Fault Detection in WSNs - An Energy Efficiency Perspective Towards Human-Centric WSNs

2015 ◽  
pp. 285-300
Author(s):  
Charalampos Orfanidis ◽  
Yue Zhang ◽  
Nicola Dragoni
Keyword(s):  
Energy Efficiency ◽  
Fault Detection

scholarly journals Combining Performance Testing and Metadata Models to Support Fault Detection and Diagnostics in Smart Buildings

2019 ◽  
Vol 2 (3) ◽  
pp. 28
Author(s):  
Elena Markoska ◽  
Aslak Johansen ◽  
Mikkel Baun Kjærgaard ◽  
Sanja Lazarova-Molnar ◽  
Muhyiddine Jradi ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Fault Detection ◽  
Performance Test ◽  
Performance Testing ◽  
Performance Tests ◽  
Actual Performance ◽  
Hypothetical Scenario ◽  
Wide Range ◽  
Promising Practice

Performance testing of components and subsystems of buildings is a promising practice for increasing energy efficiency and closing gaps between intended and actual performance of buildings. A typical shortcoming of performance testing is the difficulty of linking a failing test to a faulty or underperforming component. Furthermore, a failing test can also be linked to a wrongly configured performance test. In this paper, we present Building Metadata Performance Testing (BuMPeT), a method that addresses this shortcoming by using building metadata models to extend performance testing with fault detection and diagnostics (FDD) capabilities. We present four different procedures that apply BuMPeT to different data sources and components. We have applied the proposed method to a case study building, located in Denmark, to test its capacity and benefits. Additionally, we use two real case scenarios to showcase examples of failing performance tests in the building, as well as discovery of causes of underperformance. Finally, to examine the limits to the benefits of the applied procedure, a detailed elaboration of a hypothetical scenario is presented. Our findings demonstrate that the method has potential and it can serve to increase the energy efficiency of a wide range of buildings.

scholarly journals Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 341
Author(s):  
Amir Rafati ◽  
Hamid Reza Shaker ◽  
Saman Ghahghahzadeh
Keyword(s):  
Energy Efficiency ◽  
Fault Detection ◽  
Research Area ◽  
Hvac Systems ◽  
Fault Detection And Diagnosis ◽  
Future Research ◽  
Efficiency Assessment ◽  
Load Monitoring ◽  
Detection And Diagnosis ◽  
Inefficient Operation

Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over the last few decades. NILM can play an important role in providing future energy efficiency feedback and developing fault detection and diagnosis (FDD) tools in smart buildings. Therefore, the review of NILM-based methods for FDD and the energy efficiency (EE) assessment of HVACs can be beneficial for users as well as buildings and facilities operators. To the best of the authors’ knowledge, this paper is the first review paper on the application of NILM techniques in these areas and highlights their effectiveness and limitations. This review shows that even though NILM could be successfully implemented for FDD and the EE evaluation of HVACs, and enhance the performance of these techniques, there are many research opportunities to improve or develop NILM-based FDD methods to deal with real-world challenges. These challenges and future research works are also discussed in-depth.

Application of Machine Learning for Fault Detection and Energy Efficiency Improvement in HVAC Application

2021 ◽  
pp. 263-278
Author(s):  
Umashankar Subramaniam ◽  
Sai Charan Bharadwaj ◽  
Nabanita Dutta ◽  
M. Venkateshkumar
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Fault Detection ◽  
Efficiency Improvement ◽  
Energy Efficiency Improvement

scholarly journals IoT based Fault Detection in Solar Panel using Arduino UNO with Wi-Fi Module ESP 8266

2019 ◽  
Vol 8 (4) ◽  
pp. 2630-2633 ◽  
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Fault Detection ◽  
Natural Resource ◽  
Solar Panel ◽  
Current Sensor ◽  
Power Demand ◽  
Solar Panels ◽  
Monitoring Centre ◽  
A Current

Increase in population increases the power demand. Solar is one of the natural resource used for generation of electricity. Solar panels are used for generation. If any scratch or small damage occurs in the panel then it will cause degradation in output. To improve the energy efficiency the damages should be detected. For deducting the damages various techniques had been used but they are time consuming process and are of high cost. This project proposes that whenever fault is occurred it is automatically detected with the help of internet of things. Each solar panel is connected to a current sensor if a fault occurs due to cracks on the surface of the panel then the current values in the damaged panel gets varied. This information is obtained with the help of current sensor. Then these values are sent to the monitoring centre through WI-FI module. By this the fault can be easily detected and automatically send to the engineers.

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