A Gray-Box Based Virtual SCFM Meter in Rooftop Air-Conditioning Units

2011 ◽  
Vol 3 (1) ◽  
Author(s):  
Daihong Yu ◽  
Haorong Li ◽  
Yuebin Yu
Keyword(s):  
Real Time ◽  
Air Temperature ◽  
First Principles ◽  
Air Conditioning ◽  
Low Cost ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Airflow Rate ◽  
Fault Diagnostics ◽  
True Value

Knowledge of supply airflow rate (SCFM) measurement in packaged rooftop air-conditioning units (RTUs) is vital for improving energy management and indoor air quality and facilitating real-time automated control and fault detection and diagnosis. Despite the importance of SCFM measurement in RTUs, the conventional SCFM metering devices are very vulnerable. The credibility of SCFM measurement would be compromised dramatically after a long-term use in adverse duct work surroundings. Moreover, application of conventional SCFM meters in RTUs is very costly in regard to procurement, installation, and periodic maintenance. A cost-effective and accurate nonconventional first principles based SCFM meter in RTUs was proposed previously to virtually monitor SCFM measurement. In order to overcome the deficiencies of the first principles based virtual SCFM meter in model implementation and fault diagnostics, experiments with a wider combination and coverage are investigated in this study. It is found that a gray-box based virtual SCFM meter can be obtained with available system information (outside air damper status) and low-cost temperature measurements (direct measurement of a manufacturer-installed supply air temperature sensor (SATmfr,meas) and outside air temperature). Further experiment evaluations demonstrate that the gray-box based virtual SCFM meter could predict the true value of SCFM very accurately (the uncertainty is ±5.9%) with significantly enhanced applicability in model implementation and capability in fault diagnostics. Additionally, the gray-box based virtual SCFM meter also inherits good characteristics of the first principles based virtual SCFM meter, such as high cost-effectiveness, good robustness against variations in multivariable operating conditions, and applicability to similar RTUs. This innovative virtual meter could serve as a permanent monitoring tool to indicate real-time SCFM measurement and/or to automatically detect and diagnose an improper quantity of SCFM for RTUs.

scholarly journals Real Time Monitoring and Neuro-Fuzzy Based Fault Diagnosis of Flow Process in Hybrid System

2012 ◽  
pp. 171-177
Author(s):  
Revathi. P ◽  
Pallikonda Rajasekaran. M ◽  
Babiyola. D ◽  
Aruna. R
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Real Time ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Economic Losses ◽  
Monitoring Systems ◽  
Back Propagation Algorithm ◽  
System Failures ◽  
Detection And Diagnosis

Process variables vary with time in certain applications. Monitoring systems let us avoid severe economic losses resulting from unexpected electric system failures by improving the system reliability and maintainability The installation and maintenance of such monitoring systems is easy when it is implemented using wireless techniques. ZigBee protocol, that is a wireless technology developed as open global standard to address the low-cost, low-power wireless sensor networks. The goal is to monitor the parameters and to classify the parameters in normal and abnormal conditions to detect fault in the process as early as possible by using artificial intelligent techniques. A key issue is to prevent local faults to be developed into system failures that may cause safety hazards, stop temporarily the production and possible detrimental environment impact. Several techniques are being investigated as an extension to the traditional fault detection and diagnosis. Computational intelligence techniques are being investigated as an extension to the traditional fault detection and diagnosis methods. This paper proposes ANFIS (Adaptive Neural Fuzzy Inference System) for fault detection and diagnosis. In ANFIS, the fuzzy logic will create the rules and membership functions whereas the neural network trains the membership function to get the best output. The output of ANFIS is compared with Back Propagation Algorithm (BPN) algorithm of neural network. The training and testing data required to develop the ANFIS model were generated at different operating conditions by running the process and by creating various faults in real time in a laboratory experimental model.

A Time-series Rank Ordering Control-system Data-driven Fault Detection Approach for HVAC Systems in Buildings

2021 ◽  
pp. 1-12
Author(s):  
James Shia ◽  
David M. Auslander
Keyword(s):  
Control System ◽  
Fault Detection ◽  
Air Conditioning ◽  
Low Cost ◽  
Hvac Systems ◽  
Fault Detection And Diagnosis ◽  
Machine Learning Algorithms ◽  
Total Energy Consumption ◽  
Model Free ◽  
System Data

Abstract About 41% of total energy consumption in the U.S. of year 2014 is used for heating and air conditioning, that is about 40 quadrillion (40×1015) British thermal units (BTU). Despite the fact that people have been working on on fault detection and diagnosis (FDD) for Heating, Ventilation, and Air Conditioning (HVAC) systems for a long time, very few publications have focused on scalability and low cost. In order to address this challenge, we will propose an approach which focuses on control-system data. Several machine learning algorithms are introduced for data exploration and analysis, a control-system data focused model free approach is presented as well, and finally, FDD is carried out by implementing anomaly algorithms. A simulation model is used to evaluate the performance of the various algorithms.

