RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

Switched-mode power supply (SMPS) has been of vital importance majorly in power management of industrial equipment with much-improved efficiency and reliability. Given the diverse range on loading and operating conditions of SMPS, several anomalies can occur in the device resulting to over-voltage, overloading, erratic atmospheric conditions, etc. Electrical over-stress (EOS) is one of the commonly used causes of failure among power electronic devices. Since there is a limitation for the SMPS in terms of input voltage and current (two methods of controlling an SMPS), the device has been subjected to an accelerated aging test using EOS. This study presents a two-fold approach to evaluate the overall state of health of SMPS using an integration of extended Kalman filter (EKF) and deep neural network. Firstly, the EKF algorithm would assist in fusing fault features to acquire an comprehensive degradation trend. Secondly, the degradation pattern of the SMPS has been monitored for four different electrical loadings, and a bi-directional long short-term memory (BiLSTM) deep neural network is trained for future predictions. The proposed model provides a unique approach and accuracy in SMPS fault indication with the aid of electrical parameters.

KS IOT Based Automation System to Prevent Crop Vandalization by Rain Water in Agricultural Regions

India’s keystone is Agriculture. Around 70 percent of India’s revenue comes from Agriculture. Conversely the population of India amplifies each and every day which requires efficient and well planned decision making techniques for the production of crops. In this research paper we find the intensification of the structures which prevent destruction of crops due to uneven and heavy rainfall. The goal is achieved by the concept of Embedded System design using IOT technology. This is done automatically without any human interference. Here we first identifies the water level in the agriculture field during rainfall by using water level sensors , if the water level exceeds there limit that will cause spoilage of crop then the device are automatically cover the agriculture field. It also identifies the temperature of the crops by using temperature sensor during the sunny days, if the heat causes spoilage of crops due to intensive sun rays then the device will automatically covers the agriculture field. After covering the agriculture field it will send the alert message using GSM module to the farmer and simultaneously the water of rain is collected through piles that will be reuse later for irrigation. To achieve this we use microcontroller , Solar panels, GSM module, DC motor, sensors, Switched-mode power supply (SMPS), Rechargeable battery

Improved Power Quality Switched Mode Power Supply Using Buck-Boost Converter

In this work, The devices generally used in industrial, commercial and residential applications need to undergo rectification for their proper functioning and operation. The operation of the buck– boost converter in discontinuous conduction mode ensures inherent PFC operation and reduces complexity in control. Hence there is a need to reduce the line current harmonics so as to improve the power factor of the system. This has led to designing of Power Factor Correction circuits. This concept presents a power factor corrected (PFC) Buck–Boost converter-fed SMPS. This project deals with the design, analysis, simulation, and development of a power-factor-correction (PFC) multiple output switched-mode power supply (SMPS) using a Buck–Boost converter at the front end. Single-phase ac supply is fed to a pair of back-to-back connected buck–boost converters to eliminate the diode bridge rectifier, which results in reduction of conduction losses and power quality improvement at the front end. To observe the performance of this converter, a model based on the Buck Boost topology has been designed and developed software and implemented with Proportional-Integral (PI) and Fuzzy logic controller. The simulations are demonstrated in order to validate the effectiveness of the controllers in power factor improvement. By using MATLAB.

Rancang-Bangun Prototipe Sistem Kontrol Berbasis Programmable Logic Controller untuk Pengoperasian Miniatur Penyortiran Material

A miniature sorting of material quality has been made, aided by a prototype of the controller system based on the Mitsubishi FX1N-24MR Programmable Logic Controller (PLC). A number of stages include the manufacture of the conveyor system unit, the electrical system, PLC programming, and performance measurement. The conveyor unit assembling was processed by installing the conveyor belt, dc motor, pneumatic cylinder, solenoid valve, and sensors. The electrical system is an integration of the Mitsubishi FX1N-24MR PLC, switched-mode power supply, miniature circuit breaker (MCB), dc voltage regulator circuit, relays, digital counters, pushbuttons, and selector switches arranged in a 20 x 30 x 15 cm panel box. Mitsubishi PLC system programming is based on algorithmic determination and ladder diagram arrangement assisted by GX Developer (GX Work). Performance measurement in the form of pulse readings is carried out by setting and manufacturing ladder counters and shift registers to count the number of pulses for each material and the accuracy of sorting when the material is detected simultaneously. The system performance is indicated by pulse reading accuracy and sorting timing accuracy. The reading of the pulse from the proximity switch affects the counter calculation to activate the pneumatic cylinder unit in sorting. Sorting for material-A takes 11 pulses, while for material-B, it takes 19 pulses. The synchronization measurement functions when an error occurs in the system in order to maintain the input received is the same as the output in the PLC-based control system.

A Failure Mode Assessment Model Based on Neutrosophic Logic for Switched-Mode Power Supply Risk Analysis

Reducing the potential risks in the manufacturing process to improve the reliability of the switched-mode power supply (SMPS) is a critical issue for the users’ safety. This paper proposes a novel failure mode and effects analysis (FMEA) model based on hybrid multiple criteria decision-making (MCDM), which adopts neutrosophic set theory into the proposed model. A developed neutrosophic Best Worst method (NBWM) is used to evaluate the weights of risk factors and determine their importance. Secondly, the neutrosophic Weight Aggregated Sum Product Assessments (NWASPAS) method is utilized to calculate the Risk Priority Number (RPN) of the failure modes. The proposed model improves the shortcomings of traditional FMEA and improves the practical applicability and effectiveness of the Best Worst method (BWM) and Weight Aggregated Sum Product Assessments (WASPAS) methods. In addition, this study uses neutrosophic logic to reflect the true judgments of experts in the assessment, which considers authenticity, deviation, and uncertainty to obtain more reliable information. Finally, an empirical case study from an SMPS company headquartered in Taiwan demonstrates the effectiveness and robustness of the proposed model. In addition, by comparing with two other FMEA models, the results show that the proposed model can more clearly reflect the true and effective risks in the assessment. The results can effectively help power supply manufacturers to assess risk factors and determine key failure modes.