Multiple faults classification on three-phase induction motors using a single current transformer and artificial neural networks

The element most used to convert electrical energy into mechanical is the phase induction motor, which is indispensable in industrial production processes. This equipment is constantly subject of research to identify defects in order to reduce maintenance costs, minimize unscheduled process stoppages, reduce costs and mainly increase availability. Hence, This work proposes the study, development and implementation of a system for detecting and classify stator short-circuit faults, broken rotor bars and bearing faults in induction motors by monitoring the stator current signals in the time domain by using a current transformer. More specifically, this work consider the usage of Artificial Neural Networks of Mult Layer Perceptron type, using as the inputs the current signals measured by the current transformer, to proper identify and classify induction motor faults. The database used for the development has been obtained through the experiments performed with motors of 0,736 kW and 0,736 kW on a test bench. The system is validated with both machines working with load torque variation and voltage unbalance. Still, the proposal is embedded in dedicated hardware based on a digital signals processor and experimental tests are performed with the system running in real time.Conselho Nacional do Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do ParanáO elemento mais utilizado de conversão de energia elétrica em mecânica motriz é o motor de indução trifásico, o qual é indispensável nos processos produtivos industriais. Esse equipamento é constantemente alvo de pesquisas, as quais visam identificar defeitos a fim de diminuir as taxas de manutenções, minimizar as paradas não programadas no processo, reduzir custos e aumentar a disponibilidade dessas máquinas nos processos industriais. Esta dissertação propõe o estudo, desenvolvimento e implementação de um sistema para detecção e classificação de falhas de estator, rotor e rolamento de motores elétricos por meio do monitoramento dos sinais de corrente do motor no domínio do tempo por um transformador de corrente. Mais especificamente, o trabalho utiliza redes neurais artificiais do tipo Perceptron Multicamadas, as quais têm como entrada os sinais de corrente da máquina medidas por um único transformador de corrente, para identificar e classificar falhas deum motor de indução trifásico. A base de dados utilizada para desenvolvimento do trabalho foi obtida por meio de experimentos realizados com motores de 0,736 kW e 1,47 kW em uma bancada de testes. O sistema é validado com as duas máquinas trabalhando com variações de conjugado de carga e desequilíbrio de tensão em sua alimentação. Ainda, a proposta é embarcada em hardware dedicado baseado em um processador digital de sinais e testes experimentais são realizados com o sistema funcionando em tempo real

The Use of Digital Twins in Finite Element for the Study of Induction Motors Faults

Induction motors play a key role in the industrial sector. Thus, the correct diagnosis and classification of faults on these machines are important, even in the initial stages of evolution. Such analysis allows for increased productivity, avoids unexpected process interruptions, and prevents damage to machines. Usually, fault diagnosis is carried out by analyzing the characteristic effects caused by the faults. Thus, it is necessary to know and understand the behavior during the operation of the faulty machine. In general, monitoring these characteristics is complex, as it is necessary to acquire signals from the same motor with and without failures for comparison purposes. Whether in an industrial environment or in laboratories, the experimental characterization of failures can become unfeasible for several reasons. Thus, computer simulation of faulty motors digital twins can be an important alternative for failure analysis, especially in large motors. From this perspective, this paper presents and discusses several limitations found in the technical literature that can be minimized with the implementation of digital twins. In addition, a 3D finite element model of an induction motor with broken rotor bars is demonstrated, and motor current signature analysis is used to verify the fault effects. Results are analyzed in the time and frequency domain. Additionally, an artificial neural network of the multilayer perceptron type is used to classify the failure of broken bars in the 3D model rotor

The Portuguese Severe Asthma Registry: Development, Features, and Data Sharing Policies

The Portuguese Severe Asthma Registry (Registo de Asma Grave Portugal, RAG) was developed by an open collaborative network of asthma specialists. RAG collects data from adults and pediatric severe asthma patients that despite treatment optimization and adequate management of comorbidities require step 4/5 treatment according to GINA recommendations. In this paper, we describe the development and implementation of RAG, its features, and data sharing policies. The contents and structure of RAG were defined in a multistep consensus process. A pilot version was pretested and iteratively improved. The selection of data elements for RAG considered other severe asthma registries, aiming at characterizing the patient’s clinical status whilst avoiding overloading the standard workflow of the clinical appointment. Features of RAG include automatic assessment of eligibility, easy data input, and exportable data in natural language that can be pasted directly in patients’ electronic health record and security features to enable data sharing (among researchers and with other international databases) without compromising patients’ confidentiality. RAG is a national web-based disease registry of severe asthma patients, available at asmagrave.pt. It allows prospective clinical data collection, promotes standardized care and collaborative clinical research, and may contribute to inform evidence-based healthcare policies for severe asthma

