PV fault detection through IR thermography: using EMPHASIS under uneven environmental conditions

2021 ◽  
Author(s):  
Ciro Scognamillo ◽  
Antonio Pio Catalano ◽  
Pierluigi Guerriero ◽  
Santolo Daliento ◽  
Lorenzo Codecasa ◽  
...  
Keyword(s):  
Fault Detection ◽  
Environmental Conditions ◽  
Ir Thermography

scholarly journals An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Barun Basnet ◽  
Hyunjun Chun ◽  
Junho Bang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Normal State ◽  
Environmental Conditions ◽  
Probabilistic Neural Network ◽  
Winter Season ◽  
Normal Fault ◽  
Pv System ◽  
Detection Model ◽  
Fault State

Effective fault diagnosis in a PV system requires understanding the behavior of the current/voltage (I/V) parameters in different environmental conditions. Especially during the winter season, I/V characters of certain faulty states in a PV system closely resemble that of a normal state. Therefore, a normal fault detection model can falsely predict a well-operating PV system as a faulty state and vice versa. In this paper, an intelligent fault diagnosis model is proposed for the fault detection and classification in PV systems. For the experimental verification, various fault state and normal state datasets are collected during the winter season under wide environmental conditions. The collected datasets are normalized and preprocessed using several data-mining techniques and then fed into a probabilistic neural network (PNN). The PNN model will be trained with the historical data to predict and classify faults when new data is fetched in it. The trained model showed better performance in prediction accuracy when compared with other classification methods in machine learning.

Vibration Isolator System for Electron Microscope

1990 ◽  
Vol 48 (1) ◽  
pp. 182-183
Author(s):  
K. Ohi ◽  
M. Mizuno ◽  
T. Kasai ◽  
Y. Ohkura ◽  
K. Mizuno ◽  
...  
Keyword(s):  
Electron Microscope ◽  
Magnetic Fields ◽  
Environmental Conditions ◽  
Vibration Isolator ◽  
Air Conditioners ◽  
Vibration Isolators ◽  
Large Size ◽  
Electron Microscopes

In recent years, with electron microscopes coming into wider use, their installation environments do not necessarily give their performance full play. Their environmental conditions include air-conditioners, magnetic fields, and vibrations. We report a jointly developed entirely new vibration isolator which is effective against the vibrations transmitted from the floor.Conventionally, large-sized vibration isolators which need the digging of a pit have been used. These vibration isolators, however, are large present problems of installation and maintenance because of their large-size.Thus, we intended to make a vibration isolator which1) eliminates the need for changing the installation room2) eliminates the need of maintenance and3) are compact in size and easily installable.

The influence of external conditions on sound pressure in the pistonphone at the infrasound frequency range

2020 ◽  
pp. 67-72
Author(s):  
A. V. Konkov ◽  
D. V. Golovin
Keyword(s):  
Environmental Conditions ◽  
Sound Pressure ◽  
Primary Method ◽  
Frequency Range ◽  
External Conditions ◽  
Numerical Estimations

The influence of environmental conditions on a sound pressure reproduced by the primary method in the measuring chambers of the Pistonphone in the frequency range from 1 mHz to 250 Hz is estimated. Numerical estimations of influence of environmental conditions on sound pressure in pistonphone measuring chambers are given and special requirements to system of maintenance of required external conditions are specified.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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