On-line non-contact surface inspection with high precision is still an open problem. Laser triangulation techniques are the most common solution for this kind of systems, but there exist fundamental limitations to their applicability when high precisions, long standoffs or large apertures are needed, and when there are difficult operating conditions. Other methods are, in general, not applicable in hostile environments or inadequate for on-line measurement. In this paper we review the latest research in Conoscopic Holography, an interferometric technique that has been applied successfully in this kind of applications, ranging from submicrometric roughness measurements, to long standoff sensors for surface defect detection in steel at high temperatures.

Monitoring and fault detection can be critical for efficient, safe and reliable operation of electric drive systems. Unfortunately, developing accurate physics-based models for these tasks is difficult due to unknown machine parameters and incomplete knowledge of the physical phenomena occurring within the system. Machine Learning (ML) methods can learn the system’s behavior from data without requiring explicit models. However, expert knowledge of the system is still crucial to extract useful features before applying ML models. This paper presents an overview of the use of ML and data visualization methods for condition monitoring of inverter fed induction motors. More specifically, stator winding temperature estimation and insulation degradation are considered. The analyzed methods make use of the signals normally available in electric drives. Time and frequency-based approaches are considered. The developed methods are assessed on an experimental test bench. The paper is intended to bridge ML and electric drive domains. The desired outcome of this work is to provide useful guidelines for researchers in the electric drives field who aim to apply modern ML and data visualization techniques for monitoring and fault detection.

Energy management requires reliable tools to support decisions aimed at optimising consumption. Advances in data-driven models provide techniques like Non-Intrusive Load Monitoring (NILM), which estimates the energy demand of appliances from total consumption. Common single-target NILM approaches perform energy disaggregation by using separate learned models for each device. However, the use of single-target systems in real scenarios is computationally expensive and can obscure the interpretation of the resulting feedback. This study assesses a conditioned deep neural network built upon a Fully Convolutional Denoising AutoEncoder (FCNdAE) as multi-target NILM model. The network performs multiple disaggregations using a conditioning input that allows the specification of the target appliance. Experiments compare this approach with several single-target and multi-target models using public residential data from households and non-residential data from a hospital facility. Results show that the multi-target FCNdAE model enhances the disaggregation accuracy compared to previous models, particularly in non-residential data, and improves computational efficiency by reducing the number of trainable weights below 2 million and inference time below 0.25 s for several sequence lengths. Furthermore, the conditioning input helps the user to interpret the model and gain insight into its internal behaviour when predicting the energy demand of different appliances.

Hypoxia-inducible factors (HIF) 2α and 1α are the major oxygen-sensing molecules in eukaryotic cells. HIF2α has been pathogenically linked to paraganglioma and pheochromocytoma (PPGL) arising in sympathetic paraganglia or the adrenal medulla (AM), respectively. However, its involvement in the pathogenesis of paraganglioma arising in the carotid body (CB) or other parasympathetic ganglia in the head and neck (HNPGL) remains to be defined. Here, we retrospectively analyzed HIF2α by immunohistochemistry in 62 PPGL/HNPGL and human CB and AM, and comprehensively evaluated the HIF-related transcriptome of 202 published PPGL/HNPGL. We report that HIF2α is barely detected in the AM, but accumulates at high levels in PPGL, mostly (but not exclusively) in those with loss-of-function mutations in VHL and genes encoding components of the succinate dehydrogenase (SDH) complex. This is associated with upregulation of EPAS1 and the HIF2α-regulated genes COX4I2 and ADORA2A. In contrast, HIF2α and HIF2α-regulated genes are highly expressed in CB and HNPGL, irrespective of VHL and SDH dysfunctions. We also found that HIF2α and HIF1α protein expressions are not correlated in PPGL nor HNPGL. In addition, HIF1α-target genes are almost exclusively overexpressed in VHL-mutated HNPGL/PPGL. Collectively, the data suggest that involvement of HIF2α in the physiology and tumor pathology of human paraganglia is organ-of-origin-dependent and HIF1α-independent.

This article presents, by means of computational simulation tools, a full analysis and design of an Interferometric Fiber-Optic Gyroscope (IFOG) prototype based on a closed-loop configuration with sinusoidal bias phase- modulation. The complete design of the different blocks, optical and electronic, is presented, including some novelties as the sinusoidal bias phase-modulation and the use of an integrator to generate the serrodyne phase-modulation signal. The paper includes detailed calculation of most parameter values, and the plots of the resulting signals obtained from simulation tools. The design is focused in the use of a standard single-mode optical fiber, allowing a cost competitive implementation compared to commercial IFOG, at the expense of reduced sensitivity. The design contains an IFOG model that accomplishes tactical and industrial grade applications (sensitivity ≤ 0.055 °/h). This design presents two important properties: (1) an optical subsystem with advanced conception: depolarization of the optical wave by means of Lyot depolarizers, which allows to use a sensing coil made by standard optical fiber, instead by polarization maintaining fiber, which supposes consequent cost savings and (2) a novel and simple electronic design that incorporates a linear analog integrator with reset in feedback chain, this integrator generating a serrodyne voltage-wave to apply to Phase-Modulator (PM), so that it will be obtained the interferometric phase cancellation. This particular feedback design with sawtooth-wave generated signal for a closed-loop configuration with sinusoidal bias phase modulation has not been reported till now in the scientific literature and supposes a considerable simplification with regard to previous designs based on similar configurations. The sensing coil consists of an 8 cm average diameter spool that contains 300 m of standard single-mode optical-fiber (SMF-28 type) realized by quadrupolar winding. The working wavelength will be 1310 nm. The theoretical calculated values of threshold sensitivity and dynamic range for this prototype are 0.052 °/h and 101.38 dB (from ±1.164 × 10⁻⁵ °/s up to ±78.19 °/s), respectively. The Scale-Factor (SF) non-linearity for this model is 5.404% relative to full scale, this value being obtained from data simulation results.

One of the more challenging applications of optical metrology is real-time dimensional control and surface inspection in industrial applications, where strong requirements of cost, setup, and applicability in adverse environments greatly limit the number of applicable technologies. This work shows an optic profilometer developed specifically for this purpose, the algorithms developed for efficient and accurate distance map calculation, and an example of its use in a harsh steelmaking environment. This device, based on conoscopic holography, is able to obtain a distance profile of a target in a single shot, works at long distance standoff (700 to $1200mm$), and still keeps good resolution (under $0.2mm$) with a very easy and reliable setup. The second part of the work focuses on a real example of this technology applied in an on-line inspection system in steel continuous casting funded by the European Coal and Steel Community, and which is currently working in the Aceralia LDA steelmaking factory in Asturias (Spain). The system is placed in the process line and performs on-line detection of surface defects over hot steel slabs (above $800°C$ in the inspection point) from a distance of $1200mm$. 100% of the production can be inspected without interfering with the process and without adding any delay.