Failure Detection by signal similarity measurement of Brushless DC motors

During the last years the Brushless DC (BLDC) motors are gaining popularity as a solution for providing mechanical power, starting from low cost mobility solutions like the electric bikes, to high performance and high reliability aeronautical Electro- Mechanical Actuators (EMAs). In this framework, the availability of fault detection tools suited for these types of machines appears necessary. There is already a vast literature on this topic, but only a small percentage of the proposed techniques are developed to a Technology Readiness Level (TRL) sufficiently high to be implementable in industrial applications. The investigation on the state of the art carried out during the first phase of the present work, tries to collect the articles which are closer to a possible implementation. This choice has been influenced by the author experience when dealing with fault detection papers, which often are oriented towards a more academic public and do not concentrate on the implementation. The methodology used in this work to compile the state of the art has been the Systematic Literature Review (SLR) and it is still not diffused in the engineering world. For this reason a dedicated description has been inserted in the respective chapter of the thesis. From this study, some characteristics needed for the fault detection on electric machine have been listed and a new technique for demagnetisation detection on BLDC motors has been proposed. In the second part of the thesis, it is presented an algorithm to detect demagnetisation based on the dissimilarity between the voltages of the various electric turns of the motor due to this failure. The exposed method presents the advantages of not needing domain transforms or previous knowledge of the motor (made exception for the number of pole-pairs). Furthermore the proposed indicators are fast to be computed and require only the acquisition of motor phases voltages for a mechanical turn. The hypotheses made about the effect of a demagnetisation with Finite Element Method (FEM) have also been confirmed through simulations analysis and the proposed method to detect demagnetisation has been validated with experimental tests on a real motor. 2 Applications and Limitations The presented indicators have been studied, simulated and experimented only on an outrunner, low power BLDC motor. Anyway it is not excluded that, with some adaptation, they could be used on any BLDC motor or also on different types of motors; indeed this is an argument for a future work. Another important consideration is that, in order to detect demagnetisation, the motor should have a number of pole pairs greater than 2. This because the algorithm compares the electric turns between them and it is obviously necessary to have more than one. Another characteristic is that it can only detect partial demagnetisation. The demagnetisation of all the magnets to the same level, although very improbable, would not cause those differences in the voltage signals needed for fault detection. Various tests have been executed both at fixed and variable speed. In the first case it was possible to define a threshold to discern between the healthy and the demagnetised motor, while in the second case, even if the indicators are still separated, it was not possible to define a fixed threshold. Hence, if no classification algorithms are used (Support Vector Machine (SVM), Neural Network (NN), Artificial Intelligence (AI), etc.), the indicator shall be computed when the motor is running in steady state conditions. 3 Advantages The method of fault detection by using the proposed indicators has the main advantage of being straightforwardly applicable with no need of extra hardware. Another important characteristic to be highlighted is that the only previous needed knowledge of the motor is the number of pole-pairs. Also the intermediate data are easy to understand as they represent physical variables of the motor in the time domain. Thanks to this, also no domain transformations for frequency analysis are needed, saving computation time. The algorithm to compute the indicators is composed by few steps, it is fast to execute and does not need complex programming or libraries. Indeed the execution time for the PC implementation is already very low and an optimised implementation in a lower level programming language could easily fit in a microcontroller and be executed at even higher speed, permitting both real time monitoring and punctual testing during maintenance. Furthermore it uses only few and easily obtainable data, which makes it suitable for every industrial implementation and interesting for further academic researches. Having a maximum theoretical value for the indicator is also an important advantage, because it permits to evaluate a motor without previous knowledge of the same; indeed a healthy motor should have an ixc value always very close to this maximum value. It is worth to notice that the proposed indicators have been validated with experimental tests in various conditions, showing both good performances and space for further improvements. Finally, although it is true that constant speed is required for a correct analysis, it is needed for just a mechanical turn, i.e. for few milliseconds. For example if the motor is running at 3000 RPM, a complete turn is executed in 20 ms

High Technology Readiness Level Techniques for Brushless Direct Current Motors Failures Detection: A Systematic Review

Many papers related to this topic can be found in the bibliography; however, just a modest percentage of the introduced techniques are developed to a Technology Readiness Level (TRL) sufficiently high to be implementable in industrial applications. This paper is focused precisely on the review of this specific topic. The investigation on the state of the art has been carried out as a systematic review, a very rigorous and reliable standardised scientific methodology, and tries to collect the articles which are closer to a possible implementation. This selection has been carefully done with the definition of a series of rules, drawn to represent the adequate level of readiness of fault detection techniques which the various articles propose.Unión Europea (7PM / 2007-2013) / ERC n. 78533

