Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time. In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions. The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved. Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures. Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities

The Effects of Knowledge of Accrued Clinical Clock Hours on Supervisors\u27 Evaluations of Clinical Competence

Supervision in speech-language pathology is one facet of the field in which all speech-language pathologists have had to engage. The more that is known about the process of supervision the better future speech-language pathologists can be prepared to interact in a professional setting. Many variables are present in supervision related to the field of speech-language pathology. One variable which has received only minimal attention relates to the effect knowledge about a student clinician\u27s number of accrued clinical clock hours has on the evaluation of the clinician\u27s skills. The assumption is often made that a student clinician with more clinical clock hours will provide more efficacious services than a student clinician with fewer clinical clock hours. It has been found that during interactions with student clinicians, supervisors regularly regard all clinicians in a similar manner, and in evaluations, supervisors do not use the information of the amount of accrued clinical clock hours to determine the effectiveness of clinician\u27s interactions. The purpose of this study was, then, to determine if knowledge of student clinicians\u27 accrued clinical clock hours influenced supervisors\u27 evaluations of student clinicians. Subjects were 26 university supervisors from six midwestern states. Stimuli were videotapes of a beginning clinician with 19 accrued clinical clock hours interacting with a client and an advanced clinician with 225 accrued clinical clock hours interacting with a different client. Subjects rated the advanced and beginning clinicians\u27 performances on a nine-point Likert scale using the Cognitive Behavioral System (Leith, 1989). All data were group analyzed according to one of six treatment conditions by information versus no information and by one order effect versus the second order effect. Response similarities and response differences were calculated by using Analysis of Variance (ANOVA) and Multiple Analysis of Variance (MANOVA) procedures. The data revealed no significant difference in evaluations based on knowledge of accrued clinical clock hours. Implications for future research were reviewed

Prudential regulation and banking supervision : building an institutional framework for Banks

Economic deregulation and financial liberalization are important for a country to develop a viable and robust financial system. But deregulation will remove the protections previously afforded the banking system. Increased competition, a changing price structure, new market entrants and other factors will increase the risks banks assume and the instability of the financial system. So, the government's goal to ensure the stability of the financial system is of paramount importance. Prudential regulation and supervision are designed to remove or lessen the threat of systemic instability. In addition, the safety and soundness of the banking system must be supported by an adequate legal framework governing a bank's contractual relationship with its customers. Satisfactory accounting and auditing standards are also crucial to ensure that financial statements reflect each financial institution's condition. Different countries have adopted different models of bank regulation and supervision. Organizational approaches also vary from country to country. However, no model will be effective if significant political interference is permitted. The primary line of defense against banking insolvency and financial system distress is the quality of management within the banks themselves. Therefore, efforts to strengthen the financial system must also focus on strengthening management through a process of institutional development.Banks&Banking Reform,Financial Intermediation,Financial Crisis Management&Restructuring,Banking Law,Environmental Economics&Policies

Fault detection of a wind turbine generator bearing using interpretable machine learning

A wind turbine is subjected to a number of degradation mechanisms during its operational lifetime. If left unattended, the degradation of components will result in poor performance and potential failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbines are regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally six-month intervals) and the maintenance program (list of tasks) is prepared using experience or risk-based reliability analysis, like risk-based inspection (RBI) and reliability-centered maintenance (RCM). This time-based maintenance program can be improved by incorporating results from condition monitoring (CM) involving data acquisition using sensors and fault detection using data analytics. It is important to ensure quality and quantity of data and to use correct procedures for data interpretation for fault detection to properly carry out condition assessment. This thesis contains the work carried out to develop a machine learning (ML) based methodology for detecting faults in a wind turbine generator bearing. The methodology includes application of ML using supervisory control and data acquisition (SCADA) data for predicting the operating temperature of a healthy bearing, and then comparing the predicted bearing temperature with the actual bearing temperature. Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing. This fault detection can then be used for rescheduling the maintenance tasks. The methodology is discussed in detail using a case study. In this thesis, interpretable ML tools are used to identify faults in a wind turbine generator bearing. Furthermore, variables affecting the generator bearing temperature are investigated. The analysis used two years of operational data from a 2 MW offshore wind turbine located in the Gulf of Guinea off the west coast of Africa. Out of the four ML models that were evaluated, the XGBoost model was determined to be the most effective performer. After utilizing the Shapley additive explanations (SHAP) to analyze the XGBoost model, it was determined that the temperature in the generator phase windings had the most significant effect on the model's predictions. Finally, based upon the deviation between the actual and the predicted temperatures, an anomaly in the generator bearing was successfully identified two months prior to a generator failure occurring.Masteroppgave i havteknologiHTEK3995MAMN-HTEKMAMN-HTE