Containment measures

OBSOLETE (project finished) - Description of containment measures during COVID'19 lockdown, in the context of SIlent Cities project. Please request access to Silent Cities if neede

Archived - General Information (DO NOT USE)

DO NOT USE - The goal of this component was to document the data collection process of the Silent Cities Dataset. This component is just left for archive

Prospective observational cohort study on grading the severity of postoperative complications in global surgery research

Background The Clavien–Dindo classification is perhaps the most widely used approach for reporting postoperative complications in clinical trials. This system classifies complication severity by the treatment provided. However, it is unclear whether the Clavien–Dindo system can be used internationally in studies across differing healthcare systems in high- (HICs) and low- and middle-income countries (LMICs). Methods This was a secondary analysis of the International Surgical Outcomes Study (ISOS), a prospective observational cohort study of elective surgery in adults. Data collection occurred over a 7-day period. Severity of complications was graded using Clavien–Dindo and the simpler ISOS grading (mild, moderate or severe, based on guided investigator judgement). Severity grading was compared using the intraclass correlation coefficient (ICC). Data are presented as frequencies and ICC values (with 95 per cent c.i.). The analysis was stratified by income status of the country, comparing HICs with LMICs. Results A total of 44 814 patients were recruited from 474 hospitals in 27 countries (19 HICs and 8 LMICs). Some 7508 patients (16·8 per cent) experienced at least one postoperative complication, equivalent to 11 664 complications in total. Using the ISOS classification, 5504 of 11 664 complications (47·2 per cent) were graded as mild, 4244 (36·4 per cent) as moderate and 1916 (16·4 per cent) as severe. Using Clavien–Dindo, 6781 of 11 664 complications (58·1 per cent) were graded as I or II, 1740 (14·9 per cent) as III, 2408 (20·6 per cent) as IV and 735 (6·3 per cent) as V. Agreement between classification systems was poor overall (ICC 0·41, 95 per cent c.i. 0·20 to 0·55), and in LMICs (ICC 0·23, 0·05 to 0·38) and HICs (ICC 0·46, 0·25 to 0·59). Conclusion Caution is recommended when using a treatment approach to grade complications in global surgery studies, as this may introduce bias unintentionally

The surgical safety checklist and patient outcomes after surgery: a prospective observational cohort study, systematic review and meta-analysis

© 2017 British Journal of Anaesthesia Background: The surgical safety checklist is widely used to improve the quality of perioperative care. However, clinicians continue to debate the clinical effectiveness of this tool. Methods: Prospective analysis of data from the International Surgical Outcomes Study (ISOS), an international observational study of elective in-patient surgery, accompanied by a systematic review and meta-analysis of published literature. The exposure was surgical safety checklist use. The primary outcome was in-hospital mortality and the secondary outcome was postoperative complications. In the ISOS cohort, a multivariable multi-level generalized linear model was used to test associations. To further contextualise these findings, we included the results from the ISOS cohort in a meta-analysis. Results are reported as odds ratios (OR) with 95% confidence intervals. Results: We included 44 814 patients from 497 hospitals in 27 countries in the ISOS analysis. There were 40 245 (89.8%) patients exposed to the checklist, whilst 7508 (16.8%) sustained ≥1 postoperative complications and 207 (0.5%) died before hospital discharge. Checklist exposure was associated with reduced mortality [odds ratio (OR) 0.49 (0.32–0.77); P\u3c0.01], but no difference in complication rates [OR 1.02 (0.88–1.19); P=0.75]. In a systematic review, we screened 3732 records and identified 11 eligible studies of 453 292 patients including the ISOS cohort. Checklist exposure was associated with both reduced postoperative mortality [OR 0.75 (0.62–0.92); P\u3c0.01; I2=87%] and reduced complication rates [OR 0.73 (0.61–0.88); P\u3c0.01; I2=89%). Conclusions: Patients exposed to a surgical safety checklist experience better postoperative outcomes, but this could simply reflect wider quality of care in hospitals where checklist use is routine

Critical care admission following elective surgery was not associated with survival benefit: prospective analysis of data from 27 countries

This was an investigator initiated study funded by Nestle Health Sciences through an unrestricted research grant, and by a National Institute for Health Research (UK) Professorship held by RP. The study was sponsored by Queen Mary University of London