Failure Detection by Signal Similarity Measurement of Brushless DC Motors

In recent years, Brushless DC (BLDC) motors have been gaining popularity as a solution for providing mechanical power, starting from low cost mobility solutions like the electric bikes, to high performance and high reliability aeronautical Electro-Mechanical Actuator (EMA). In this framework, the availability of fault detection tools suited to these types of machines appears necessary. There is already a vast literature on this topic, but only a small percentage of the proposed techniques have been developed to a sufficiently high Technology Readiness Level (TRL) to be implementable in industrial applications. The investigation on the state of the art carried out during the first phase of the present work, tried to collect the techniques which are closest to possible implementation. To fill a gap identified in the current techniques, a partial demagnetisation detection method is proposed in this paper. This technique takes advantage of the asymmetries generated in the current by the missing magnetic flux to detect the failure. Simulations and laboratory experiments have been carried out to validate the idea, showing the potential and the easy implementation of the method. The results have been examined in detail and satisfactory conclusions have been drawn

Psychoeducational Intervention for Perinatal Depression: Study Protocol of a Randomized Controlled Trial

Perinatal depression (PD) is a severe and disabling condition impacting negatively on children in terms of adverse neonatal outcomes and on the well-being of women and their families. All pregnant women attending the unit of Gynecology and Obstetrics Service of the University of Campania "L. Vanvitelli" will be screened for PD using the Edinburgh Postpartum Depression Scale (EPDS). Women with a score ≥10 at the EPDS will be invited to receive a full psychiatric assessment. The required sample size is of 126 women with PD which will be randomly allocated to either an experimental group, receiving a uni-familiar psychoeducational intervention, or to a control group, receiving the Best Treatment Option (BTO). Patients will be evaluated through several assessment instruments: Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), Global Assessment of Functioning (GAF), Clinical Global Impression (CGI), Manchester Short Assessment of Quality of Life (MANSA), Family Assessment Device (FAD), Family Coping Questionnaire (FCQ), and Pattern of Care Schedule (PCS). Patients will be evaluated at baseline, 3, 6, 9, and 12 months post-randomization. The severity of depressive symptoms at the HAM-D scale has been selected as primary outcome. Other outcome measures include improvement in the severity of anxiety symptoms, of global and personal functioning, an improvement in family members' coping strategies and in the level of quality of life. It has been highlighted the importance of developing screening and treating programs for PD, and our study will use rigorous study design to evaluate the efficacy of the adaption of a well-known family psychoeducational model to the treatment of PD. The aims of present trial are to: (1) develop an informative package for pregnant women with PD; (2) promote a screening programme for PD; (3) identify those (socio-demographic and pregnancy-related environmental) factors associated with a higher risk to develop a perinatal or postnatal depression; (4) evaluate the efficacy of a new experimental psychoeducational intervention in reducing the depressive symptoms during pregnancy compared to the BTO

Effects of pre‐operative isolation on postoperative pulmonary complications after elective surgery: an international prospective cohort study

We aimed to determine the impact of pre-operative isolation on postoperative pulmonary complications after elective surgery during the global SARS-CoV-2 pandemic. We performed an international prospective cohort study including patients undergoing elective surgery in October 2020. Isolation was defined as the period before surgery during which patients did not leave their house or receive visitors from outside their household. The primary outcome was postoperative pulmonary complications, adjusted in multivariable models for measured confounders. Pre-defined sub-group analyses were performed for the primary outcome. A total of 96,454 patients from 114 countries were included and overall, 26,948 (27.9%) patients isolated before surgery. Postoperative pulmonary complications were recorded in 1947 (2.0%) patients of which 227 (11.7%) were associated with SARS-CoV-2 infection. Patients who isolated pre-operatively were older, had more respiratory comorbidities and were more commonly from areas of high SARS-CoV-2 incidence and high-income countries. Although the overall rates of postoperative pulmonary complications were similar in those that isolated and those that did not (2.1% vs 2.0%, respectively), isolation was associated with higher rates of postoperative pulmonary complications after adjustment (adjusted OR 1.20, 95%CI 1.05-1.36, p = 0.005). Sensitivity analyses revealed no further differences when patients were categorised by: pre-operative testing; use of COVID-19-free pathways; or community SARS-CoV-2 prevalence. The rate of postoperative pulmonary complications increased with periods of isolation longer than 3 days, with an OR (95%CI) at 4-7 days or >= 8 days of 1.25 (1.04-1.48), p = 0.015 and 1.31 (1.11-1.55), p = 0.001, respectively. Isolation before elective surgery might be associated with a small but clinically important increased risk of postoperative pulmonary complications. Longer periods of isolation showed no reduction in the risk of postoperative pulmonary complications. These findings have significant implications for global provision of elective surgical care