Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis

As crucial parts of various engineering systems, solenoid valves (SVs) operated by electromagnetic solenoid (EMS) are of great importance and their failure may lead to cause unexpected casualties. This failure, characterized by a degradation of the performances of the SVs, could be due to a fluctuations in the EMS parameters. These fluctuations are essentially attributed to the changes in the spring constant, coefficient of friction, inductance, and the resistance of the coil. Preventive maintenance by controlling and monitoring these parameters is necessary to avoid eventual failure of these actuators. The authors propose a new methodology for the functional diagnosis of electromagnetic solenoids (EMS) used in hydraulic systems. The proposed method monitors online the electrical and mechanical parameters varying over time by using artificial neural networks algorithm coupled with an optical fiber polarization squeezer based on EMS for polarization scrambling. First, the MATLAB/Simulink model is proposed to analyze the effect of the parameters on the dynamic EMS model. The result of this simulation is used for training the neural network, then a simulation is proposed using the neural net fitting toolbox to determine the solenoid parameters (Resistance of the coil R, stiffness K and coefficient of friction B of the spring) from the coefficients of the transfer function, established from the model step response. Future work will include not only diagnosing failure modes, but also predicting the remaining life based on the results of monitoring

Vacant Parking Lot Information System Using Transfer Learning and IoT

Parking information systems have become very important, especially in metropolitan areas as they help to save time, effort and fuel when searching for parking. This paper offers a novel low-cost deep learning approach to easily implement vacancy detection at outdoor parking spaces with CCTV surveillance. The proposed method also addresses issues due to perspective distortion in CCTV images. The architecture consists of three classifiers for checking the availability of parking spaces. They were developed on the TensorFlow platform by re-training MobileNet (a pre-trained Convolutional Neural Network (CNN)) model using the transfer learning technique. A performance analysis showed 88% accuracy for vacancy detection. An end-to-end application model with Internet of Things (IoT) and an Android application is also presented. Users can interact with the cloud using their Android application to get real-time updates on parking space availability and the parking location. In the future, an autonomous car could use this system as a V2I (Vehicle to Infrastructure) application in deciding the nearest parking space

Feature Selection For The Fuzzy Artmap Neural Network Using A Hybrid Genetic Algorithm And Tabu Search

Prestasi pengelas rangkaian neural amat bergantung kepada set data yang digunakan dalam process pembelajaran. The performance of Neural-Network (NN)-based classifiers is strongly dependent on the data set used for learning

Dynamic analysis of synchronous machine using neural network based characterization clustering and pattern recognition

Synchronous generators form the principal source of electric energy in power systems. Dynamic analysis for transient condition of a synchronous machine is done under different fault conditions. Synchronous machine models are simulated numerically based on mathematical models where saturation on main flux was ignored in one model and taken into account in another. The developed models were compared and scrutinized for transient conditions under different kind of faults – loss of field (LOF), disturbance in torque (DIT) & short circuit (SC). The simulation was done for LOF and DIT for different levels of fault and time durations, whereas, for SC simulation was done for different time durations. The model is also scrutinized for stability stipulations. Based on the synchronous machine model, a neural network model of synchronous machine is developed using neural network based characterization. The model is trained to approximate different transient conditions; such as – loss of field, disturbance in torque and short circuit conditions. In the case of multiple or mixture of different kinds of faults, neural network based clustering is used to distinguish and identify specific fault conditions by looking at the behaviour of the load angle. By observing the weight distribution pattern of the Self Organizing Map (SOM) space, specific kinds of faults is recognized. Neural network patter identification is used to identify and specify unknown fault patterns. Once the faults are identified neural network pattern identification is used to recognize and indicate the level or time duration of the fault

An adaptive atmospheric prediction algorithm to improve density forecasting for aerocapture guidance processes

Many modern entry guidance systems depend on predictions of atmospheric parameters, notably atmospheric density, in order to guide the entry vehicle to some desired final state. However, in highly dynamic atmospheric environments such as the Martian atmosphere, the density may vary by as much as 200% from predicted pre-entry trends. This high level of atmospheric density uncertainty can cause significant complications for entry guidance processes and may in extreme scenarios cause complete failure of the entry. In the face of this uncertainty, mission designers are compelled to apply large trajectory and design safety margins which typically drive the system design towards less efficient solutions with smaller delivered payloads. The margins necessary to combat the high levels of atmospheric uncertainty may even preclude scientifically interesting destinations or architecturally useful mission modes such as aerocapture. Aerocapture is a method for inserting a spacecraft into an orbit about a planetary body with an atmosphere without the need for significant propulsive maneuvers. This can reduce the required propellant and propulsion hardware for a given mission which lowers mission costs and increases the available payload fraction. However, large density dispersions have a particularly acute effect on aerocapture trajectories due to the interaction of the high required speeds and relatively low densities encountered at aerocapture altitudes. Therefore, while the potential system level benefits of aerocapture are great, so too are the risks associated with this mission mode in highly uncertain atmospheric environments such as Mars. Contemporary entry guidance systems utilize static atmospheric density models for trajectory prediction and control. These static models are unable to alter the fundamental nature of the underlying state equations which are used to predict atmospheric density. This limits both the fidelity and adaptive freedom of these models and forces the guidance system to retroactively correct for the density prediction errors after those errors have already impacted the trajectory. A new class of dynamic density estimator called a Plastic Ensemble Neural System (PENS) is introduced which is able to generate high fidelity, adaptable density forecast models by altering the underlying atmospheric state equations to better agree with observed atmospheric trends. A new construct called an ensemble echo is also introduced which creates an associative learning architecture, permitting PENS to evolve with increasing atmospheric exposure. The PENS estimator is applied to a numerical guidance system and the performance of the composite system is investigated with over 144,000 guided trajectory simulations. The results demonstrate that the PENS algorithm achieves significant reductions in both the required post-aerocapture performance, and the aerocapture failure rates relative to historical density estimators.Ph.D