Abstract: - In industrial and automotive electric drives, the main components are an electric motor and a power electronics-based inverter. We describe a model-based fault diagnosis system in this research machine learning technology was used to design a system for identifying and detecting many types of defects in an electric system drive. From a hardware failure point of view, the weakest link in such a system is as a result, the focus of this study is on detecting and locating flaws [1].
The aim of this work is to show how to detect defects in an electromechanical conversion chain for traditional or autonomous electric vehicles. EVs are feasible to operate the information and data collected by several sensors to recover a sequence of data such as currents, voltages, and speeds, and so on. Using the characteristics extraction technique, create an architecture for a fault detection model. The long short-term memory (LSTM) technique for fault detection is displayed in this regard. This method has been used to build an electric vehicle prototype and has shown to be more accurate than other methods [2].
This article describes a fault detection technique based on machine learning (ML) that can help maintenance assistants in discovering defects in induction machine power connections. The system has been built to handle not only single phasing failures but also opposing wiring connections. As field data in default, in an industry where defective incidents are rare, a simulation-driven ML-based fault detection system could be useful. As a result, the ML algorithm's training data is now available. To train the machine learning models, developed using Software-in-the-Loop simulations [3].
Keywords: Vibration sensor, Voltage Sensor, Current Sensor, DHT11 Humidity Sensor and Temperature Sensor, Arduino Nano, LCD Display, Relay,
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