The myClinicalOutcomes website: providing real-time, patient-level PROMs data

2012 ◽  
Vol 94 (1) ◽  
pp. 20-21 ◽  
Author(s):  
Dan Williams
Keyword(s):  
Real Time ◽  
Current System ◽  
Low Cost ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Web Based ◽  
Patient Level ◽  
True Value ◽  
Patient Reported ◽  
National Patient

The national patient reported outcome measures (PROMs) programme has been under way since April 2009, yet the true value of capturing and utilising these metrics has yet to be realised. The current system needs to evolve and should deliver real-time, patient-level benefits for more procedures, bringing long-term monitoring into routine clinical practice. The myClinicalOutcomes website is a low-cost, straightforward web-based system that could improve the current situation.

Short Circuit between Turns in Stator Winding of Induction Machine Fault Detection and Diagnosis

2013 ◽  
Vol 416-417 ◽  
pp. 565-571 ◽  
Author(s):  
Youcef Soufi ◽  
Tahar Bahi ◽  
H. Merabet ◽  
S. Lekhchine
Keyword(s):  
Induction Motor ◽  
Low Cost ◽  
Short Circuit ◽  
Induction Machine ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Stator Winding ◽  
Detection And Diagnosis ◽  
Abnormal Operating Conditions ◽  
Short Circuit Fault

The induction motor is one of the most used electric machines in variable speed system in the different field of industry due to its robustness, mechanical strength and low cost. Despite these qualities, the induction machine is subjected during its operation to a number of constraints of various natures (electrical, mechanical and environmental). This paper focuses on the diagnosis and the detection of the short circuit fault between turns in the stator winding of an induction machine, based on analyzing the evolution of the stator current in each stator phase, using tools based both on motor current spectral analysis and Park vector approach. A study by simulation was presented. The obtained results show that the considered methods can effectively diagnose and detect abnormal operating conditions in induction motor applications. Therefore, they clearly show the possibility of extracting signatures and the application of these techniques offered reliable and satisfactory results for the diagnosis and detection of such fault.

Hybrid model for fault detection and diagnosis in an industrial distillation column

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Julia Picabea ◽  
Mauricio Maestri ◽  
Miryan Cassanello ◽  
Gabriel Horowitz
Keyword(s):  
Fault Detection ◽  
Hybrid Model ◽  
First Principles ◽  
Distillation Column ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Equivalent Model ◽  
Wide Range ◽  
Fault Detection And Identification ◽  
State Library

AbstractThe present work describes a method of automatic fault detection and identification based on a hybrid model (HM): First Principles – Neural Network. The FPM can simulate a wide range of situations while the NN corrects the model output using information from the historical data of the process. Operating conditions corresponding to different types of faults were simulated with the HM and saved with their description in a process state library. To detect a fault, the online measured data was compared with that corresponding to the operation under normal conditions. If a significant deviation was detected, the current state was compared with all the states stored in the process state library and it was identified as the one at the shortest distance. The method was tested with real data from a methanol-water industrial distillation column. During the studied period of operation of the plant, two faults were identified and reported. The proposed method was able to identify such failures more effectively than an equivalent model of first principles. The results obtained show that the proposed method has a great potential to be used in the automatic diagnosis of faults in refining and petrochemical processes.

The MAIANDROS System for Random-Vibration-Based On-Board Railway Vehicle and Track Monitoring

2021 ◽  
Author(s):  
Georgios Vlachospyros ◽  
Ilias A. Iliopoulos ◽  
Kiriakos Kritikakos ◽  
Nikolaos Kaliorakis ◽  
Spilios D. Fassois ◽  
...  
Keyword(s):  
Real Time ◽  
Random Vibration ◽  
Early Stage ◽  
Low Cost ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Railway Vehicle ◽  
Prototype System ◽  
Time Operation ◽  
Track Monitoring

Abstract A bird’s–eye overview of the innovative, on–board and Multi–Purpose, random vibration based MAIANDROS Condition Monitoring system for railway vehicles and infrastructure is presented. The system includes Modules for Suspension Monitoring (SM), Wheel Monitoring (WM), Track Monitoring (TM) for track segment condition characterization, Lateral Stability Monitoring (LSM), and Remaining Useful Life Estimation (RULE) for critical components such as wheels. It is based on Statistical Time Series type methods and proper decision making, and aims at overcoming various challenges of current systems while pushing their performance limits. Its unique advantages include high diagnostic performance, ability to detect early–stage (incipient) faults, robustness to varying Operating Conditions, early detection of the onset of hunting, operation with a minimal number of low–cost sensors, and minimal computational complexity for achieving real–time or almost real–time operation. Its high achievable performance is demonstrated via indicative assessments using a prototype system onboard an Athens Metro vehicle and Monte Carlo simulations with a SIMPACK based high–fidelity vehicle model.

scholarly journals Preliminary symptoms assessment of typical faults related to the fans and humidifiers of HVAC systems based on experimental data collected during Italian summer and winter