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

The effects of supervisory feedback on the perceived and observed behaviors of preservice teachers

The effects of supervisory feedback on the perceived and observed behaviors of preservice teachers were investigated. The subjects for this investigation were 16 students enrolled in the Curriculum and Methods in Elementary Physical Education class at Ithaca College, Ithaca, New York. [This is an excerpt from the abstract. For the complete abstract, please see the document.

Peer Review Report 2007

The purpose of this document is to report findings from the research peer reviews held March 27-29, 2007 for PHMSA’s Pipeline Safety Research and Development Program. The findings and recommendations in this report derive from the scoring and comments collected from the peer review panelists. Department of Transportation (DOT) Operating Agencies (OA) are required to develop and execute a systematic process for peer review plan for all influential and highly influential information the OA plans to disseminate in the foreseeable future. Through the Information Quality Act1, Congress directed Office of Management and Budget (OMB) to “provide policy and procedural guidance to Federal agencies for ensuring and maximizing the quality, objectivity, utility, and integrity of information, (including statistical information) disseminated by Federal agencies.” A resulting OMB Bulletin, titled “Final Information Quality Bulletin for Peer Review,” was issued prescribing required procedures for Federal programs. The Office of the Secretary of Transportation (OST) produced procedures governing modal implementation of this OMB Bulletin. These procedures, as well as the OMB Bulletin, serve as the basis and justification for the PHMSA Pipeline Safety R&D Program peer reviews. The purpose of peer reviews is to uncover any technical problems or unsolved issues in a scientific work product with technically competent and independent, objective experts. Peer review of a major scientific work product that will have the imprimatur of the Federal Government needs to be incorporated into the upfront planning of any action based in the work product. This includes obtaining the proper resources commitments (reviewers and funds), then establishing realistic schedules

Immediate Feedback Using the Bug-In-The-Ear in Counselor Training: Implications for Counseling Self-Efficacy, Trainee Anxiety and Skill Development

This investigation examines the delivery of immediate, in-session feedback using the “bug-in-the-ear” (BITE) as an instructional technique in conjunction with live supervision during the counseling practicum. The study was conducted to explore an effective means of supervisor intervention which did not disrupt the counseling session. Few empirical investigations have been conducted in this area, and previous studies on this instructional aid used in models of live supervision were largely narrative in design. Counseling self-efficacy, trainee anxiety, and counseling performance were examined for twenty graduate student counselor trainees enrolled in the department of counseling at a northern plains university. Ten participants received immediate feedback via the BITE in conjunction with a live supervision model of training during the first half of 10 practicum sessions conducted at a community counseling clinic. Ten participants serving as controls received live supervision without the BITE feedback during their 10 sessions. Results indicated that participants who received immediate feedback via the BITE demonstrated significantly greater increases in counseling self-efficacy throughout the course of the investigation than did the control group participants. Changes in participant anxiety levels did not differ significantly between groups. BITE or no-BITE feedback condition, changes in counseling self-efficacy and changes in anxiety level combined to account for significant portions of the variance in participants’ scores on two measures of counseling performance. Participants reported no adverse effects due to the immediate feedback, although problems with the physical equipment were noted. A series of exploratory analyses based on previous BITE investigations were also conducted. Attempts to theoretically explain the benefits of incorporating immediate feedback in live supervision using Bandura’s (1977) self-efficacy theory are presented. Implications for the training of graduate students in the counseling practicum and suggestions for future research in this area are discusse