Vibration-based Fault Diagnostics in Wind Turbine Gearboxes Using Machine Learning

A significantly increased production of wind energy offers a path to achieve the goals of green energy policies in the United States and other countries. However, failures in wind turbines and specifically their gearboxes are higher due to their operation in unpredictable wind conditions that result in downtime and losses. Early detection of faults in wind turbines will greatly increase their reliability and commercial feasibility. Recently, data-driven fault diagnosis techniques based on deep learning have gained significant attention due to their powerful feature learning capabilities. Nonetheless, diagnosing faults in wind turbines operating under varying conditions poses a major challenge. Signal components unrelated to faults and high levels of noise obscure the signature generated by early-stage damage. To address this issue, we propose an innovative fault diagnosis framework that utilizes deep learning and leverages cyclostationary analysis of sensor data. By generating cyclic spectral coherence maps from the sensor data, we can emphasize fault-related signatures. These 2D color map representations are then used to train convolutional neural networks capable of detecting even minor faults and early-stage damages. The proposed method is evaluated using test data obtained from multibody dynamic simulations conducted under various operating conditions. The benchmark test cases, inspired by an NREL study, are successfully detected using our approach. To further enhance the accuracy of the model, subsequent studies employ Convolutional Neural Networks with Local Interpretable Model-Agnostic Explanations (LIME). This approach aids in interpreting classifier predictions and developing an interpretable classifier by focusing on a subset range of cyclic spectral coherence maps that carry the unique fault signatures. This improvement contributes to better accuracy, especially in scenarios involving multiple faults in the gearbox that need to be identified. Moreover, to address the challenge of applying this framework in practical settings, where standard deep learning techniques tend to provide inaccurate predictions for unseen faults or unusual operating conditions, we investigate fault diagnostics using a Bayesian convolutional neural network. This approach incorporates uncertainty bounds into prediction results, reducing overconfident misclassifications. The results demonstrate the effectiveness of the Bayesian approach in fault diagnosis, offering valuable implications for condition monitoring in other rotating machinery applications

Evaluation of the Optimum Pre-Tensioning Forces for Cable Stayed Bridges

The Cable-stayed Bridge is one of the most modern bridges. The structural system of this type of bridge is effectively composed of cables, main girders and towers. Because of their complex structural system, Cable-stayed bridges are highly indeterminate structures that require a high degree of technology for analysis and design. Hence, they demand sophisticated structural techniques for analysis and design when compared with other types of conventional bridges. In such bridges the cables, being flexible supports, require pre-tensioning. These pre-tension forces are important factors in the design and construction process. Thus, the response of the bridge is highly non-linear and an optimization procedure is required to evaluate the pre-tensioning forces. In this study, the unknown load factor optimization method is the method used to determine the cable forces. The procedure is based on using finite element analysis programs. The cable tension of a cable stayed bridge is evaluated under the effect of Dead load (Self weight, additional loads), Initial pre- tension force in the cable, and live load (moving load) according to AASHTO LRFD 2010 and using MIDAS Civil computer program. TUTI BAHARI cable stayed bridge of semi-fan type arrangement is analyzed for static load as a case study. The unknown load factor optimization method is used to determine the cable pre–tension forces to achieve a perfectly safe and stable bridge. The maximum cable forces (6670 kN), as well as the stresses (372 N/mm2) and displacements at the top of tower (0.033308m), are found to be within the allowable limits. The results obtained illustrate that the unknown load factor optimization method leads to optimal structural performance for the cable stayed bridge. Hence it might be a useful tool for the analysis and design of such bridges

Leveraging Time Series Data in Similarity Based Healthcare Predictive Models: The Case of Early ICU Mortality Prediction

Patient time series classification faces challenges in high degrees of dimensionality and missingness. In light of patient similarity theory, this study explores effective temporal feature engineering and reduction, missing value imputation, and change point detection methods that can afford similarity-based classification models with desirable accuracy enhancement. We select a piecewise aggregation approximation method to extract fine-grain temporal features and propose a minimalist method to impute missing values in temporal features. For dimensionality reduction, we adopt a gradient descent search method for feature weight assignment. We propose new patient status and directional change definitions based on medical knowledge or clinical guidelines about the value ranges for different patient status levels, and develop a method to detect change points indicating positive or negative patient status changes. We evaluate the effectiveness of the proposed methods in the context of early Intensive Care Unit mortality prediction. The evaluation results show that the k-Nearest Neighbor algorithm that incorporates methods we select and propose significantly outperform the relevant benchmarks for early ICU mortality prediction. This study makes contributions to time series classification and early ICU mortality prediction via identifying and enhancing temporal feature engineering and reduction methods for similarity-based time series classification. Keywords: time-series classification, similarity-based classification, mortality prediction, directional change poin

