Fault Light Detection and Identification System using RFID

2018 ◽  
Author(s):  
Muawya Naser ◽  
Fadi Abuamara
Keyword(s):  
Identification System ◽  
Light Detection ◽  
Detection And Identification

A Hyperspectral Library of Foliar Diseases of Wheat

2021 ◽  
Author(s):  
David Bohnenkamp ◽  
Jan Behmann ◽  
Stefan Paulus ◽  
Ulrike Steiner ◽  
Anne-Katrin Mahlein
Keyword(s):  
Developmental Stages ◽  
Leaf Tissue ◽  
Plant Diseases ◽  
Identification System ◽  
Spectral Library ◽  
Foliar Diseases ◽  
Robust Detection ◽  
Analysis Methods ◽  
Detection And Identification ◽  
Biotrophic Fungi

This work established a hyperspectral library of important foliar diseases of wheat in time series to detect spectral changes from infection to symptom appearance induced by different pathogens. The data was generated under controlled conditions at the leaf-scale. The transition from healthy to diseased leaf tissue was assessed, spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that are indicative of a certain developmental stage during pathogenesis - defined as turning points - were combined into a spectral library. Different machine learning analysis methods were applied and compared to test the potential of this library for the detection and quantification of foliar diseases in hyperspectral images. All evaluated classifiers provided a high accuracy for the detection and identification for both the biotrophic fungi and the necrotrophic fungi of up to 99%. The potential of applying spectral analysis methods, in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques of plant diseases under field conditions.

scholarly journals Detection and Identification of Microbial Volatile Organic Compounds of the Green Mold Disease

2020 ◽  
Vol 11 (2) ◽  
pp. 14-28
Author(s):  
Dalma Radványi ◽  
András Geösel ◽  
Zsuzsa Jókai ◽  
Péter Fodor ◽  
Attila Gere
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Solid Phase ◽  
Gas Chromatography Mass Spectrometry ◽  
Identification System ◽  
Mushroom Species ◽  
Detection And Identification ◽  
Volatile Organic ◽  
Microbial Volatile Organic Compounds ◽  
Green Mould

Button mushrooms are one of the most commonly cultivated mushroom species facing different risks e.g.: viral, bacterial and fungal diseases. One of the most common problems is caused by Trichoderma aggressivum, or ‘green mould' disease. The presence or absence of mushroom disease-related moulds can sufficiently be detected from the air by headspace solid-phase microextraction coupled gas chromatography-mass spectrometry (HS SPME GC-MS) via their emitted microbial volatile organic compounds (MVOCs). In the present study, HS SPME GC-MS was used to explore the volatile secondary metabolites released by T. aggressivum f. europaeum on different nutrient-rich and -poor media. The MVOC pattern of green mould was determined, then media-dependent and independent biomarkers were also identified during metabolomic experiments. The presented results provide the basics of a green mould identification system which helps producers reducing yield loss, new directions for researchers in mapping the metabolomic pathways of T. aggressivum and new tools for policy makers in mushroom quality control.

Performance Comparison of VSI Switches Faults Analysis Using STFT and S Transform

2015 ◽  
Vol 785 ◽  
pp. 210-214 ◽  
Author(s):  
M. Manap ◽  
A.R. Abdullah ◽  
N.Z. Saharuddin ◽  
N.A. Abidullah ◽  
Nur Sumayyah Ahmad ◽  
...  
Keyword(s):  
Power Converter ◽  
Performance Comparison ◽  
Voltage Source ◽  
Percentage Error ◽  
Identification System ◽  
Time Frequency ◽  
Signal Parameters ◽  
Detection And Identification ◽  
Frequency Representation ◽  
S Transform

Switches fault in power converter has become compelling issues over the years. To reduce cost and maintenance downtime, a good fault detection technique is an essential. In this paper, the performance of STFT and S transform techniques are analysed and compared for voltage source inverter (VSI) switches faults. The signal from phase current is represented in jointly time-frequency representation (TFR) to estimate signal parameters and characteristics. Then, the degree of accuracy for both STFT and S transform are determined by the lowest value of mean absolute percentage error (MAPE). The results demonstrate that S transform gives better accuracy compare to STFT and is suitable for VSI switches faults detection and identification system.

Robust Model Reference Fault Detection and Identification System for Fixed Wing Aircrafts

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
R. Jaganraj ◽  
R. Velu
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Fault Estimation ◽  
Identification System ◽  
Model Reference ◽  
Robust Model ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Time Parameters ◽  
Implantable Sensor

Fault Detection and Identification system (FDI) and Fault Tolerant Flight Control (FTFC) system are used to correct the faulty operation of an aircraft. Both FDIs and FTFCs have operational disadvantages due to their inherent limitation of fault source identification. This paper presents the design and implementation of a robust model reference fault detection and identification (MRFDI) system on a fixed-wing aircraft for identifying actuator fault, instrument fault and presence of any uncertainties. The proposed MRDFI fuses the real-time parameters and actuator feedback to combine the advantages of data driven and model reference FDI that makes robust fault estimation. The MRFDI system is implemented on a typical aircraft altitude hold autopilot simulation environment with a predefined fault scenario. The fault scenario includes a faulty elevator, a faulty skin-implantable sensor and wind gust as environmental uncertainty. The MRFDI performs logical analysis to detect fault using state-dependent real-time parameters and state-independent skin implantable sensor. This two-step fault detection method makes MRFDI robust to any type of fault identification. The results show that the MRFDI detects and distinguishes faults in actuator, instrument and any of the listed uncertainties thrown by the environment accurately.

Comparison of Open and Short-Circuit Switches Faults Voltage Source Inverter (VSI) Analysis Using Time-Frequency Distributions

2015 ◽  
Vol 752-753 ◽  
pp. 1164-1169 ◽  
Author(s):  
M. Manap ◽  
Nur Sumayyah Ahmad ◽  
Abdul Rahim Abdullah ◽  
Norhazilina Bahari
Keyword(s):  
Short Circuit ◽  
Harmonic Distortion ◽  
Automated System ◽  
Voltage Source ◽  
Identification System ◽  
Voltage Source Inverter ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Detection Analysis ◽  
Detection And Identification

Voltage source inverter (VSI) plays an important roles in electrical drive systems. Consistently, expose to hash environmental condition, the lifespan of the electronic component such as insulated-gate bipolar transistor (IGBT) may shorten and many faults related to the inverter especially switches can be occur. The present of VSI switches faults causing equipment failure and increased the cost of manufacturing process. Therefore, faults detection analysis is mandatory to identify the VSI switches faults. This paper presents the analysis of VSI switches faults using time-frequency distributions (TFDs) which are short times Fourier transform (STFT) and spectrogram. From time-frequency representation (TFR) obtained by using the TFDs, parameters of the faults signal are estimated such as instantaneous of average, root mean square (RMS), fundamental, Total Waveform Distortion (TWD), Total Harmonics Distortion (THD) and Total non-Harmonic Distortion (TnHD) of current signals. Then, based on the characteristics of the faults calculated from the signal parameters, VSI switches faults can be detected and identified. The performance of TFD for the faults analysis is also demonstrated to obtain the best TFD for switches faults detection and identification system. The results show that, STFT is the best technique to classify and identify VSI switches faults and can be implemented for automated system.

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