2021 ◽  
Vol 897 (1) ◽  
pp. 012009
Author(s):  
A Rosato ◽  
F Guarino ◽  
M El Youssef ◽  
S Sibilio ◽  
L Maffei
Keyword(s):  
Air Temperature ◽  
Indoor Air ◽  
Air Conditioning ◽  
Hvac Systems ◽  
Fault Detection And Diagnosis ◽  
Hvac System ◽  
Air Handling Unit ◽  
Air Volume ◽  
Detection And Diagnosis ◽  
Main Operating

Abstract The symptoms associated to the occurrence of typical faults in a heating, ventilation and air-conditioning (HVAC) system, including a single duct dual fan constant air volume air-handling unit, have been experimentally characterized. The operation of the HVAC unit with 3 artificially forced faults ((1) reduced velocity of the supply air fan, (2) reduced velocity of the return air fan, (3) the valve supplying the humidifier kept always closed) has been analysed and compared with that of healthy operation of the same plant under very similar boundary conditions (outside air temperature and initial indoor air temperature) during Italian summer and winter in order to preliminarily assess (i) the effects on the main operating parameters, and (ii) generate preliminary operation data to assist further research in fault detection and diagnosis of HVAC systems.

scholarly journals Detection of Anomalous Noise Events for Real-Time Road-Traffic Noise Mapping: The Dynamap’s project case study

Noise Mapping ◽  
2018 ◽  
Vol 5 (1) ◽  
pp. 71-85 ◽  
Author(s):  
Francesc Alías ◽  
Rosa Ma Alsina-Pagès ◽  
Ferran Orga ◽  
Joan Claudi Socoró
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Low Cost ◽  
Traffic Noise ◽  
Real Life ◽  
Noise Pollution ◽  
Environmental Noise ◽  
Operating Conditions ◽  
Road Traffic Noise ◽  
Noise Mapping

Abstract Environmental noise is increasing year after year, especially in urban and suburban areas. Besides annoyance, environmental noise also causes harmful health effects on people. The Environmental Noise Directive 2002/49/EC (END) is the main instrument of the European Union to identify and combat noise pollution, followed by the CNOSSOS-EU methodological framework. In compliance with the END legislation, the European Member States are required to publish noise maps and action plans every five years. The emergence of Wireless Acoustic Sensor Networks (WASNs) have changed the paradigm to address the END regulatory requirements, allowing the dynamic ubiquitous measurement of environmental noise pollution. Following the END, the LIFE DYNAMAP project aims to develop a WASN-based low-cost noise mapping system to monitor the acoustic impact of road infrastructures in real time. Those acoustic events unrelated to regular traffic noise should be removed from the equivalent noise level calculations to avoid biasing the noise map generation. This work describes the different approaches developed within the DYNAMAP project to implement an Anomalous Noise Event Detector on the low-cost sensors of the network, considering both synthetic and real-life acoustic data.Moreover, the paper reflects on several open challenges, discussing how to tackle them for the future deployment of WASN-based noise monitoring systems in real-life operating conditions.

Phase Change Material to Reduce Cooling Load of Buildings in Hot Climate

2019 ◽  
Vol 801 ◽  
pp. 416-423
Author(s):  
Mahmoud Haggag ◽  
Ahmed Hassan ◽  
Shaimaa Abdelbaqi
Keyword(s):  
Phase Change ◽  
Phase Change Material ◽  
Air Temperature ◽  
Air Conditioning ◽  
Cooling Effect ◽  
Airflow Rate ◽  
Duct System ◽  
Air Velocity ◽  
Hot Climate ◽  
Change Material

Due to the hot climate of the United Arab Emirates (UAE), where the external ambient temperature may reach 50°C in the summer season, almost 75% of the total energy is consumed in air-conditioning (AC). A significant improvement in the AC systems performance during hot summer time aligned with energy conservation could be achieved by pre-cooling of the air entering the condensers. Inclusion of Phase Change Material (PCM) as thermal energy storage (TES) have been widely used as one of the environmentally friendly energy saving materials due to its high energy density. The absorption/releasing of heat by PCM during its phase change, provides a latent heating/cooling for the surrounding. Numerous systems have implemented PCM based-TES for cooling purposes, such as thermally activated building systems (TABS), suspended ceilings, external facades or in the ventilation system. This study examines PCM based air pre-cooling concept and evaluates its performance in extremely hot climate of UAE. The drop in the outlet air temperature of the duct system quantifies the cooling effect. A paraffin based PCM with melting range of 30–33°C integrated in containers placed in the test chamber mimic the air conditioning duct system, and its cooling effect is monitored. A Conjugate heat transfer model employing enthalpy-based formulation is developed to predict the optimized PCM container size and optimum airflow rate validated with experimental data. Single and series columns of PCM containers subjected to different levels of supplied air velocity at range of 1 m/s - 4m/s are evaluated. Employing series of PCM enclosures at low air velocity of 1m/s enhanced the pre-cooling performance and reduced the outlet air temperature to 35°C yielding a temperature drop up to 11°C.

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