Effect of Metformin and Sitagliptin on Doxorubicin-Induced Cardiotoxicity in Rats: Impact of Oxidative Stress, Inflammation, and Apoptosis

Doxorubicin (DOX) is a widely used antineoplastic drug whose efficacy is limited by its cardiotoxicity. The aim of this study was to investigate the possible protective role of the antidiabetic drugs metformin (250 mg/kg dissolved in DW p.o. for seven days) and sitagliptin (10 mg/kg dissolved in DW p.o. for seven days) in a model of DOX-induced (single dose 15 mg/kg i.p. at the fifth day) cardiotoxicity in rats. Results of our study revealed that pretreatment with metformin or sitagliptin produced significant (P<0.05) cardiac protection manifested by a significant decrease in serum levels of LDH and CK-MB enzymes and cardiac MDA and total nitrites and nitrates levels, a significant increase in cardiac SOD activity, and remarkable improvement in the histopathological features as well as a significant reduction in the immunohistochemical expression of COX-2, iNOS, and caspase-3 enzymes as compared to DOX group. These results may suggest using metformin and/or sitagliptin as preferable drugs for diabetic patients suffering from cancer and receiving DOX in their chemotherapy regimen

DESIGN, SYNTHESIS AND COX1/2 INHIBITORY ACTIVITY OF NEW QUINAZOLINE-5-ONE DERIVATIVES

A new series of 1-(4-Acetylphenyl)-7,7-dimethyl-3-(substitutedphenyl)-1,2,3,4,7,8-octahydroquinazolin-5(6H)-ones (6-15) were synthesized and tested against COX-1 and COX-2 enzymes. Those compounds exhibited strong interaction at the COX-2 binding site and poor interaction at the COX-1 active site. Subjected to in vitro cyclooxygenase COX-1/COX-2 inhibition assay; most of the compounds especially compounds 6, 7, 12, 13, and 16 exhibited potent anti-inflammatory effects, selective COX-2 inhibition, with half-maximal inhibitor concentration (IC50) values of 0.22–1.42 μM and selectivity index (SI) values of 6.16–14.18 compared with celecoxib (IC50 = 0.05 μM and COX-2 SI: 296), diclofenac (IC50 = 0.8 μM and COX-2 SI: 4.87), and indomethacin (IC50 = 0.49 μM and COX-2 SI: 0.08) as reference drugs. Docking study has been carried out to confirm the binding affinity and selectivity of the most active compound (compound 6) to COX-2 enzyme

Comparative Study between Intrapolyp Corticosteroid Injection and Oral Corticosteroid in Treatment of Allergic Nasal Polyposis

Background: Sino-nasal polyps can be treated medically (with systemic and local steroids) or surgically; but a lot of patients refuse surgical intervention or are contraindicated to use systemic steroids. Intra-polyp steroid injections have recently been utilized to deliver high concentrations of steroid directly into the nasal polyp while simultaneously shielding the patient from the systemic steroid side effects. Objectives: To assess the role and efficacy of intra-polyp injection of steroids in the management of allergic nasal polypi, as well as to compare these findings to those of oral steroid. Patients and methods: Our study involved sixty patients that attended the outpatient clinic of the ENT department at Qena University Hospital who diagnosed with allergic nasal polypi. Their ages ranged from 17 to 63 years. They were randomly divided into 2 groups (oral steroid and intra-polyp steroid injection) according to type of treatment, each consisting of 30 patients. Results: After treatment, both groups showed a statistically significant decrease in the Symptom Score, Polyp Score, and Lund-Mackay score (P <0.001), with a significant difference between them (P < 0.001). Conclusion: Intra-polyp injection of steroid appears to be a safe and effective treatment method for Sino- nasal polyposis, with results comparable to systemic corticosteroids