Advanced Analysis of Motor Currents for the Diagnosis of the Rotor Condition in Electric Motors Operating in Mining Facilities

© 1972-2012 IEEE. Predictive maintenance of electric motors is a topic of increasing importance in many industrial applications. The mining industry is not an exception; many electric motors operating in mining facilities are critical machines, and their unexpected failures may imply significant losses and can be hazardous for the users. Due to these facts, an increasing research effort has been dedicated to investigate new techniques that are able to provide a reliable diagnostic of the motor condition. Over recent years, monitoring of electrical quantities (e.g., motor currents) has emerged as a very attractive option for determining the health of several motor parts (rotor, eccentricities, bearings) due to its very interesting advantages: possibility of remote motor monitoring, noninvasive nature, simple application, broad fault coverage, etc. The traditional methods based on the analysis of motor currents during a steady-state operation [motor current signature analysis (MCSA)] are being complemented when not replaced by more reliable approaches. This paper applies an innovative transient-based methodology to several case studies referred to large motors operating in mining facilities. The results prove how this modern methodology enables us to overcome some important drawbacks of the classical MCSA, such as its unsuitability under varying speed conditions, and may provide an earlier indication of rotor electrical asymmetries under such working conditions

Detection of interictal discharges with convolutional neural networks using discrete ordered multichannel intracranial EEG

Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditionally employed handcrafted features which utilised specific characteristics of neural responses. Although these algorithms achieve high accuracy, mere detection of an IED holds little clinical significance. In this work, we consider deep learning for epileptic subjects to accommodate automatic feature generation from intracranial EEG data, while also providing clinical insight. Convolutional neural networks are trained in a subject independent fashion to demonstrate how meaningful features are automatically learned in a hierarchical process. We illustrate how the convolved filters in the deepest layers provide insight towards the different types of IEDs within the group, as confirmed by our expert clinicians. The morphology of the IEDs found in filters can help evaluate the treatment of a patient. To improve the learning of the deep model, moderately different score classes are utilised as opposed to binary IED and non-IED labels. The resulting model achieves state of the art classification performance and is also invariant to time differences between the IEDs. This study suggests that deep learning is suitable for automatic feature generation from intracranial EEG data, while also providing insight into the dat

A GEANT4 Study of a Gamma-ray Collimation Array

Proton beam therapy uses high-energy protons to destroy cancer cells which are still uncertain about where in the body they hit. A possible way to answer this question is to detect the gamma rays produced during the irradiation and determine where in the body they are produced. This work investigates the use of collimators to determine where the proton interactions occur. GEANT4 is used to simulate the gamma production of a source interacting with a collimator. Each event simulates a number of gammas obtained as a function of the position along the detector. Repeating for different collimator configurations can thus help determine the best characteristics of a detector device

Evaluation of the Detectability of Electromechanical Faults in Induction Motors Via Transient Analysis of the Stray Flux

© 1972-2012 IEEE. The stray flux that is present in the vicinity of an induction motor is a very interesting information source to detect several types of failures in these machines. The analysis of this quantity can be employed, in some cases, as a supportive tool to complement the diagnosis provided by other quantities. In other cases, when no other motor quantities are available, stray flux analysis can become one of the few alternatives to evaluate the motor condition. Its noninvasive nature, low cost, and easy implementation makes it a very interesting option that requires further investigation. The aim of this work is to evaluate the suitability of the stray flux analysis under the starting transient as a way to detect certain faults in induction motors (broken rotor bars and misalignments), even when these types of faults coexist in the motor. To this end, advanced signal processing tools will be applied. Several positions of the flux sensors are considered in this study. Also, for the first time, a fault indicator based on the stray flux analysis under the starting is introduced and its sensitivity is compared versus other indicators relying on other quantities. It must be emphasized that, since the capture of the transient and steady-state flux signals can be carried out in the same measurement, the application of the approach presented in this work is straightforward and its derived information may become crucial for the diagnosis of some faults.Ministerio de Economía y Competitividad’ (MINECO) and FEDER program in the framework of the ‘Proyectos I+D del Subprograma de Generación de Conocimiento, Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia’ (ref: DPI2014-52842-P)

Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges

Objective. Interictal epileptiform discharges (IEDs) occur between two seizures onsets. IEDs are mainly captured by intracranial recordings and are often invisible over the scalp. This study proposes a model based on tensor factorization to map the time-frequency (TF) features of scalp EEG (sEEG) to the TF features of intracranial EEG (iEEG) in order to detect IEDs from over the scalp with high sensitivity. Approach. Continuous wavelet transform is employed to extract the TF features. Time, frequency, and channel modes of IED segments from iEEG recordings are concatenated into a four-way tensor. Tucker and CANDECOMP/PARAFAC decomposition techniques are employed to decompose the tensor into temporal, spectral, spatial, and segmental factors. Finally, TF features of both IED and non-IED segments from scalp recordings are projected onto the temporal components for classification. Main results. The model performance is obtained in two different approaches: within- and between-subject classification approaches. Our proposed method is compared with four other methods, namely a tensor-based spatial component analysis method, TF-based method, linear regression mapping model, and asymmetric-symmetric autoencoder mapping model followed by convolutional neural networks. Our proposed method outperforms all these methods in both within- and between-subject classification approaches by respectively achieving 84.2% and 72.6% accuracy values. Significance. The findings show that mapping sEEG to iEEG improves the performance of the scalp-based IED detection model. Furthermore, the tensor-based mapping model outperforms the autoencoder- and regression-based mapping models

