Component-level Modeling and Simulation of Cable Fault Detection System

The paper mainly aimed at solving the problem of yarn color fault detection. Yarn with different color is hard to detect in yarn production, a special photoelectric sensor is designed in this paper. First, this paper analyzed the requirement of light source and photoelectric receiver in the photoelectric sensor, and designs the light path and driver circuit. Then this paper analyzed the amplifier circuit and noise in the photoelectric sensor, with an amplifier circuit of minimal noise proposed at last. Finally, this paper tested the yarn color fault detection system with virtual instrument, and the test results showed a great application prospect of the photoelectric sensor. Photoelectric yarn clearer was the first type of electronic yarn clearer, but due to the under development of the optical technology and measurement technology, the photoelectric yarn cleaner can't meet the requirements of textile production, gradually replaced by capacitive yarn cleaner. Though photoelectric yarn cleaner had a good visual conformity degree, it’s still a unreplaceable method in colored yarn faults

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A Low-Cost Monitoring and Fault Detection System for Stand-Alone Photovoltaic Systems Using IoT Technique

Lecture Notes in Electrical Engineering - ELECTRIMACS 2019 ◽

10.1007/978-3-030-37161-6_26 ◽

2020 ◽

pp. 349-358 ◽

Cited By ~ 3

Author(s):

Adel Mellit ◽

Amor Hamied ◽

Vanni Lughi ◽

Alessandro Massi Pavan

Keyword(s):

Fault Detection ◽

Detection System ◽

Low Cost ◽

Photovoltaic Systems

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Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy

Annual Conference of the PHM Society ◽

10.36001/phmconf.2018.v10i1.517 ◽

2018 ◽

Vol 10 (1) ◽

Author(s):

Yimin Chen ◽

Jin Wen

Keyword(s):

Fault Detection ◽

Principal Component ◽

Field Evaluation ◽

Fault Detection And Diagnosis ◽

Data Driven ◽

Machine Learning Techniques ◽

Component Level ◽

Automation Systems ◽

Tightly Coupled ◽

Detection And Diagnosis

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.

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