Fault detection
Brushless DC (BLDC) motors depend on accurate rotor position detection via Hall sensors for optimal performance. Faults, such as sensor displacement, can disrupt commutation and lead to efficiency losses. This study utilizes deep learning to detect Hall sensor faults, focusing on a meticulously prepared dataset designed for this purpose. The dataset study consists of phase current measurements under various Hall sensor displacement conditions, categorized as No Delay, 0.0001 Delay, 0.005 Delay, and 0.01 Delay.
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This dataset contain the pulse responses of the Tow-Thomas filter circuit, CSTV filter circuit and the four-op-amp biquadratic filter circuit. The test excitation is a 10 us pulse signal with an amplitude of 5 V and a frequency of 5 kHZ that exhibits abundant frequency components. By observing the pulse response, the sampling frequency is set to 5 MHz and the number of sampling points for each sample is fixed at 1000 in Case 1. PSPICE is applied for circuit simulation to set up the circuit fault according to the range of fault component parameter values.
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There are several non-idealities that can degrade magnetic Hall-effect sensors performance and impact related applications. Thus, a confidence weighted learning entropy (CWLE) is proposed as a fault-tolerant control strategy for field-oriented control (FOC) of permanent magnet synchronous machines (PMSM). It combines sensorless and sensor-based control, while capitalizing on their major advantages, such as operation from standstill and at lower speeds, fast dynamic response, and fault tolerance to encoder errors.
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123 Views
The Inverter Fault Diagnosis dataset is a comprehensive collection of data aimed at facilitating research and development in the field of fault diagnosis for solar integrated grid-side three-phase inverters. This dataset includes three key features, namely Ea, Eb, and Ec, representing the energy calculated from the fault currents for phases A, B, and C, respectively.
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1092 Views
Recently, a limited number of datasets that exist are used to detect errors in the printing process of the 3D printer. Limited datasets lead most researchers to dive into sensor data fault classification.
The dataset is captured and labelled before being fed to the DL model. The image dataset is captured in a time-lapse video mode with a 15-second duration for each printing process. Next, the time-lapse is used to extract around 50 images per video. In total, 2297 images containing four classes are collected.
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1872 Views
Multiwinding-Transfomer-based (MTB) DC-DC converter did emerge in the last 25 years as an interesting possibility to connect several energy systems and/or to offer higher power density because of the reduction of transformer core material and reduction of power converter stages. MTB DC-DC converters can be considered as an interesting compromise between non-modular and a modular DC-DC converter since they are themselves modular in the construction.
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1148 Views
The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.
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Tenessee Eastman (TE) process simulates actual chemical processes and is widely used as a benchmark in test fault diagnosis and process control. The overall process consists of five operating units: reactor, condenser, vapor-liquid separator, recycle compressor and product stripper. It has standard training and test data sets for fault detection and diagnosis, classification, etc. Each data set is under different operating conditions。
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10835 Views