 0.98) and low RMSE (min. 0.08, max. 2.35) for moisture content, a* and b* values.

High pH thresholding of beef with VNIR hyperspectral imaging

Initial quality grading of meat is generally carried out using invasive and occasionally destructive sampling for the purposes of pH testing. Precise pH and thresholds exist to allow the classification of different statuses of meat, e.g. for detection of dry, firm, and dark (DFD) (when dealing with cattle and sheep), or pale, soft exudative meat (when dealing with pork). This paper illustrates that threshold detection for pH level in beef with different freshness levels (fresh, fresh frozen-thawed, matured, and matured frozen-thawed). Use of support vector machine (SVM) analysis allowed for the classification of beef samples with a pH above 5.9, and below 5.6, with an accuracy of 91% and 99% respectively. Biochemical and physical conditions of the meat concerning the pH are discussed.

Use of hyperspectral imaging for the prediction of moisture content and chromaticity of raw and pretreated apple slices during convection drying

ABSTRACT The feasibility of using spectral reflectance information in the visible—near infrared (400–1,000 nm) region to estimate moisture content (gW/gDM) and chromaticity (CIELAB) of apple slices was investigated during convection drying. Apple slices were pretreated with hot water blanching (50 and 70°C), acid application (citric and ascorbic), and combinations thereof before drying at 50 and 70°C. Prediction models for the space-averaged spectral reflectance curves were built using the partial least square regression method. A three-component partial least square regression (PLSR) model satisfied the minimal root mean square error (RMSE) criterion for predicting moisture content (avg. RMSEP = 0.13, r2 = 0.99); importantly, the critical wavelengths remained the same across all pretreatments (540, 817, 977 nm). Similarly, PLSR modeling showed that the optimal set of wavelengths (in terms of RMSE) were invariant across pretreatment for CIELAB a* prediction (543, 966 nm) and CIELAB b* prediction (510, 664, 714, 914, 969 nm). The stability of the information content of these wavelengths across pretreatments indicates their independence of color changes. Additionally, the spatial information in the hyperspectral images was exploited to visualize the performance of the predictive models by pseudo-coloring their values for each pixel in a single apple slice across different drying times. This visualization of spatial distribution of predicted moisture content and chromaticity changes shows significant potential for use in online monitoring of the drying process.

Postharvest Monitoring of Organic Potato (CV. Anuschka) During Hot-Air Drying Using Vis/Nir Hyperspectral Imaging

BACKGROUND The potential of hyperspectral imaging (500-1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS). RESULTS The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ1 ]-R[λ2 ] and R[λ1 ]:R[λ2 ], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511 nm]-R[994 nm]) and spatial domain (i.e. relative area shrinkage of slice). CONCLUSIONS Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices.