Spark plug failure detection using Z-freq and machine learning

Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimental output were proved and endorsed in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. Finally, it confirmed that the proposed technique capably to make a diagnosis: fault detection, fault localization, and fault severity classification

Smart PV grid to reinforce the electrical network

Photovoltaic (PV) became the new competitive energy resources of the planet and needs to be engaged in grid to break up the congestion in both Distribution and Transmission systems. The objective of this research is to reduce the load flow through the distribution and transmission equipment by 20%. This reduction will help in relief networks loaded equipment’s in all networks. Many projects are starting to develop in the GCC countries and need to be organized to achieve maximum benefits from involving the Renewable Energy Sources (RES) in the network. The GCC countries have a good location for solar energy with high intensity of the solar radiation and clear sky along the year. The opportunities of the solar energy is to utilize and create a sustainable energy resource for this region. Moreover, the target of this research is to engage the PV technology in such a way to lower the over loaded equipment and increases the electricity demand at the consumer’s side

TVA generating unit modeling using MATLAB

In the present paper, the numerical evaluation of Markov model transient behavior is considered. It is focused on finding the transient-state probabilities of well-known four Tennessee Valley Authority (TVA) models. Three computational approaches are examined to find the three transient probabilities after modeling. These approaches are the Laplace Transforms, curve fitting and Neuro-Fuzzy. The MATLAB Simulink 7.10 package is used to obtain the transient state-probabilities for the four TVA models and at the same interval of time these solutions are reproduced by Laplace Transforms. For each model the three-state probabilities of the TVA models are derived. Each model is considered as a 3-state model, where its equations are obtained using the curve fitting and Neuro-Fuzzy techniques. All techniques used and applied in the present study are used to formulate and obtain the TVA models, where the Laplace Transforms is re-derived and re-used for a double check to model and obtain the results

Developing a computer package for designing an electric power station

In order to find the transient probabilities that reflect the behaviour of a large electrical-power system or any other system, a suitable technique is needed. Before calculating the transient probabilities for the overall electrical power-system, which consists of a number of sub-systems (such as generators), the failure and repair rates of each generator are needed. These two coefficients can form a transition rate matrix which represent each sub-system. In the present study, the Kronecker technique is used to form the overall system: this is the first part of the current study. The second part is to find the transient probabilities following the fourth-order Runge-Kutta method. A number of examples related to the State of Bahrain are considered. In addition, by applying the new technique under investigation, we describe the behaviours of electric-power stations.