Rough set theory applied to pattern recognition of partial discharge in noise affected cable data

This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data

Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm

For problem of complex fault types and uncertain diagnostic features of the ZPW-2000K track circuit, traditional fault diagnosis mainly adopts manual diagnosis methods, which leads to low automatic diagnosis. This paper proposes a fault diagnosis method based on Rough Sets (RS) reduction model and Bayesian Network (BN) structure learning fusion. Firstly, data mining and feature extraction are performed on the fault data table, and expert knowledge is built into the prior knowledge base. Secondly, the K2 algorithm is used to train the fault feature quantity, and the BN model is built by combining the prior knowledge base. Then, a diagnostic decision table is established through the fault instance, and RS is used for attribute reduction, dimensionality reduction, and simplified model. The MLE algorithm is used again to learn the parameters to obtain the conditional probability table of the model, and the complete BN structure is established based on the RS-BN algorithm. Finally, the comparative analysis of the simplified model and the non-simplified model is carried out. Through the experimental simulation of the ZPW-2000K track circuit fault of a high-speed railway station, the accuracy and effectiveness of the diagnostic method are verified

Learning for predictions: Real-time reliability assessment of aerospace systems

Prognostics and Health Management (PHM) aim to predict the Remaining Useful Life (RUL) of a system and to allow a timely planning of replacement of components, limiting the need for corrective maintenance and the down time of equipment. A major challenge in system prognostics is the availability of accurate physics based representations of the grow rate of faults. Additionally, the analysis of data acquired during flight operations is traditionally time consuming and expensive. This work proposes a computational method to overcome these limitations through the dynamic adaptation of the state-space model of fault propagation to on-board observations of system’s health. Our approach aims at enabling real-time assessment of systems health and reliability through fast predictions of the Remaining Useful Life that account for uncertainty. The strategy combines physics-based knowledge of the system damage propagation rate, machine learning and real-time measurements of the health status to obtain an accurate estimate of the RUL of aerospace systems. The RUL prediction algorithm relies on a dynamical estimator filter, which allows to deal with nonlinear systems affected by uncertainties with unknown distribution. The proposed method integrates a dynamical model of the fault propagation, accounting for the current and past measured health conditions, the past time history of the operating conditions (such as input command, load, temperature, etc.), and the expected future operating conditions. The model leverages the knowledge collected through the record of past fault measurements, and dynamically adapts the prediction of the damage propagation by learning from the observed time history. The original method is demonstrated for the RUL prediction of an electromechanical actuator for aircraft flight controls. We observe that the strategy allows to refine rapid predictions of the RUL in fractions of seconds by progressively learning from on-board acquisitions

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented

Contemporary Affirmation of Machine Learning Models for Sensor Validation and Recommendations for Future research Directions

Wireless Sensor Networks (WSNs) are important and needed systems for the future as the notion "Internet of Things" has emerged lately. They're used for observation, tracking, or controlling of several uses in sector, health care, home, and military. Yet, the quality of info collected by sensor nodes is changed by anomalies that happen because of various grounds, including node failures, reading errors, unusual events, and malicious assaults. Thus, fault detection is a necessary procedure before it's used in making selections to make sure the quality of sensor information. A multitude of methods can be called multiple-changeable systems/agents. For example methods such as for example creating heating system, ventilation and air conditioner(HVAC) methods are changeable methods / agents . Multiple-changeable methods /agents such as for instance these commonly don't meet performance expectations imagined at design time. Such failings can be a result of a number of factors, for example difficulties due to improper installment, substandard maintenance, or products failure. These issues, or "faults," can comprise mechanical disappointments, management difficulties, design mistakes, and improper operator treatment

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Performance of Induction Machines

Induction machines are one of the most important technical applications for both the industrial world and private use. Since their invention (achievements of Galileo Ferraris, Nikola Tesla, and Michal Doliwo-Dobrowolski), they have been widely used in different electrical drives and as generators, thanks to their features such as reliability, durability, low price, high efficiency, and resistance to failure. The methods for designing and using induction machines are similar to the methods used in other electric machines but have their own specificity. Many issues discussed here are based on the fundamental achievements of authors such as Nasar, Boldea, Yamamura, Tegopoulos, and Kriezis, who laid the foundations for the development of induction machines, which are still relevant today. The control algorithms are based on the achievements of Blaschke (field vector-oriented control) and Depenbrock or Takahashi (direct torque control), who created standards for the control of induction machines. Today’s induction machines must meet very stringent requirements of reliability, high efficiency, and performance. Thanks to the application of highly efficient numerical algorithms, it is possible to design induction machines faster and at a lower cost. At the same time, progress in materials science and technology enables the development of new machine topologies. The main objective of this book is to contribute to the development of induction machines in all areas of their applications

Space Station Freedom automation and robotics: An assessment of the potential for increased productivity

This report presents the results of a study performed in support of the Space Station Freedom Advanced Development Program, under the sponsorship of the Space Station Engineering (Code MT), Office of Space Flight. The study consisted of the collection, compilation, and analysis of lessons learned, crew time requirements, and other factors influencing the application of advanced automation and robotics, with emphasis on potential improvements in productivity. The lessons learned data collected were based primarily on Skylab, Spacelab, and other Space Shuttle experiences, consisting principally of interviews with current and former crew members and other NASA personnel with relevant experience. The objectives of this report are to present a summary of this data and its analysis, and to present conclusions regarding promising areas for the application of advanced automation and robotics technology to the Space Station Freedom and the potential benefits in terms of increased productivity. In this study, primary emphasis was placed on advanced automation technology because of its fairly extensive utilization within private industry including the aerospace sector. In contrast, other than the Remote Manipulator System (RMS), there has been relatively limited experience with advanced robotics technology applicable to the Space Station. This report should be used as a guide and is not intended to be used as a substitute for official Astronaut Office crew positions on specific issues

Análisis de riesgo de vehículos submarinos no tripulados

To know the different missions that the UUV can carry out. To Know the operation of Glider vehicles. Analyze the different methodologies available for UUV risk assessment (equipment failure, collisions with other objects, human failures, etc.). Analyze real data of the mission of a Glider in a hypothetical scenario from deep water to shallow water. Final conclusions about the study. Conocer los distintas misiones que pueden realizar los UUV Conocer el funcionamiento de los vehículos tipo Glider Analizar las distintas metodologías existentes para la valoración del riesgo en UUV (Fallo equipos, colisiones con otros objetos, fallos humanos, etc.) Analizar datos reales de misiones realizadas por Glider en un hipotético escenario desde aguas profundas a aguas someras. Obtener conclusiones finales sobre el estudio.Escuela Técnica Superior de Ingeniería Naval y OceánicaUniversidad Politécnica de Cartagen