Wireless Hybrid Vehicle Three-Phase Motor Diagnosis Using Z-Freq Due to Unbalance Fault

Online diagnostics of three phase motor rotor faults of hybrid vehicle can be identified using a method called machine learning. Unfortunately, there is still a constraint in achieving a high success rate because a huge volume of training data is required. These faults were represented on its frequency content throughout the Fast Fourier Transform (FFT) algorithm to observe data acquired from multi-signal sensors. At that point, these failure-induced faults studies were improved using an enhanced statistical frequency-based analysis named Z-freq to optimize the study. This analysis is an investigation of the frequency domain of data acquired from the turbine blade after it runs under a specific condition. During the experiment, the faults were simulated by equipment with all those four conditions including normal mode. The failure induced by fault signals from static, coupled and dynamic were measured using high sensitivity, space-saving and a durable piezo-based sensor called a wireless accelerometer. The obtained result and analysis showed a significant pattern in the coefficient value and distribution of Z-freq data scattered for all flaws. Finally, the simulation and experimental output were verified and validated in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. This outcome has a great prospect to diagnose and monitor hybrid electric motor wirelessly. &nbsp

Wireless Hybrid Vehicle Three-Phase Motor Diagnosis Using Z-Freq Due to Unbalance Fault

Online diagnostics of three phase motor rotor faults of hybrid vehicle can be identified using a method called machine learning. Unfortunately, there is still a constraint in achieving a high success rate because a huge volume of training data is required. These faults were represented on its frequency content throughout the Fast Fourier Transform (FFT) algorithm to observe data acquired from multi-signal sensors. At that point, these failure-induced faults studies were improved using an enhanced statistical frequency-based analysis named Z-freq to optimize the study. This analysis is an investigation of the frequency domain of data acquired from the turbine blade after it runs under a specific condition. During the experiment, the faults were simulated by equipment with all those four conditions including normal mode. The failure induced by fault signals from static, coupled and dynamic were measured using high sensitivity, space-saving and a durable piezo-based sensor called a wireless accelerometer. The obtained result and analysis showed a significant pattern in the coefficient value and distribution of Z-freq data scattered for all flaws. Finally, the simulation and experimental output were verified and validated in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. This outcome has a great prospect to diagnose and monitor hybrid electric motor wirelessly. &nbsp

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024