Modification of Food Systems by Ultrasound

This review describes the mechanism, operation, and recent potential applications of ultrasound in various food systems, as well as the physical and chemical effects of ultrasound treatments on the conservation and modification of different groups of food. Acoustic energy has been recognized as an emerging technology with great potential for applications in the food industry. The phenomenon of acoustic cavitation, which modifies the physical, chemical, and functional properties of food, can be used to improve existing processes and to develop new ones. The combination of ultrasonic energy with a sanitizing agent can improve the effect of microbial reduction in foods and, thereby, their quality. Finally, it is concluded that the use of ultrasound in food is a very promising area of research; however, more research is still needed before applying this technology in a wider range of industrial sectors

Real-life management of patients with breakthrough cancer pain caused by bone metastases in Spain

Purpose: We aimed to explore the characteristics, and real-life therapeutic management of patients with breakthrough cancer pain (BTcP) caused by bone metastases in Spain, and to evaluate physicians' opinion of and satisfaction with prescribed BTcP therapy. Participants and methods: For the purposes of this study, an ad-hoc questionnaire was developed consisting of two domains: a) organizational aspects and care standards; b) clinical and treatment variables of bone metastatic BTcP patients. In addition, physicians' satisfaction with their prescribed BTcP therapy was assessed. Specialists collected data from up to five patients receiving treatment for BTcP caused by bone metastasis, all patients gave their consent to participate prior to inclusion. Results: A total of 103 cancer pain specialists (radiation oncologists [38.8%], pain specialists [33.0%], and palliative care (PC) specialists [21.4%]) were polled, and data on 386 BTcP patients with bone metastatic disease were collected. Only 33% of the specialists had implemented specific protocols for BTcP management, and 19.4% had established referral protocols for this group of patients. Half of all participants (50.5%) address quality of life and quality of care in their patients; however, only 27.0% did so from the patient's perspective, as they should do. Most patients had multiple metastases and were prescribed rapid-onset fentanyl preparations (71.2%), followed by immediate-release morphine (9.3%) for the treatment of BTcP. Rapid-onset fentanyl was prescribed more often in PC units (79.0%) than in pain units (75.9%) and radiation oncology units (61.1%) (p<0.01). Furthermore, most physicians (71.8%) were satisfied with the BTcP therapy prescribed. Conclusions: Our results demonstrate the need for routine assessment of quality of life in patients with bone BTcP. These findings also underscore the necessity for a multidisciplinary therapeutic strategy for breakthrough pain in clinical practice in Spain

Deep Neural Architectures for Mapping Scalp to Intracranial EEG

Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and EEG data is no exemption. Intracranial EEG data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for non-linearly mapping scalp to intracranial EEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp- intracranial recording to establish a novel methodology for detecting the epileptic discharges from the scalp EEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real world clinical application, these pseudo-intracranial EEG are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of intracranial EEG and the limitations of scalp EEG, we have achieved a classification accuracy of 64%; an increase of 6% over the previously proposed linear regression mapping

Type 2 diabetes mellitus alters cardiac mitochondrial content and function in a non-obese mice model

Type 2 diabetes mellitus (T2DM) is associated with an increase of premature appearance of several disorders such as cardiac complications. Thus, we test the hypothesis that a combination of a high fat diet (HFD) and low doses of streptozotocin (STZ) recapitulate a suitable mice model of T2DM to study the cardiac mitochondrial disturbances induced by this disease. Animals were divided in 2 groups: the T2DM group was given a HFD and injected with 2 low doses of STZ, while the CNTRL group was given a standard chow and a buffer solution. The combination of HFD and STZ recapitulate the T2DM metabolic profile showing higher blood glucose levels in T2DM mice when compared to CNTRL, and also, insulin resistance. The kidney structure/function was preserved. Regarding cardiac mitochondrial function, in all phosphorylative states, the cardiac mitochondria from T2DM mice presented reduced oxygen fluxes when compared to CNTRL mice. Also, mitochondria from T2DM mice showed decreased citrate synthase activity and lower protein content of mitochondrial complexes. Our results show that in this non-obese T2DM model, which recapitulates the classical metabolic alterations, mitochondrial function is impaired and provides a useful model to deepen study the mechanisms underlying these alterations.This study was supported by Coordenacao de aperfeicoamento de pessoal de nivel superior (CAPES), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) and Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ)