Designing and analyzing two non-invasive current sensors using Ampere Force Law (AFL)

Here two different non-invasive current sensors are proposed, modeled and analyzed. The current sensors are based on the Ampere Force Law (AFL), defining the magnetic force between two parallel wire carrying currents. These current sensors can be used for detecting/sensing DC and AC currents as well as their combination in a single wire or multiple wires, and they do not rely on any permanent magnets for operation. In the first configuration, there are two microbeams, in which one of them is at the vicinity of the wire and undergoes the mechanical vibrations due to the magnetic force between the wire and the microbeam. The movement of the microbeam while it is generating a magnetic field induces a current inside another microbeam, which is stationary, as the output signal of the current sensor. In the second configuration, a single composite piezoelectric microbeam is used. The magnetic force between the wire and the piezoelectric microbeam leads the piezoelectric microbeam to move, thus it produces a voltage. Both configurations present extremely low power consumption, which is not dependent on the sensitivity of the current sensors. The dynamic response, sensitivity and power consumption of the current sensors are investigated, compared and discussed.

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A Deep Learninag based Non-invasive and Real-Time Fault Detection System for 3-Phase Induction Motors

Vol 3 No 2 - Sukkur IBA Journal of Emerging Technologies ◽

10.30537/sjet.v4i1.777 ◽

2021 ◽

Vol 4 (1) ◽

pp. 1-11

Author(s):

Muhammad Abdullah Fahim ◽

Dileep Kumar Soother ◽

Bharat Lal Harijan ◽

Jotee Kumari ◽

Areesha Qureshi

Keyword(s):

Real Time ◽

Induction Motor ◽

Short Term Memory ◽

Induction Motors ◽

Detection System ◽

Current Data ◽

Raspberry Pi ◽

Analog To Digital ◽

Non Invasive ◽

Current Sensors

Induction motor plays a major role in industry. Despite of its strong structure, induction motors are often prone to faults. There are different types of faults that occurs in the induction motor such as bearing faults, winding faults, etc. Thus motors in major applications require continuous and effective monitoring. In this paper, a stand-alone and non-invasive condition monitoring system that can monitor the condition of 3-phase induction motor using motor current signatures with aid of deep learning (DL) approaches. The proposed system extracts the features using non-invasive current sensors it further samples the features using an analog to digital converter (ADC) and organizes the data acquired from ADC using Raspberry-pi microcomputer. The current data acquired from induction motor is used to train and test the DL models including Multilayer Perceptron (MLP), Long Short-term Memory (LSTM), and One-Dimensional Convolutional Neural Networks (1DCNN). The comparative analysis is demonstrated and the LSTM model as best fault classifier among all with accuracy up to 100%. Finally, the real-time testing of the device showed that the developed system can effectively monitor the conditions of motor using non-invasive current sensors.

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X-ray microtomography

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100107058 ◽

1988 ◽

Vol 46 ◽

pp. 998-999

Author(s):

H.W. Deckman ◽

B.F. Flannery ◽

J.H. Dunsmuir ◽

K.D' Amico

Keyword(s):

Computed Tomography ◽

Internal Structure ◽

Imaging Technique ◽

Three Dimensional ◽

Tissue Structure ◽

Individual Element ◽

X Ray ◽

Non Invasive ◽

Panoramic Imaging ◽

Three Dimensional Images

We have developed a new X-ray microscope which produces complete three dimensional images of samples. The microscope operates by performing X-ray tomography with unprecedented resolution. Tomography is a non-invasive imaging technique that creates maps of the internal structure of samples from measurement of the attenuation of penetrating radiation. As conventionally practiced in medical Computed Tomography (CT), radiologists produce maps of bone and tissue structure in several planar sections that reveal features with 1mm resolution and 1% contrast. Microtomography extends the capability of CT in several ways. First, the resolution which approaches one micron, is one thousand times higher than that of the medical CT. Second, our approach acquires and analyses the data in a panoramic imaging format that directly produces three-dimensional maps in a series of contiguous stacked planes. Typical maps available today consist of three hundred planar sections each containing 512x512 pixels. Finally, and perhaps of most import scientifically, microtomography using a synchrotron X-ray source, allows us to generate maps of individual element.

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